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system-design-primer | english readme md readme ja md readme zh hans md readme zh tw md https github com donnemartin system design primer issues 170 https github com donnemartin system design primer issues 220 portugu s do brasil https github com donnemartin system design primer issues 40 deutsch https github com donnemartin system design primer issues 186 https github com donnemartin system design primer issues 130 https github com donnemartin system design primer issues 272 italiano https github com donnemartin system design primer issues 104 https github com donnemartin system design primer issues 102 https github com donnemartin system design primer issues 110 polski https github com donnemartin system design primer issues 68 https github com donnemartin system design primer issues 87 espa ol https github com donnemartin system design primer issues 136 https github com donnemartin system design primer issues 187 t rk e https github com donnemartin system design primer issues 39 ti ng vi t https github com donnemartin system design primer issues 127 fran ais https github com donnemartin system design primer issues 250 add translation https github com donnemartin system design primer issues 28 help translate translations md this guide the system design primer p align center img src images jj3a5n8 png br p motivation learn how to design large scale systems prep for the system design interview learn how to design large scale systems learning how to design scalable systems will help you become a better engineer system design is a broad topic there is a vast amount of resources scattered throughout the web on system design principles this repo is an organized collection of resources to help you learn how to build systems at scale learn from the open source community this is a continually updated open source project contributions contributing are welcome prep for the system design interview in addition to coding interviews system design is a required component of the technical interview process at many tech companies practice common system design interview questions and compare your results with sample solutions discussions code and diagrams additional topics for interview prep study guide study guide how to approach a system design interview question how to approach a system design interview question system design interview questions with solutions system design interview questions with solutions object oriented design interview questions with solutions object oriented design interview questions with solutions additional system design interview questions additional system design interview questions anki flashcards p align center img src images zdcakb3 png br p the provided anki flashcard decks https apps ankiweb net use spaced repetition to help you retain key system design concepts system design deck https github com donnemartin system design primer tree master resources flash cards system 20design apkg system design exercises deck https github com donnemartin system design primer tree master resources flash cards system 20design 20exercises apkg object oriented design exercises deck https github com donnemartin system design primer tree master resources flash cards oo 20design apkg great for use while on the go coding resource interactive coding challenges looking for resources to help you prep for the coding interview https github com donnemartin interactive coding challenges p align center img src images b4ytaen png br p check out the sister repo interactive coding challenges https github com donnemartin interactive coding challenges which contains an additional anki deck coding deck https github com donnemartin interactive coding challenges tree master anki cards coding apkg contributing learn from the community feel free to submit pull requests to help fix errors improve sections add new sections translate https github com donnemartin system design primer issues 28 content that needs some polishing is placed under development under development review the contributing guidelines contributing md index of system design topics summaries of various system design topics including pros and cons everything is a trade off each section contains links to more in depth resources p align center img src images jrubaf7 png br p system design topics start here system design topics start here step 1 review the scalability video lecture step 1 review the scalability video lecture step 2 review the scalability article step 2 review the scalability article next steps next steps performance vs scalability performance vs scalability latency vs throughput latency vs throughput availability vs consistency availability vs consistency cap theorem cap theorem cp consistency and partition tolerance cp consistency and partition tolerance ap availability and partition tolerance ap availability and partition tolerance consistency patterns consistency patterns weak consistency weak consistency eventual consistency eventual consistency strong consistency strong consistency availability patterns availability patterns fail over fail over replication replication availability in numbers availability in numbers domain name system domain name system content delivery network content delivery network push cdns push cdns pull cdns pull cdns load balancer load balancer active passive active passive active active active active layer 4 load balancing layer 4 load balancing layer 7 load balancing layer 7 load balancing horizontal scaling horizontal scaling reverse proxy web server reverse proxy web server load balancer vs reverse proxy load balancer vs reverse proxy application layer application layer microservices microservices service discovery service discovery database database relational database management system rdbms relational database management system rdbms master slave replication master slave replication master master replication master master replication federation federation sharding sharding denormalization denormalization sql tuning sql tuning nosql nosql key value store key value store document store document store wide column store wide column store graph database graph database sql or nosql sql or nosql cache cache client caching client caching cdn caching cdn caching web server caching web server caching database caching database caching application caching application caching caching at the database query level caching at the database query level caching at the object level caching at the object level when to update the cache when to update the cache cache aside cache aside write through write through write behind write back write behind write back refresh ahead refresh ahead asynchronism asynchronism message queues message queues task queues task queues back pressure back pressure communication communication transmission control protocol tcp transmission control protocol tcp user datagram protocol udp user datagram protocol udp remote procedure call rpc remote procedure call rpc representational state transfer rest representational state transfer rest security security appendix appendix powers of two table powers of two table latency numbers every programmer should know latency numbers every programmer should know additional system design interview questions additional system design interview questions real world architectures real world architectures company architectures company architectures company engineering blogs company engineering blogs under development under development credits credits contact info contact info license license study guide suggested topics to review based on your interview timeline short medium long imgur images ofvllex png q for interviews do i need to know everything here a no you don t need to know everything here to prepare for the interview what you are asked in an interview depends on variables such as how much experience you have what your technical background is what positions you are interviewing for which companies you are interviewing with luck more experienced candidates are generally expected to know more about system design architects or team leads might be expected to know more than individual contributors top tech companies are likely to have one or more design interview rounds start broad and go deeper in a few areas it helps to know a little about various key system design topics adjust the following guide based on your timeline experience what positions you are interviewing for and which companies you are interviewing with short timeline aim for breadth with system design topics practice by solving some interview questions medium timeline aim for breadth and some depth with system design topics practice by solving many interview questions long timeline aim for breadth and more depth with system design topics practice by solving most interview questions short medium long read through the system design topics index of system design topics to get a broad understanding of how systems work 1 1 1 read through a few articles in the company engineering blogs company engineering blogs for the companies you are interviewing with 1 1 1 read through a few real world architectures real world architectures 1 1 1 review how to approach a system design interview question how to approach a system design interview question 1 1 1 work through system design interview questions with solutions system design interview questions with solutions some many most work through object oriented design interview questions with solutions object oriented design interview questions with solutions some many most review additional system design interview questions additional system design interview questions some many most how to approach a system design interview question how to tackle a system design interview question the system design interview is an open ended conversation you are expected to lead it you can use the following steps to guide the discussion to help solidify this process work through the system design interview questions with solutions system design interview questions with solutions section using the following steps step 1 outline use cases constraints and assumptions gather requirements and scope the problem ask questions to clarify use cases and constraints discuss assumptions who is going to use it how are they going to use it how many users are there what does the system do what are the inputs and outputs of the system how much data do we expect to handle how many requests per second do we expect what is the expected read to write ratio step 2 create a high level design outline a high level design with all important components sketch the main components and connections justify your ideas step 3 design core components dive into details for each core component for example if you were asked to design a url shortening service solutions system design pastebin readme md discuss generating and storing a hash of the full url md5 solutions system design pastebin readme md and base62 solutions system design pastebin readme md hash collisions sql or nosql database schema translating a hashed url to the full url database lookup api and object oriented design step 4 scale the design identify and address bottlenecks given the constraints for example do you need the following to address scalability issues load balancer horizontal scaling caching database sharding discuss potential solutions and trade offs everything is a trade off address bottlenecks using principles of scalable system design index of system design topics back of the envelope calculations you might be asked to do some estimates by hand refer to the appendix appendix for the following resources use back of the envelope calculations http highscalability com blog 2011 1 26 google pro tip use back of the envelope calculations to choo html powers of two table powers of two table latency numbers every programmer should know latency numbers every programmer should know source s and further reading check out the following links to get a better idea of what to expect how to ace a systems design interview https www palantir com 2011 10 how to rock a systems design interview the system design interview http www hiredintech com system design intro to architecture and systems design interviews https www youtube com watch v zgds0eumn70 system design template https leetcode com discuss career 229177 my system design template system design interview questions with solutions common system design interview questions with sample discussions code and diagrams solutions linked to content in the solutions folder question design pastebin com or bit ly solution solutions system design pastebin readme md design the twitter timeline and search or facebook feed and search solution solutions system design twitter readme md design a web crawler solution solutions system design web crawler readme md design mint com solution solutions system design mint readme md design the data structures for a social network solution solutions system design social graph readme md design a key value store for a search engine solution solutions system design query cache readme md design amazon s sales ranking by category feature solution solutions system design sales rank readme md design a system that scales to millions of users on aws solution solutions system design scaling aws readme md add a system design question contribute contributing design pastebin com or bit ly view exercise and solution solutions system design pastebin readme md imgur images 4edxg0t png design the twitter timeline and search or facebook feed and search view exercise and solution solutions system design twitter readme md imgur images jrubaf7 png design a web crawler view exercise and solution solutions system design web crawler readme md imgur images bwxptqa png design mint com view exercise and solution solutions system design mint readme md imgur images v5q57vu png design the data structures for a social network view exercise and solution solutions system design social graph readme md imgur images cdcv5g7 png design a key value store for a search engine view exercise and solution solutions system design query cache readme md imgur images 4j99mhe png design amazon s sales ranking by category feature view exercise and solution solutions system design sales rank readme md imgur images mzexp06 png design a system that scales to millions of users on aws view exercise and solution solutions system design scaling aws readme md imgur images jj3a5n8 png object oriented design interview questions with solutions common object oriented design interview questions with sample discussions code and diagrams solutions linked to content in the solutions folder note this section is under development question design a hash map solution solutions object oriented design hash table hash map ipynb design a least recently used cache solution solutions object oriented design lru cache lru cache ipynb design a call center solution solutions object oriented design call center call center ipynb design a deck of cards solution solutions object oriented design deck of cards deck of cards ipynb design a parking lot solution solutions object oriented design parking lot parking lot ipynb design a chat server solution solutions object oriented design online chat online chat ipynb design a circular array contribute contributing add an object oriented design question contribute contributing system design topics start here new to system design first you ll need a basic understanding of common principles learning about what they are how they are used and their pros and cons step 1 review the scalability video lecture scalability lecture at harvard https www youtube com watch v w9f d3oy4 topics covered vertical scaling horizontal scaling caching load balancing database replication database partitioning step 2 review the scalability article scalability https web archive org web 20221030091841 http www lecloud net tagged scalability chrono topics covered clones https web archive org web 20220530193911 https www lecloud net post 7295452622 scalability for dummies part 1 clones databases https web archive org web 20220602114024 https www lecloud net post 7994751381 scalability for dummies part 2 database caches https web archive org web 20230126233752 https www lecloud net post 9246290032 scalability for dummies part 3 cache asynchronism https web archive org web 20220926171507 https www lecloud net post 9699762917 scalability for dummies part 4 asynchronism next steps next we ll look at high level trade offs performance vs scalability latency vs throughput availability vs consistency keep in mind that everything is a trade off then we ll dive into more specific topics such as dns cdns and load balancers performance vs scalability a service is scalable if it results in increased performance in a manner proportional to resources added generally increasing performance means serving more units of work but it can also be to handle larger units of work such as when datasets grow sup a href http www allthingsdistributed com 2006 03 a word on scalability html 1 a sup another way to look at performance vs scalability if you have a performance problem your system is slow for a single user if you have a scalability problem your system is fast for a single user but slow under heavy load source s and further reading a word on scalability http www allthingsdistributed com 2006 03 a word on scalability html scalability availability stability patterns http www slideshare net jboner scalability availability stability patterns latency vs throughput latency is the time to perform some action or to produce some result throughput is the number of such actions or results per unit of time generally you should aim for maximal throughput with acceptable latency source s and further reading understanding latency vs throughput https community cadence com cadence blogs 8 b fv posts understanding latency vs throughput availability vs consistency cap theorem p align center img src images bglmi2u png br i a href http robertgreiner com 2014 08 cap theorem revisited source cap theorem revisited a i p in a distributed computer system you can only support two of the following guarantees consistency every read receives the most recent write or an error availability every request receives a response without guarantee that it contains the most recent version of the information partition tolerance the system continues to operate despite arbitrary partitioning due to network failures networks aren t reliable so you ll need to support partition tolerance you ll need to make a software tradeoff between consistency and availability cp consistency and partition tolerance waiting for a response from the partitioned node might result in a timeout error cp is a good choice if your business needs require atomic reads and writes ap availability and partition tolerance responses return the most readily available version of the data available on any node which might not be the latest writes might take some time to propagate when the partition is resolved ap is a good choice if the business needs to allow for eventual consistency eventual consistency or when the system needs to continue working despite external errors source s and further reading cap theorem revisited http robertgreiner com 2014 08 cap theorem revisited a plain english introduction to cap theorem http ksat me a plain english introduction to cap theorem cap faq https github com henryr cap faq the cap theorem https www youtube com watch v k yaq8ahlfa consistency patterns with multiple copies of the same data we are faced with options on how to synchronize them so clients have a consistent view of the data recall the definition of consistency from the cap theorem cap theorem every read receives the most recent write or an error weak consistency after a write reads may or may not see it a best effort approach is taken this approach is seen in systems such as memcached weak consistency works well in real time use cases such as voip video chat and realtime multiplayer games for example if you are on a phone call and lose reception for a few seconds when you regain connection you do not hear what was spoken during connection loss eventual consistency after a write reads will eventually see it typically within milliseconds data is replicated asynchronously this approach is seen in systems such as dns and email eventual consistency works well in highly available systems strong consistency after a write reads will see it data is replicated synchronously this approach is seen in file systems and rdbmses strong consistency works well in systems that need transactions source s and further reading transactions across data centers http snarfed org transactions across datacenters io html availability patterns there are two complementary patterns to support high availability fail over and replication fail over active passive with active passive fail over heartbeats are sent between the active and the passive server on standby if the heartbeat is interrupted the passive server takes over the active s ip address and resumes service the length of downtime is determined by whether the passive server is already running in hot standby or whether it needs to start up from cold standby only the active server handles traffic active passive failover can also be referred to as master slave failover active active in active active both servers are managing traffic spreading the load between them if the servers are public facing the dns would need to know about the public ips of both servers if the servers are internal facing application logic would need to know about both servers active active failover can also be referred to as master master failover disadvantage s failover fail over adds more hardware and additional complexity there is a potential for loss of data if the active system fails before any newly written data can be replicated to the passive replication master slave and master master this topic is further discussed in the database database section master slave replication master slave replication master master replication master master replication availability in numbers availability is often quantified by uptime or downtime as a percentage of time the service is available availability is generally measured in number of 9s a service with 99 99 availability is described as having four 9s 99 9 availability three 9s duration acceptable downtime downtime per year 8h 45min 57s downtime per month 43m 49 7s downtime per week 10m 4 8s downtime per day 1m 26 4s 99 99 availability four 9s duration acceptable downtime downtime per year 52min 35 7s downtime per month 4m 23s downtime per week 1m 5s downtime per day 8 6s availability in parallel vs in sequence if a service consists of multiple components prone to failure the service s overall availability depends on whether the components are in sequence or in parallel in sequence overall availability decreases when two components with availability 100 are in sequence availability total availability foo availability bar if both foo and bar each had 99 9 availability their total availability in sequence would be 99 8 in parallel overall availability increases when two components with availability 100 are in parallel availability total 1 1 availability foo 1 availability bar if both foo and bar each had 99 9 availability their total availability in parallel would be 99 9999 domain name system p align center img src images ioylj4i jpg br i a href http www slideshare net srikrupa5 dns security presentation issa source dns security presentation a i p a domain name system dns translates a domain name such as www example com to an ip address dns is hierarchical with a few authoritative servers at the top level your router or isp provides information about which dns server s to contact when doing a lookup lower level dns servers cache mappings which could become stale due to dns propagation delays dns results can also be cached by your browser or os for a certain period of time determined by the time to live ttl https en wikipedia org wiki time to live ns record name server specifies the dns servers for your domain subdomain mx record mail exchange specifies the mail servers for accepting messages a record address points a name to an ip address cname canonical points a name to another name or cname example com to www example com or to an a record services such as cloudflare https www cloudflare com dns and route 53 https aws amazon com route53 provide managed dns services some dns services can route traffic through various methods weighted round robin https www jscape com blog load balancing algorithms prevent traffic from going to servers under maintenance balance between varying cluster sizes a b testing latency based https docs aws amazon com route53 latest developerguide routing policy html routing policy latency geolocation based https docs aws amazon com route53 latest developerguide routing policy html routing policy geo disadvantage s dns accessing a dns server introduces a slight delay although mitigated by caching described above dns server management could be complex and is generally managed by governments isps and large companies http superuser com questions 472695 who controls the dns servers 472729 dns services have recently come under ddos attack http dyn com blog dyn analysis summary of friday october 21 attack preventing users from accessing websites such as twitter without knowing twitter s ip address es source s and further reading dns architecture https technet microsoft com en us library dd197427 v ws 10 aspx wikipedia https en wikipedia org wiki domain name system dns articles https support dnsimple com categories dns content delivery network p align center img src images h9taugi jpg br i a href https www creative artworks eu why use a content delivery network cdn source why use a cdn a i p a content delivery network cdn is a globally distributed network of proxy servers serving content from locations closer to the user generally static files such as html css js photos and videos are served from cdn although some cdns such as amazon s cloudfront support dynamic content the site s dns resolution will tell clients which server to contact serving content from cdns can significantly improve performance in two ways users receive content from data centers close to them your servers do not have to serve requests that the cdn fulfills push cdns push cdns receive new content whenever changes occur on your server you take full responsibility for providing content uploading directly to the cdn and rewriting urls to point to the cdn you can configure when content expires and when it is updated content is uploaded only when it is new or changed minimizing traffic but maximizing storage sites with a small amount of traffic or sites with content that isn t often updated work well with push cdns content is placed on the cdns once instead of being re pulled at regular intervals pull cdns pull cdns grab new content from your server when the first user requests the content you leave the content on your server and rewrite urls to point to the cdn this results in a slower request until the content is cached on the cdn a time to live ttl https en wikipedia org wiki time to live determines how long content is cached pull cdns minimize storage space on the cdn but can create redundant traffic if files expire and are pulled before they have actually changed sites with heavy traffic work well with pull cdns as traffic is spread out more evenly with only recently requested content remaining on the cdn disadvantage s cdn cdn costs could be significant depending on traffic although this should be weighed with additional costs you would incur not using a cdn content might be stale if it is updated before the ttl expires it cdns require changing urls for static content to point to the cdn source s and further reading globally distributed content delivery https figshare com articles globally distributed content delivery 6605972 the differences between push and pull cdns http www travelblogadvice com technical the differences between push and pull cdns wikipedia https en wikipedia org wiki content delivery network load balancer p align center img src images h81n9ik png br i a href http horicky blogspot com 2010 10 scalable system design patterns html source scalable system design patterns a i p load balancers distribute incoming client requests to computing resources such as application servers and databases in each case the load balancer returns the response from the computing resource to the appropriate client load balancers are effective at preventing requests from going to unhealthy servers preventing overloading resources helping to eliminate a single point of failure load balancers can be implemented with hardware expensive or with software such as haproxy additional benefits include ssl termination decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations removes the need to install x 509 certificates https en wikipedia org wiki x 509 on each server session persistence issue cookies and route a specific client s requests to same instance if the web apps do not keep track of sessions to protect against failures it s common to set up multiple load balancers either in active passive active passive or active active active active mode load balancers can route traffic based on various metrics including random least loaded session cookies round robin or weighted round robin https www g33kinfo com info round robin vs weighted round robin lb layer 4 layer 4 load balancing layer 7 layer 7 load balancing layer 4 load balancing layer 4 load balancers look at info at the transport layer communication to decide how to distribute requests generally this involves the source destination ip addresses and ports in the header but not the contents of the packet layer 4 load balancers forward network packets to and from the upstream server performing network address translation nat https www nginx com resources glossary layer 4 load balancing layer 7 load balancing layer 7 load balancers look at the application layer communication to decide how to distribute requests this can involve contents of the header message and cookies layer 7 load balancers terminate network traffic reads the message makes a load balancing decision then opens a connection to the selected server for example a layer 7 load balancer can direct video traffic to servers that host videos while directing more sensitive user billing traffic to security hardened servers at the cost of flexibility layer 4 load balancing requires less time and computing resources than layer 7 although the performance impact can be minimal on modern commodity hardware horizontal scaling load balancers can also help with horizontal scaling improving performance and availability scaling out using commodity machines is more cost efficient and results in higher availability than scaling up a single server on more expensive hardware called vertical scaling it is also easier to hire for talent working on commodity hardware than it is for specialized enterprise systems disadvantage s horizontal scaling scaling horizontally introduces complexity and involves cloning servers servers should be stateless they should not contain any user related data like sessions or profile pictures sessions can be stored in a centralized data store such as a database database sql nosql or a persistent cache cache redis memcached downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out disadvantage s load balancer the load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly introducing a load balancer to help eliminate a single point of failure results in increased complexity a single load balancer is a single point of failure configuring multiple load balancers further increases complexity source s and further reading nginx architecture https www nginx com blog inside nginx how we designed for performance scale haproxy architecture guide http www haproxy org download 1 2 doc architecture txt scalability http www lecloud net post 7295452622 scalability for dummies part 1 clones wikipedia https en wikipedia org wiki load balancing computing layer 4 load balancing https www nginx com resources glossary layer 4 load balancing layer 7 load balancing https www nginx com resources glossary layer 7 load balancing elb listener config http docs aws amazon com elasticloadbalancing latest classic elb listener config html reverse proxy web server p align center img src images n41azff png br i a href https upload wikimedia org wikipedia commons 6 67 reverse proxy h2g2bob svg source wikipedia a i br p a reverse proxy is a web server that centralizes internal services and provides unified interfaces to the public requests from clients are forwarded to a server that can fulfill it before the reverse proxy returns the server s response to the client additional benefits include increased security hide information about backend servers blacklist ips limit number of connections per client increased scalability and flexibility clients only see the reverse proxy s ip allowing you to scale servers or change their configuration ssl termination decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations removes the need to install x 509 certificates https en wikipedia org wiki x 509 on each server compression compress server responses caching return the response for cached requests static content serve static content directly html css js photos videos etc load balancer vs reverse proxy deploying a load balancer is useful when you have multiple servers often load balancers route traffic to a set of servers serving the same function reverse proxies can be useful even with just one web server or application server opening up the benefits described in the previous section solutions such as nginx and haproxy can support both layer 7 reverse proxying and load balancing disadvantage s reverse proxy introducing a reverse proxy results in increased complexity a single reverse proxy is a single point of failure configuring multiple reverse proxies ie a failover https en wikipedia org wiki failover further increases complexity source s and further reading reverse proxy vs load balancer https www nginx com resources glossary reverse proxy vs load balancer nginx architecture https www nginx com blog inside nginx how we designed for performance scale haproxy architecture guide http www haproxy org download 1 2 doc architecture txt wikipedia https en wikipedia org wiki reverse proxy application layer p align center img src images yb5sywm png br i a href http lethain com introduction to architecting systems for scale platform layer source intro to architecting systems for scale a i p separating out the web layer from the application layer also known as platform layer allows you to scale and configure both layers independently adding a new api results in adding application servers without necessarily adding additional web servers the single responsibility principle advocates for small and autonomous services that work together small teams with small services can plan more aggressively for rapid growth workers in the application layer also help enable asynchronism asynchronism microservices related to this discussion are microservices https en wikipedia org wiki microservices which can be described as a suite of independently deployable small modular services each service runs a unique process and communicates through a well defined lightweight mechanism to serve a business goal sup a href https smartbear com learn api design what are microservices 1 a sup pinterest for example could have the following microservices user profile follower feed search photo upload etc service discovery systems such as consul https www consul io docs index html etcd https coreos com etcd docs latest and zookeeper http www slideshare net sauravhaloi introduction to apache zookeeper can help services find each other by keeping track of registered names addresses and ports health checks https www consul io intro getting started checks html help verify service integrity and are often done using an http hypertext transfer protocol http endpoint both consul and etcd have a built in key value store key value store that can be useful for storing config values and other shared data disadvantage s application layer adding an application layer with loosely coupled services requires a different approach from an architectural operations and process viewpoint vs a monolithic system microservices can add complexity in terms of deployments and operations source s and further reading intro to architecting systems for scale http lethain com introduction to architecting systems for scale crack the system design interview http www puncsky com blog 2016 02 13 crack the system design interview service oriented architecture https en wikipedia org wiki service oriented architecture introduction to zookeeper http www slideshare net sauravhaloi introduction to apache zookeeper here s what you need to know about building microservices https cloudncode wordpress com 2016 07 22 msa getting started database p align center img src images xkm5cxz png br i a href https www youtube com watch v kkjm4ehyims source scaling up to your first 10 million users a i p relational database management system rdbms a relational database like sql is a collection of data items organized in tables acid is a set of properties of relational database transactions https en wikipedia org wiki database transaction atomicity each transaction is all or nothing consistency any transaction will bring the database from one valid state to another isolation executing transactions concurrently has the same results as if the transactions were executed serially durability once a transaction has been committed it will remain so there are many techniques to scale a relational database master slave replication master master replication federation sharding denormalization and sql tuning master slave replication the master serves reads and writes replicating writes to one or more slaves which serve only reads slaves can also replicate to additional slaves in a tree like fashion if the master goes offline the system can continue to operate in read only mode until a slave is promoted to a master or a new master is provisioned p align center img src images c9iogtn png br i a href http www slideshare net jboner scalability availability stability patterns source scalability availability stability patterns a i p disadvantage s master slave replication additional logic is needed to promote a slave to a master see disadvantage s replication disadvantages replication for points related to both master slave and master master master master replication both masters serve reads and writes and coordinate with each other on writes if either master goes down the system can continue to operate with both reads and writes p align center img src images krahlgg png br i a href http www slideshare net jboner scalability availability stability patterns source scalability availability stability patterns a i p disadvantage s master master replication you ll need a load balancer or you ll need to make changes to your application logic to determine where to write most master master systems are either loosely consistent violating acid or have increased write latency due to synchronization conflict resolution comes more into play as more write nodes are added and as latency increases see disadvantage s replication disadvantages replication for points related to both master slave and master master disadvantage s replication there is a potential for loss of data if the master fails before any newly written data can be replicated to other nodes writes are replayed to the read replicas if there are a lot of writes the read replicas can get bogged down with replaying writes and can t do as many reads the more read slaves the more you have to replicate which leads to greater replication lag on some systems writing to the master can spawn multiple threads to write in parallel whereas read replicas only support writing sequentially with a single thread replication adds more hardware and additional complexity source s and further reading replication scalability availability stability patterns http www slideshare net jboner scalability availability stability patterns multi master replication https en wikipedia org wiki multi master replication federation p align center img src images u3qv33e png br i a href https www youtube com watch v kkjm4ehyims source scaling up to your first 10 million users a i p federation or functional partitioning splits up databases by function for example instead of a single monolithic database you could have three databases forums users and products resulting in less read and write traffic to each database and therefore less replication lag smaller databases result in more data that can fit in memory which in turn results in more cache hits due to improved cache locality with no single central master serializing writes you can write in parallel increasing throughput disadvantage s federation federation is not effective if your schema requires huge functions or tables you ll need to update your application logic to determine which database to read and write joining data from two databases is more complex with a server link http stackoverflow com questions 5145637 querying data by joining two tables in two database on different servers federation adds more hardware and additional complexity source s and further reading federation scaling up to your first 10 million users https www youtube com watch v kkjm4ehyims sharding p align center img src images wu8x5id png br i a href http www slideshare net jboner scalability availability stability patterns source scalability availability stability patterns a i p sharding distributes data across different databases such that each database can only manage a subset of the data taking a users database as an example as the number of users increases more shards are added to the cluster similar to the advantages of federation federation sharding results in less read and write traffic less replication and more cache hits index size is also reduced which generally improves performance with faster queries if one shard goes down the other shards are still operational although you ll want to add some form of replication to avoid data loss like federation there is no single central master serializing writes allowing you to write in parallel with increased throughput common ways to shard a table of users is either through the user s last name initial or the user s geographic location disadvantage s sharding you ll need to update your application logic to work with shards which could result in complex sql queries data distribution can become lopsided in a shard for example a set of power users on a shard could result in increased load to that shard compared to others rebalancing adds additional complexity a sharding function based on consistent hashing http www paperplanes de 2011 12 9 the magic of consistent hashing html can reduce the amount of transferred data joining data from multiple shards is more complex sharding adds more hardware and additional complexity source s and further reading sharding the coming of the shard http highscalability com blog 2009 8 6 an unorthodox approach to database design the coming of the html shard database architecture https en wikipedia org wiki shard database architecture consistent hashing http www paperplanes de 2011 12 9 the magic of consistent hashing html denormalization denormalization attempts to improve read performance at the expense of some write performance redundant copies of the data are written in multiple tables to avoid expensive joins some rdbms such as postgresql https en wikipedia org wiki postgresql and oracle support materialized views https en wikipedia org wiki materialized view which handle the work of storing redundant information and keeping redundant copies consistent once data becomes distributed with techniques such as federation federation and sharding sharding managing joins across data centers further increases complexity denormalization might circumvent the need for such complex joins in most systems reads can heavily outnumber writes 100 1 or even 1000 1 a read resulting in a complex database join can be very expensive spending a significant amount of time on disk operations disadvantage s denormalization data is duplicated constraints can help redundant copies of information stay in sync which increases complexity of the database design a denormalized database under heavy write load might perform worse than its normalized counterpart source s and further reading denormalization denormalization https en wikipedia org wiki denormalization sql tuning sql tuning is a broad topic and many books https www amazon com s ref nb sb noss 2 url search alias 3daps field keywords sql tuning have been written as reference it s important to benchmark and profile to simulate and uncover bottlenecks benchmark simulate high load situations with tools such as ab http httpd apache org docs 2 2 programs ab html profile enable tools such as the slow query log http dev mysql com doc refman 5 7 en slow query log html to help track performance issues benchmarking and profiling might point you to the following optimizations tighten up the schema mysql dumps to disk in contiguous blocks for fast access use char instead of varchar for fixed length fields char effectively allows for fast random access whereas with varchar you must find the end of a string before moving onto the next one use text for large blocks of text such as blog posts text also allows for boolean searches using a text field results in storing a pointer on disk that is used to locate the text block use int for larger numbers up to 2 32 or 4 billion use decimal for currency to avoid floating point representation errors avoid storing large blobs store the location of where to get the object instead varchar 255 is the largest number of characters that can be counted in an 8 bit number often maximizing the use of a byte in some rdbms set the not null constraint where applicable to improve search performance http stackoverflow com questions 1017239 how do null values affect performance in a database search use good indices columns that you are querying select group by order by join could be faster with indices indices are usually represented as self balancing b tree https en wikipedia org wiki b tree that keeps data sorted and allows searches sequential access insertions and deletions in logarithmic time placing an index can keep the data in memory requiring more space writes could also be slower since the index also needs to be updated when loading large amounts of data it might be faster to disable indices load the data then rebuild the indices avoid expensive joins denormalize denormalization where performance demands it partition tables break up a table by putting hot spots in a separate table to help keep it in memory tune the query cache in some cases the query cache https dev mysql com doc refman 5 7 en query cache html could lead to performance issues https www percona com blog 2016 10 12 mysql 5 7 performance tuning immediately after installation source s and further reading sql tuning tips for optimizing mysql queries http aiddroid com 10 tips optimizing mysql queries dont suck is there a good reason i see varchar 255 used so often http stackoverflow com questions 1217466 is there a good reason i see varchar255 used so often as opposed to another l how do null values affect performance http stackoverflow com questions 1017239 how do null values affect performance in a database search slow query log http dev mysql com doc refman 5 7 en slow query log html nosql nosql is a collection of data items represented in a key value store document store wide column store or a graph database data is denormalized and joins are generally done in the application code most nosql stores lack true acid transactions and favor eventual consistency eventual consistency base is often used to describe the properties of nosql databases in comparison with the cap theorem cap theorem base chooses availability over consistency basically available the system guarantees availability soft state the state of the system may change over time even without input eventual consistency the system will become consistent over a period of time given that the system doesn t receive input during that period in addition to choosing between sql or nosql sql or nosql it is helpful to understand which type of nosql database best fits your use case s we ll review key value stores document stores wide column stores and graph databases in the next section key value store abstraction hash table a key value store generally allows for o 1 reads and writes and is often backed by memory or ssd data stores can maintain keys in lexicographic order https en wikipedia org wiki lexicographical order allowing efficient retrieval of key ranges key value stores can allow for storing of metadata with a value key value stores provide high performance and are often used for simple data models or for rapidly changing data such as an in memory cache layer since they offer only a limited set of operations complexity is shifted to the application layer if additional operations are needed a key value store is the basis for more complex systems such as a document store and in some cases a graph database source s and further reading key value store key value database https en wikipedia org wiki key value database disadvantages of key value stores http stackoverflow com questions 4056093 what are the disadvantages of using a key value table over nullable columns or redis architecture http qnimate com overview of redis architecture memcached architecture https adayinthelifeof nl 2011 02 06 memcache internals document store abstraction key value store with documents stored as values a document store is centered around documents xml json binary etc where a document stores all information for a given object document stores provide apis or a query language to query based on the internal structure of the document itself note many key value stores include features for working with a value s metadata blurring the lines between these two storage types based on the underlying implementation documents are organized by collections tags metadata or directories although documents can be organized or grouped together documents may have fields that are completely different from each other some document stores like mongodb https www mongodb com mongodb architecture and couchdb https blog couchdb org 2016 08 01 couchdb 2 0 architecture also provide a sql like language to perform complex queries dynamodb http www read seas harvard edu kohler class cs239 w08 decandia07dynamo pdf supports both key values and documents document stores provide high flexibility and are often used for working with occasionally changing data source s and further reading document store document oriented database https en wikipedia org wiki document oriented database mongodb architecture https www mongodb com mongodb architecture couchdb architecture https blog couchdb org 2016 08 01 couchdb 2 0 architecture elasticsearch architecture https www elastic co blog found elasticsearch from the bottom up wide column store p align center img src images n16iogk png br i a href http blog grio com 2015 11 sql nosql a brief history html source sql nosql a brief history a i p abstraction nested map columnfamily rowkey columns colkey value timestamp a wide column store s basic unit of data is a column name value pair a column can be grouped in column families analogous to a sql table super column families further group column families you can access each column independently with a row key and columns with the same row key form a row each value contains a timestamp for versioning and for conflict resolution google introduced bigtable http www read seas harvard edu kohler class cs239 w08 chang06bigtable pdf as the first wide column store which influenced the open source hbase https www edureka co blog hbase architecture often used in the hadoop ecosystem and cassandra http docs datastax com en cassandra 3 0 cassandra architecture archintro html from facebook stores such as bigtable hbase and cassandra maintain keys in lexicographic order allowing efficient retrieval of selective key ranges wide column stores offer high availability and high scalability they are often used for very large data sets source s and further reading wide column store sql nosql a brief history http blog grio com 2015 11 sql nosql a brief history html bigtable architecture http www read seas harvard edu kohler class cs239 w08 chang06bigtable pdf hbase architecture https www edureka co blog hbase architecture cassandra architecture http docs datastax com en cassandra 3 0 cassandra architecture archintro html graph database p align center img src images fncl65g png br i a href https en wikipedia org wiki file graphdatabase propertygraph png source graph database a i p abstraction graph in a graph database each node is a record and each arc is a relationship between two nodes graph databases are optimized to represent complex relationships with many foreign keys or many to many relationships graphs databases offer high performance for data models with complex relationships such as a social network they are relatively new and are not yet widely used it might be more difficult to find development tools and resources many graphs can only be accessed with rest apis representational state transfer rest source s and further reading graph graph database https en wikipedia org wiki graph database neo4j https neo4j com flockdb https blog twitter com 2010 introducing flockdb source s and further reading nosql explanation of base terminology http stackoverflow com questions 3342497 explanation of base terminology nosql databases a survey and decision guidance https medium com baqend blog nosql databases a survey and decision guidance ea7823a822d wskogqenq scalability http www lecloud net post 7994751381 scalability for dummies part 2 database introduction to nosql https www youtube com watch v qi g07c q5i nosql patterns http horicky blogspot com 2009 11 nosql patterns html sql or nosql p align center img src images wxgqg5f png br i a href https www infoq com articles transition rdbms nosql source transitioning from rdbms to nosql a i p reasons for sql structured data strict schema relational data need for complex joins transactions clear patterns for scaling more established developers community code tools etc lookups by index are very fast reasons for nosql semi structured data dynamic or flexible schema non relational data no need for complex joins store many tb or pb of data very data intensive workload very high throughput for iops sample data well suited for nosql rapid ingest of clickstream and log data leaderboard or scoring data temporary data such as a shopping cart frequently accessed hot tables metadata lookup tables source s and further reading sql or nosql scaling up to your first 10 million users https www youtube com watch v kkjm4ehyims sql vs nosql differences https www sitepoint com sql vs nosql differences cache p align center img src images q6z24la png br i a href http horicky blogspot com 2010 10 scalable system design patterns html source scalable system design patterns a i p caching improves page load times and can reduce the load on your servers and databases in this model the dispatcher will first lookup if the request has been made before and try to find the previous result to return in order to save the actual execution databases often benefit from a uniform distribution of reads and writes across its partitions popular items can skew the distribution causing bottlenecks putting a cache in front of a database can help absorb uneven loads and spikes in traffic client caching caches can be located on the client side os or browser server side reverse proxy web server or in a distinct cache layer cdn caching cdns content delivery network are considered a type of cache web server caching reverse proxies reverse proxy web server and caches such as varnish https www varnish cache org can serve static and dynamic content directly web servers can also cache requests returning responses without having to contact application servers database caching your database usually includes some level of caching in a default configuration optimized for a generic use case tweaking these settings for specific usage patterns can further boost performance application caching in memory caches such as memcached and redis are key value stores between your application and your data storage since the data is held in ram it is much faster than typical databases where data is stored on disk ram is more limited than disk so cache invalidation https en wikipedia org wiki cache algorithms algorithms such as least recently used lru https en wikipedia org wiki cache replacement policies least recently used lru can help invalidate cold entries and keep hot data in ram redis has the following additional features persistence option built in data structures such as sorted sets and lists there are multiple levels you can cache that fall into two general categories database queries and objects row level query level fully formed serializable objects fully rendered html generally you should try to avoid file based caching as it makes cloning and auto scaling more difficult caching at the database query level whenever you query the database hash the query as a key and store the result to the cache this approach suffers from expiration issues hard to delete a cached result with complex queries if one piece of data changes such as a table cell you need to delete all cached queries that might include the changed cell caching at the object level see your data as an object similar to what you do with your application code have your application assemble the dataset from the database into a class instance or a data structure s remove the object from cache if its underlying data has changed allows for asynchronous processing workers assemble objects by consuming the latest cached object suggestions of what to cache user sessions fully rendered web pages activity streams user graph data when to update the cache since you can only store a limited amount of data in cache you ll need to determine which cache update strategy works best for your use case cache aside p align center img src images onjorqk png br i a href http www slideshare net tmatyashovsky from cache to in memory data grid introduction to hazelcast source from cache to in memory data grid a i p the application is responsible for reading and writing from storage the cache does not interact with storage directly the application does the following look for entry in cache resulting in a cache miss load entry from the database add entry to cache return entry python def get user self user id user cache get user 0 user id if user is none user db query select from users where user id 0 user id if user is not none key user 0 format user id cache set key json dumps user return user memcached https memcached org is generally used in this manner subsequent reads of data added to cache are fast cache aside is also referred to as lazy loading only requested data is cached which avoids filling up the cache with data that isn t requested disadvantage s cache aside each cache miss results in three trips which can cause a noticeable delay data can become stale if it is updated in the database this issue is mitigated by setting a time to live ttl which forces an update of the cache entry or by using write through when a node fails it is replaced by a new empty node increasing latency write through p align center img src images 0vbc0hn png br i a href http www slideshare net jboner scalability availability stability patterns source scalability availability stability patterns a i p the application uses the cache as the main data store reading and writing data to it while the cache is responsible for reading and writing to the database application adds updates entry in cache cache synchronously writes entry to data store return application code python set user 12345 foo bar cache code python def set user user id values user db query update users where id 0 user id values cache set user id user write through is a slow overall operation due to the write operation but subsequent reads of just written data are fast users are generally more tolerant of latency when updating data than reading data data in the cache is not stale disadvantage s write through when a new node is created due to failure or scaling the new node will not cache entries until the entry is updated in the database cache aside in conjunction with write through can mitigate this issue most data written might never be read which can be minimized with a ttl write behind write back p align center img src images rgsrvjg png br i a href http www slideshare net jboner scalability availability stability patterns source scalability availability stability patterns a i p in write behind the application does the following add update entry in cache asynchronously write entry to the data store improving write performance disadvantage s write behind there could be data loss if the cache goes down prior to its contents hitting the data store it is more complex to implement write behind than it is to implement cache aside or write through refresh ahead p align center img src images kxtjqge png br i a href http www slideshare net tmatyashovsky from cache to in memory data grid introduction to hazelcast source from cache to in memory data grid a i p you can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration refresh ahead can result in reduced latency vs read through if the cache can accurately predict which items are likely to be needed in the future disadvantage s refresh ahead not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh ahead disadvantage s cache need to maintain consistency between caches and the source of truth such as the database through cache invalidation https en wikipedia org wiki cache algorithms cache invalidation is a difficult problem there is additional complexity associated with when to update the cache need to make application changes such as adding redis or memcached source s and further reading from cache to in memory data grid http www slideshare net tmatyashovsky from cache to in memory data grid introduction to hazelcast scalable system design patterns http horicky blogspot com 2010 10 scalable system design patterns html introduction to architecting systems for scale http lethain com introduction to architecting systems for scale scalability availability stability patterns http www slideshare net jboner scalability availability stability patterns scalability http www lecloud net post 9246290032 scalability for dummies part 3 cache aws elasticache strategies http docs aws amazon com amazonelasticache latest userguide strategies html wikipedia https en wikipedia org wiki cache computing asynchronism p align center img src images 54gyssx png br i a href http lethain com introduction to architecting systems for scale platform layer source intro to architecting systems for scale a i p asynchronous workflows help reduce request times for expensive operations that would otherwise be performed in line they can also help by doing time consuming work in advance such as periodic aggregation of data message queues message queues receive hold and deliver messages if an operation is too slow to perform inline you can use a message queue with the following workflow an application publishes a job to the queue then notifies the user of job status a worker picks up the job from the queue processes it then signals the job is complete the user is not blocked and the job is processed in the background during this time the client might optionally do a small amount of processing to make it seem like the task has completed for example if posting a tweet the tweet could be instantly posted to your timeline but it could take some time before your tweet is actually delivered to all of your followers redis https redis io is useful as a simple message broker but messages can be lost rabbitmq https www rabbitmq com is popular but requires you to adapt to the amqp protocol and manage your own nodes amazon sqs https aws amazon com sqs is hosted but can have high latency and has the possibility of messages being delivered twice task queues tasks queues receive tasks and their related data runs them then delivers their results they can support scheduling and can be used to run computationally intensive jobs in the background celery https docs celeryproject org en stable has support for scheduling and primarily has python support back pressure if queues start to grow significantly the queue size can become larger than memory resulting in cache misses disk reads and even slower performance back pressure http mechanical sympathy blogspot com 2012 05 apply back pressure when overloaded html can help by limiting the queue size thereby maintaining a high throughput rate and good response times for jobs already in the queue once the queue fills up clients get a server busy or http 503 status code to try again later clients can retry the request at a later time perhaps with exponential backoff https en wikipedia org wiki exponential backoff disadvantage s asynchronism use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations as introducing queues can add delays and complexity source s and further reading it s all a numbers game https www youtube com watch v 1kryh75wgy4 applying back pressure when overloaded http mechanical sympathy blogspot com 2012 05 apply back pressure when overloaded html little s law https en wikipedia org wiki little 27s law what is the difference between a message queue and a task queue https www quora com what is the difference between a message queue and a task queue why would a task queue require a message broker like rabbitmq redis celery or ironmq to function communication p align center img src images 5keocqs jpg br i a href http www escotal com osilayer html source osi 7 layer model a i p hypertext transfer protocol http http is a method for encoding and transporting data between a client and a server it is a request response protocol clients issue requests and servers issue responses with relevant content and completion status info about the request http is self contained allowing requests and responses to flow through many intermediate routers and servers that perform load balancing caching encryption and compression a basic http request consists of a verb method and a resource endpoint below are common http verbs verb description idempotent safe cacheable get reads a resource yes yes yes post creates a resource or trigger a process that handles data no no yes if response contains freshness info put creates or replace a resource yes no no patch partially updates a resource no no yes if response contains freshness info delete deletes a resource yes no no can be called many times without different outcomes http is an application layer protocol relying on lower level protocols such as tcp and udp source s and further reading http what is http https www nginx com resources glossary http difference between http and tcp https www quora com what is the difference between http protocol and tcp protocol difference between put and patch https laracasts com discuss channels general discussion whats the differences between put and patch page 1 transmission control protocol tcp p align center img src images jdasdvg jpg br i a href http www wildbunny co uk blog 2012 10 09 how to make a multi player game part 1 source how to make a multiplayer game a i p tcp is a connection oriented protocol over an ip network https en wikipedia org wiki internet protocol connection is established and terminated using a handshake https en wikipedia org wiki handshaking all packets sent are guaranteed to reach the destination in the original order and without corruption through sequence numbers and checksum fields https en wikipedia org wiki transmission control protocol checksum computation for each packet acknowledgement https en wikipedia org wiki acknowledgement data networks packets and automatic retransmission if the sender does not receive a correct response it will resend the packets if there are multiple timeouts the connection is dropped tcp also implements flow control https en wikipedia org wiki flow control data and congestion control https en wikipedia org wiki network congestion congestion control these guarantees cause delays and generally result in less efficient transmission than udp to ensure high throughput web servers can keep a large number of tcp connections open resulting in high memory usage it can be expensive to have a large number of open connections between web server threads and say a memcached https memcached org server connection pooling https en wikipedia org wiki connection pool can help in addition to switching to udp where applicable tcp is useful for applications that require high reliability but are less time critical some examples include web servers database info smtp ftp and ssh use tcp over udp when you need all of the data to arrive intact you want to automatically make a best estimate use of the network throughput user datagram protocol udp p align center img src images yzdrjta jpg br i a href http www wildbunny co uk blog 2012 10 09 how to make a multi player game part 1 source how to make a multiplayer game a i p udp is connectionless datagrams analogous to packets are guaranteed only at the datagram level datagrams might reach their destination out of order or not at all udp does not support congestion control without the guarantees that tcp support udp is generally more efficient udp can broadcast sending datagrams to all devices on the subnet this is useful with dhcp https en wikipedia org wiki dynamic host configuration protocol because the client has not yet received an ip address thus preventing a way for tcp to stream without the ip address udp is less reliable but works well in real time use cases such as voip video chat streaming and realtime multiplayer games use udp over tcp when you need the lowest latency late data is worse than loss of data you want to implement your own error correction source s and further reading tcp and udp networking for game programming http gafferongames com networking for game programmers udp vs tcp key differences between tcp and udp protocols http www cyberciti biz faq key differences between tcp and udp protocols difference between tcp and udp http stackoverflow com questions 5970383 difference between tcp and udp transmission control protocol https en wikipedia org wiki transmission control protocol user datagram protocol https en wikipedia org wiki user datagram protocol scaling memcache at facebook http www cs bu edu jappavoo jappavoo github com 451 papers memcache fb pdf remote procedure call rpc p align center img src images if4mkb5 png br i a href http www puncsky com blog 2016 02 13 crack the system design interview source crack the system design interview a i p in an rpc a client causes a procedure to execute on a different address space usually a remote server the procedure is coded as if it were a local procedure call abstracting away the details of how to communicate with the server from the client program remote calls are usually slower and less reliable than local calls so it is helpful to distinguish rpc calls from local calls popular rpc frameworks include protobuf https developers google com protocol buffers thrift https thrift apache org and avro https avro apache org docs current rpc is a request response protocol client program calls the client stub procedure the parameters are pushed onto the stack like a local procedure call client stub procedure marshals packs procedure id and arguments into a request message client communication module os sends the message from the client to the server server communication module os passes the incoming packets to the server stub procedure server stub procedure unmarshalls the results calls the server procedure matching the procedure id and passes the given arguments the server response repeats the steps above in reverse order sample rpc calls get someoperation data anid post anotheroperation data anid anotherdata another value rpc is focused on exposing behaviors rpcs are often used for performance reasons with internal communications as you can hand craft native calls to better fit your use cases choose a native library aka sdk when you know your target platform you want to control how your logic is accessed you want to control how error control happens off your library performance and end user experience is your primary concern http apis following rest tend to be used more often for public apis disadvantage s rpc rpc clients become tightly coupled to the service implementation a new api must be defined for every new operation or use case it can be difficult to debug rpc you might not be able to leverage existing technologies out of the box for example it might require additional effort to ensure rpc calls are properly cached http etherealbits com 2012 12 debunking the myths of rpc rest on caching servers such as squid http www squid cache org representational state transfer rest rest is an architectural style enforcing a client server model where the client acts on a set of resources managed by the server the server provides a representation of resources and actions that can either manipulate or get a new representation of resources all communication must be stateless and cacheable there are four qualities of a restful interface identify resources uri in http use the same uri regardless of any operation change with representations verbs in http use verbs headers and body self descriptive error message status response in http use status codes don t reinvent the wheel hateoas http restcookbook com basics hateoas html interface for http your web service should be fully accessible in a browser sample rest calls get someresources anid put someresources anid anotherdata another value rest is focused on exposing data it minimizes the coupling between client server and is often used for public http apis rest uses a more generic and uniform method of exposing resources through uris representation through headers https github com for get know your http well blob master headers md and actions through verbs such as get post put delete and patch being stateless rest is great for horizontal scaling and partitioning disadvantage s rest with rest being focused on exposing data it might not be a good fit if resources are not naturally organized or accessed in a simple hierarchy for example returning all updated records from the past hour matching a particular set of events is not easily expressed as a path with rest it is likely to be implemented with a combination of uri path query parameters and possibly the request body rest typically relies on a few verbs get post put delete and patch which sometimes doesn t fit your use case for example moving expired documents to the archive folder might not cleanly fit within these verbs fetching complicated resources with nested hierarchies requires multiple round trips between the client and server to render single views e g fetching content of a blog entry and the comments on that entry for mobile applications operating in variable network conditions these multiple roundtrips are highly undesirable over time more fields might be added to an api response and older clients will receive all new data fields even those that they do not need as a result it bloats the payload size and leads to larger latencies rpc and rest calls comparison operation rpc rest signup post signup post persons resign post resign br br personid 1234 br delete persons 1234 read a person get readperson personid 1234 get persons 1234 read a person s items list get readusersitemslist personid 1234 get persons 1234 items add an item to a person s items post additemtousersitemslist br br personid 1234 br itemid 456 br post persons 1234 items br br itemid 456 br update an item post modifyitem br br itemid 456 br key value br put items 456 br br key value br delete an item post removeitem br br itemid 456 br delete items 456 p align center i a href https apihandyman io do you really know why you prefer rest over rpc source do you really know why you prefer rest over rpc a i p source s and further reading rest and rpc do you really know why you prefer rest over rpc https apihandyman io do you really know why you prefer rest over rpc when are rpc ish approaches more appropriate than rest http programmers stackexchange com a 181186 rest vs json rpc http stackoverflow com questions 15056878 rest vs json rpc debunking the myths of rpc and rest http etherealbits com 2012 12 debunking the myths of rpc rest what are the drawbacks of using rest https www quora com what are the drawbacks of using restful apis crack the system design interview http www puncsky com blog 2016 02 13 crack the system design interview thrift https code facebook com posts 1468950976659943 why rest for internal use and not rpc http arstechnica com civis viewtopic php t 1190508 security this section could use some updates consider contributing contributing security is a broad topic unless you have considerable experience a security background or are applying for a position that requires knowledge of security you probably won t need to know more than the basics encrypt in transit and at rest sanitize all user inputs or any input parameters exposed to user to prevent xss https en wikipedia org wiki cross site scripting and sql injection https en wikipedia org wiki sql injection use parameterized queries to prevent sql injection use the principle of least privilege https en wikipedia org wiki principle of least privilege source s and further reading api security checklist https github com shieldfy api security checklist security guide for developers https github com fallibleinc security guide for developers owasp top ten https www owasp org index php owasp top ten cheat sheet appendix you ll sometimes be asked to do back of the envelope estimates for example you might need to determine how long it will take to generate 100 image thumbnails from disk or how much memory a data structure will take the powers of two table and latency numbers every programmer should know are handy references powers of two table power exact value approx value bytes 7 128 8 256 10 1024 1 thousand 1 kb 16 65 536 64 kb 20 1 048 576 1 million 1 mb 30 1 073 741 824 1 billion 1 gb 32 4 294 967 296 4 gb 40 1 099 511 627 776 1 trillion 1 tb source s and further reading powers of two https en wikipedia org wiki power of two latency numbers every programmer should know latency comparison numbers l1 cache reference 0 5 ns branch mispredict 5 ns l2 cache reference 7 ns 14x l1 cache mutex lock unlock 25 ns main memory reference 100 ns 20x l2 cache 200x l1 cache compress 1k bytes with zippy 10 000 ns 10 us send 1 kb bytes over 1 gbps network 10 000 ns 10 us read 4 kb randomly from ssd 150 000 ns 150 us 1gb sec ssd read 1 mb sequentially from memory 250 000 ns 250 us round trip within same datacenter 500 000 ns 500 us read 1 mb sequentially from ssd 1 000 000 ns 1 000 us 1 ms 1gb sec ssd 4x memory hdd seek 10 000 000 ns 10 000 us 10 ms 20x datacenter roundtrip read 1 mb sequentially from 1 gbps 10 000 000 ns 10 000 us 10 ms 40x memory 10x ssd read 1 mb sequentially from hdd 30 000 000 ns 30 000 us 30 ms 120x memory 30x ssd send packet ca netherlands ca 150 000 000 ns 150 000 us 150 ms notes 1 ns 10 9 seconds 1 us 10 6 seconds 1 000 ns 1 ms 10 3 seconds 1 000 us 1 000 000 ns handy metrics based on numbers above read sequentially from hdd at 30 mb s read sequentially from 1 gbps ethernet at 100 mb s read sequentially from ssd at 1 gb s read sequentially from main memory at 4 gb s 6 7 world wide round trips per second 2 000 round trips per second within a data center latency numbers visualized https camo githubusercontent com 77f72259e1eb58596b564d1ad823af1853bc60a3 687474703a2f2f692e696d6775722e636f6d2f6b307431652e706e67 source s and further reading latency numbers every programmer should know 1 https gist github com jboner 2841832 latency numbers every programmer should know 2 https gist github com hellerbarde 2843375 designs lessons and advice from building large distributed systems http www cs cornell edu projects ladis2009 talks dean keynote ladis2009 pdf software engineering advice from building large scale distributed systems https static googleusercontent com media research google com en people jeff stanford 295 talk pdf additional system design interview questions common system design interview questions with links to resources on how to solve each question reference s design a file sync service like dropbox youtube com https www youtube com watch v pe4gwstwhmc design a search engine like google queue acm org http queue acm org detail cfm id 988407 br stackexchange com http programmers stackexchange com questions 38324 interview question how would you implement google search br ardendertat com http www ardendertat com 2012 01 11 implementing search engines br stanford edu http infolab stanford edu backrub google html design a scalable web crawler like google quora com https www quora com how can i build a web crawler from scratch design google docs code google com https code google com p google mobwrite br neil fraser name https neil fraser name writing sync design a key value store like redis slideshare net http www slideshare net dvirsky introduction to redis design a cache system like memcached slideshare net http www slideshare net oemebamo introduction to memcached design a recommendation system like amazon s hulu com https web archive org web 20170406065247 http tech hulu com blog 2011 09 19 recommendation system html br ijcai13 org http ijcai13 org files tutorial slides td3 pdf design a tinyurl system like bitly n00tc0d3r blogspot com http n00tc0d3r blogspot com design a chat app like whatsapp highscalability com http highscalability com blog 2014 2 26 the whatsapp architecture facebook bought for 19 billion html design a picture sharing system like instagram highscalability com http highscalability com flickr architecture br highscalability com http highscalability com blog 2011 12 6 instagram architecture 14 million users terabytes of photos html design the facebook news feed function quora com http www quora com what are best practices for building something like a news feed br quora com http www quora com activity streams what are the scaling issues to keep in mind while developing a social network feed br slideshare net http www slideshare net danmckinley etsy activity feeds architecture design the facebook timeline function facebook com https www facebook com note php note id 10150468255628920 br highscalability com http highscalability com blog 2012 1 23 facebook timeline brought to you by the power of denormaliza html design the facebook chat function erlang factory com http www erlang factory com upload presentations 31 eugeneletuchy erlangatfacebook pdf br facebook com https www facebook com note php note id 14218138919 id 9445547199 index 0 design a graph search function like facebook s facebook com https www facebook com notes facebook engineering under the hood building out the infrastructure for graph search 10151347573598920 br facebook com https www facebook com notes facebook engineering under the hood indexing and ranking in graph search 10151361720763920 br facebook com https www facebook com notes facebook engineering under the hood the natural language interface of graph search 10151432733048920 design a content delivery network like cloudflare figshare com https figshare com articles globally distributed content delivery 6605972 design a trending topic system like twitter s michael noll com http www michael noll com blog 2013 01 18 implementing real time trending topics in storm br snikolov wordpress com http snikolov wordpress com 2012 11 14 early detection of twitter trends design a random id generation system blog twitter com https blog twitter com 2010 announcing snowflake br github com https github com twitter snowflake return the top k requests during a time interval cs ucsb edu https www cs ucsb edu sites default files documents 2005 23 pdf br wpi edu http davis wpi edu xmdv docs edbt11 diyang pdf design a system that serves data from multiple data centers highscalability com http highscalability com blog 2009 8 24 how google serves data from multiple datacenters html design an online multiplayer card game indieflashblog com https web archive org web 20180929181117 http www indieflashblog com how to create an asynchronous multiplayer game html br buildnewgames com http buildnewgames com real time multiplayer design a garbage collection system stuffwithstuff com http journal stuffwithstuff com 2013 12 08 babys first garbage collector br washington edu http courses cs washington edu courses csep521 07wi prj rick pdf design an api rate limiter https stripe com blog https stripe com blog rate limiters design a stock exchange like nasdaq or binance jane street https youtu be b1e4t2k2kjy br golang implementation https around25 com blog building a trading engine for a crypto exchange br go implementation http bhomnick net building a simple limit order in go add a system design question contribute contributing real world architectures articles on how real world systems are designed p align center img src images tcuo2fw png br i a href https www infoq com presentations twitter timeline scalability source twitter timelines at scale a i p don t focus on nitty gritty details for the following articles instead identify shared principles common technologies and patterns within these articles study what problems are solved by each component where it works where it doesn t review the lessons learned type system reference s data processing mapreduce distributed data processing from google research google com http static googleusercontent com media research google com zh cn us archive mapreduce osdi04 pdf data processing spark distributed data processing from databricks slideshare net http www slideshare net agrishchenko apache spark architecture data processing storm distributed data processing from twitter slideshare net http www slideshare net previa storm 16094009 data store bigtable distributed column oriented database from google harvard edu http www read seas harvard edu kohler class cs239 w08 chang06bigtable pdf data store hbase open source implementation of bigtable slideshare net http www slideshare net alexbaranau intro to hbase data store cassandra distributed column oriented database from facebook slideshare net http www slideshare net planetcassandra cassandra introduction features 30103666 data store dynamodb document oriented database from amazon harvard edu http www read seas harvard edu kohler class cs239 w08 decandia07dynamo pdf data store mongodb document oriented database slideshare net http www slideshare net mdirolf introduction to mongodb data store spanner globally distributed database from google research google com http research google com archive spanner osdi2012 pdf data store memcached distributed memory caching system slideshare net http www slideshare net oemebamo introduction to memcached data store redis distributed memory caching system with persistence and value types slideshare net http www slideshare net dvirsky introduction to redis file system google file system gfs distributed file system research google com http static googleusercontent com media research google com zh cn us archive gfs sosp2003 pdf file system hadoop file system hdfs open source implementation of gfs apache org http hadoop apache org docs stable hadoop project dist hadoop hdfs hdfsdesign html misc chubby lock service for loosely coupled distributed systems from google research google com http static googleusercontent com external content untrusted dlcp research google com en us archive chubby osdi06 pdf misc dapper distributed systems tracing infrastructure research google com http static googleusercontent com media research google com en pubs archive 36356 pdf misc kafka pub sub message queue from linkedin slideshare net http www slideshare net mumrah kafka talk tri hug misc zookeeper centralized infrastructure and services enabling synchronization slideshare net http www slideshare net sauravhaloi introduction to apache zookeeper add an architecture contribute contributing company architectures company reference s amazon amazon architecture http highscalability com amazon architecture cinchcast producing 1 500 hours of audio every day http highscalability com blog 2012 7 16 cinchcast architecture producing 1500 hours of audio every d html datasift realtime datamining at 120 000 tweets per second http highscalability com blog 2011 11 29 datasift architecture realtime datamining at 120000 tweets p html dropbox how we ve scaled dropbox https www youtube com watch v pe4gwstwhmc espn operating at 100 000 duh nuh nuhs per second http highscalability com blog 2013 11 4 espns architecture at scale operating at 100000 duh nuh nuhs html google google architecture http highscalability com google architecture instagram 14 million users terabytes of photos http highscalability com blog 2011 12 6 instagram architecture 14 million users terabytes of photos html br what powers instagram http instagram engineering tumblr com post 13649370142 what powers instagram hundreds of instances justin tv justin tv s live video broadcasting architecture http highscalability com blog 2010 3 16 justintvs live video broadcasting architecture html facebook scaling memcached at facebook https cs uwaterloo ca brecht courses 854 emerging 2014 readings key value fb memcached nsdi 2013 pdf br tao facebook s distributed data store for the social graph https cs uwaterloo ca brecht courses 854 emerging 2014 readings data store tao facebook distributed datastore atc 2013 pdf br facebook s photo storage https www usenix org legacy event osdi10 tech full papers beaver pdf br how facebook live streams to 800 000 simultaneous viewers http highscalability com blog 2016 6 27 how facebook live streams to 800000 simultaneous viewers html flickr flickr architecture http highscalability com flickr architecture mailbox from 0 to one million users in 6 weeks http highscalability com blog 2013 6 18 scaling mailbox from 0 to one million users in 6 weeks and 1 html netflix a 360 degree view of the entire netflix stack http highscalability com blog 2015 11 9 a 360 degree view of the entire netflix stack html br netflix what happens when you press play http highscalability com blog 2017 12 11 netflix what happens when you press play html pinterest from 0 to 10s of billions of page views a month http highscalability com blog 2013 4 15 scaling pinterest from 0 to 10s of billions of page views a html br 18 million visitors 10x growth 12 employees http highscalability com blog 2012 5 21 pinterest architecture update 18 million visitors 10x growth html playfish 50 million monthly users and growing http highscalability com blog 2010 9 21 playfishs social gaming architecture 50 million monthly user html plentyoffish plentyoffish architecture http highscalability com plentyoffish architecture salesforce how they handle 1 3 billion transactions a day http highscalability com blog 2013 9 23 salesforce architecture how they handle 13 billion transacti html stack overflow stack overflow architecture http highscalability com blog 2009 8 5 stack overflow architecture html tripadvisor 40m visitors 200m dynamic page views 30tb data http highscalability com blog 2011 6 27 tripadvisor architecture 40m visitors 200m dynamic page view html tumblr 15 billion page views a month http highscalability com blog 2012 2 13 tumblr architecture 15 billion page views a month and harder html twitter making twitter 10000 percent faster http highscalability com scaling twitter making twitter 10000 percent faster br storing 250 million tweets a day using mysql http highscalability com blog 2011 12 19 how twitter stores 250 million tweets a day using mysql html br 150m active users 300k qps a 22 mb s firehose http highscalability com blog 2013 7 8 the architecture twitter uses to deal with 150m active users html br timelines at scale https www infoq com presentations twitter timeline scalability br big and small data at twitter https www youtube com watch v 5cktp36hvgi br operations at twitter scaling beyond 100 million users https www youtube com watch v z8lu0cj6bou br how twitter handles 3 000 images per second http highscalability com blog 2016 4 20 how twitter handles 3000 images per second html uber how uber scales their real time market platform http highscalability com blog 2015 9 14 how uber scales their real time market platform html br lessons learned from scaling uber to 2000 engineers 1000 services and 8000 git repositories http highscalability com blog 2016 10 12 lessons learned from scaling uber to 2000 engineers 1000 ser html whatsapp the whatsapp architecture facebook bought for 19 billion http highscalability com blog 2014 2 26 the whatsapp architecture facebook bought for 19 billion html youtube youtube scalability https www youtube com watch v w5wvu624fy8 br youtube architecture http highscalability com youtube architecture company engineering blogs architectures for companies you are interviewing with questions you encounter might be from the same domain airbnb engineering http nerds airbnb com atlassian developers https developer atlassian com blog aws blog https aws amazon com blogs aws bitly engineering blog http word bitly com box blogs https blog box com blog category engineering cloudera developer blog http blog cloudera com dropbox tech blog https tech dropbox com engineering at quora https www quora com q quoraengineering ebay tech blog http www ebaytechblog com evernote tech blog https blog evernote com tech etsy code as craft http codeascraft com facebook engineering https www facebook com engineering flickr code http code flickr net foursquare engineering blog http engineering foursquare com github engineering blog https github blog category engineering google research blog http googleresearch blogspot com groupon engineering blog https engineering groupon com heroku engineering blog https engineering heroku com hubspot engineering blog http product hubspot com blog topic engineering high scalability http highscalability com instagram engineering http instagram engineering tumblr com intel software blog https software intel com en us blogs jane street tech blog https blogs janestreet com category ocaml linkedin engineering http engineering linkedin com blog microsoft engineering https engineering microsoft com microsoft python engineering https blogs msdn microsoft com pythonengineering netflix tech blog http techblog netflix com paypal developer blog https medium com paypal engineering pinterest engineering blog https medium com pinterest engineering reddit blog http www redditblog com salesforce engineering blog https developer salesforce com blogs engineering slack engineering blog https slack engineering spotify labs https labs spotify com twilio engineering blog http www twilio com engineering twitter engineering https blog twitter com engineering uber engineering blog http eng uber com yahoo engineering blog http yahooeng tumblr com yelp engineering blog http engineeringblog yelp com zynga engineering blog https www zynga com blogs engineering source s and further reading looking to add a blog to avoid duplicating work consider adding your company blog to the following repo kilimchoi engineering blogs https github com kilimchoi engineering blogs under development interested in adding a section or helping complete one in progress contribute contributing distributed computing with mapreduce consistent hashing scatter gather contribute contributing credits credits and sources are provided throughout this repo special thanks to hired in tech http www hiredintech com system design the system design process cracking the coding interview https www amazon com dp 0984782850 high scalability http highscalability com checkcheckzz system design interview https github com checkcheckzz system design interview shashank88 system design https github com shashank88 system design mmcgrana services engineering https github com mmcgrana services engineering system design cheat sheet https gist github com vasanthk 485d1c25737e8e72759f a distributed systems reading list http dancres github io pages cracking the system design interview http www puncsky com blog 2016 02 13 crack the system design interview contact info feel free to contact me to discuss any issues questions or comments my contact info can be found on my github page https github com donnemartin license i am providing code and resources in this repository to you under an open source license because this is my personal repository the license you receive to my code and resources is from me and not my employer facebook copyright 2017 donne martin creative commons attribution 4 0 international license cc by 4 0 http creativecommons org licenses by 4 0 | programming development design design-system system design-patterns web web-application webapp python interview interview-questions interview-practice | os |
valtech | valtech valued information technology services | server |
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local-government-design-systems | non federal government design systems pattern library directory background while there is a robust conversation around federal and sometimes state government innovation local governments are slower to reach the innovation game for every big city that s leading the way there are lots of smaller places around the world with folks sharing what they know i thought it d be useful to have a centralized place for local government design systems pattern libraries that are open sourced so others can learn share and remix since there are so few local government design systems that exist i ve expanded the repo to include state level systems feel free to submit others or contact me via twitter ronbronson http twitter com ronbronson resources city of austin texas https github com cityofaustin janis city of bloomington indiana design system https github com city of bloomington design system bristol city council pattern library uk http style bristol gov uk chicago design system https design chicago gov city of philadelphia digital standards http standards phila gov city of tampa pattern library https www tampagov net static pattern library city of boston pattern library https github com cityofboston patterns municipality of c rdoba argentina web templates https github com modernizacionmunicba guia estilos y web province of alberta corporate identity manual https corporateidentity alberta ca downloads alberta corporate identity manual pdf pdf state of massachusetts design system http mayflower digital mass gov | design-system city civic-tech design local-government | os |
graphite-design-system | p align center a href https graphitedesignsystem com img width 100 src https graphitedesignsystem com favicon svg alt graphite logo a p h1 align center graphite design system h1 p align center graphite is a href https paqt com paqt a s white label design system for digital products and experiences the system consists of working code design kits and human interface guidelines p p align center this repository contains the working code of the components the core components are a href https developer mozilla org en us docs web web components web components a and we offer wrappers for react vue 3 for optimal dx p p align center a href https github com paqtcom graphite design system blob master license img src https img shields io badge license mit blue svg alt graphite design system is released under the mit license a a href https www jsdelivr com package npm graphiteds core img src https data jsdelivr com v1 package npm graphiteds core badge alt graphite design system is available on jsdelivr a p h2 align center a href https graphitedesignsystem com getting started developers quick start quickstart a span span a href https graphitedesignsystem com documentation a h2 packages project package version downloads links core graphiteds core https www npmjs com package graphiteds core version https img shields io npm v graphiteds core latest svg https www npmjs com package graphiteds core a href https www npmjs com package graphiteds core target blank img src https img shields io npm dm graphiteds core svg alt npm downloads a readme md packages core readme md react graphiteds react https www npmjs com package graphiteds react version https img shields io npm v graphiteds react latest svg https www npmjs com package graphiteds react a href https www npmjs com package graphiteds react target blank img src https img shields io npm dm graphiteds react svg alt npm downloads a readme md packages react readme md vue 3 graphiteds vue https www npmjs com package graphiteds vue version https img shields io npm v graphiteds vue latest svg https www npmjs com package graphiteds vue a href https www npmjs com package graphiteds vue target blank img src https img shields io npm dm graphiteds vue svg alt npm downloads a readme md packages vue readme md getting started start using our design system by following our quick getting started guide https graphitedesignsystem com getting started developers quick start we would love to hear from you if you have any feedback or run into issues using our design system please file an issue https github com paqtcom graphite design system issues new on this repository development to start building the components using stencil https stenciljs com clone this repo to a new directory make sure you are using node v16 with npm v8 if you use nvm you can run nvm use bash git clone https github com paqtcom graphite design system git graphite design system cd graphite design system run these commands to setup this project bash npm install npm run bootstrap navigate to the core package packages core and run npm start bash cd packages core npm start now you can develop the components need help check out the stencil docs https stenciljs com docs my first component automated tests run this command in the core package packages core to test the core components bash npm run test or run this command in the root to test the core components and all the framework wrappers release a new version to create a new npm release you have to be a member of the graphiteds organization and be logged in to npm in your local terminal npm login your local terminal should also have permission to push to github switch to the master branch go to the root of the repo in your terminal run these commands bash npm run bootstrap npm run build this will generate all necessary builds in the packages core components and framework wrappers if successful you can then publish all the packages to npm with lerna bash npm run publish this will ask what the next version should be changes all the package json s creates a tag in the repo and publishes it to npm if your terminal has github and npm access | design-system webcomponents vue react javascript typescript | os |
django-flutter-todo | todo application flutter provider django django rest framework about todo application made with flutter https flutter dev flutter is an open source ui software development kit created by google provider https pub dev packages provider state management library for flutter django https www djangoproject com back end rest api using django rest framework https www django rest framework org features create a new task complete a task delete a task view incomplete tasks view complete tasks setup bash clone the project git clone git github com reecerose django flutter todo git djang flutter todo install pipenv if not installed already pip install pipenv install all python packages pipenv install r requirements txt pipenv shell setup backend cd backend make apply all migrations python manage py makemigrations python manage py migrate create new super user python manage py createsuperuser run python server http localhost 8000 python manage py runserver open new terminal and go to the mobile folder to setup front end cd mobile get flutter packages flutter packages get run flutter project flutter run screenshots home screen https static reecerose com images projects provider todos homescreen png this is the home screen from here you can see all of your tasks add new tasks and delete tasks add item https static reecerose com images projects provider todos additem png here you can add any task multiple tasks https static reecerose com images projects provider todos multipletasks png here you can see how it looks with multiple tasks added progress on multiple tasks https static reecerose com images projects provider todos progressonmultipletasks png here you can see how it looks with multiple tasks in different progression states completed tasks https static reecerose com images projects provider todos completedtasks png here you can see the compelted tasks tab incomplete tasks https static reecerose com images projects provider todos incompletetasks png here you can see the incompelte tasks tab | todo flutter flutter-examples django django-rest-framework python dart | front_end |
sparkify | sparkify sparkify a pretend startup wants to analyze data on their music streaming app what songs are people listening to what artists get the most play time what type of users are listening to the most music in order to answer those questions this project takes json files full of data and transforms it into a postgres database full of tables worth querying how to use these files locally 1 download fork the repository 2 create a credentials json file where the keys username and password are present these should be equal to the values that will log you in to your local postgres database 3 run create tables py this will create a fresh sparkdb postgres database and create the tables for that database 4 run etl py this will process that data listed below and add that data to the tables just created 5 have fun querying the database data in this project song data a subset of the million song dataset http millionsongdataset com nested in json files each file contains metadata about a song and the artist of that song log data json files generated by eventsim https github com interana eventsim which simulate activity logs from a music streaming app based on specified configurations files in this project sparkify app py this is a streamlit https streamlit io app which displays the different tables created in this project as well as some simple bar charts of the grouped data will be hosted in the future create tables py this file drops and creates the database tables this file is run to reset the tables before each etl script run etl ipynb reads and processes the first file from the song data and log data folders and loads them into the tables and contains detailed instructions on the process this is the building block used to create etl py etl py reads and processes files from the song data and log data folders and loads them into the database tables sql queries py contains all sql queries for the project test ipynb helper file to query the database after the tables are created and data is inserted erd of the database created in this project sparkify erd sparkifyerd png | server |
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blm | p float left img src anon sample1 png width 250 img src anon sample2 png width 245 img src anon sample3 png width 300 p anonymize blm protest images this repository automates blmprivacybot https twitter com blmprivacybot a twitter bot that shows the anonymized images to help keep protesters safe use our interface at blm stanford edu http blm stanford edu what s happened arrests at protests from public images over the past weeks we have seen an increasing number of arrests at blm protests with images circulating around the web enabling automatic identification of those individuals and subsequent arrests to hamper protest activity this primarily concerns social media protest images numerous applications have emerged in response to this threat that aim to anonymize protest images and enable people to continue protesting in safety of course this would require a shift on the public s part to recognize this issue and an easy and effective method for anonymization to surface in an ideal world platforms like twitter would enable an on platform solution so what s your goal ai to help alleviate some of the worst parts of ai the goal of this work is to leverage our group s knowledge of facial recognition ai to offer the most effective anonymization tool that evades the state of the art in facial recognition technology ai facial recognition models can still recognize blurred faces this work tries to discourage people from trying to recognize or reconstruct pixelated faces by masking people with an opaque mask we use the blm fist emoji as that mask for solidarity while posting anonymized images does not delete the originals we are starting with awareness and hope twitter and other platforms would offer an on platform solution might be a tall order but one can hope importantly this application does not save images we hope the transparency of this repository will allow for community input the twitter bot posts anonymized images based on the fair use policy however if your image is used and you d like it to be taken down we will do our best to do so immediately q a how can ai models still recognize blurred faces even if they cannot reconstruct them perfectly recognition is different from reconstruction facial recognition technology can still identify many blurred faces and is better than humans at it reconstruction is a much more arduous task see the difference between discriminative and generative models if you re curious reconstruction has recently been exposed to be very biased see lessons from pulse https thegradient pub pulse lessons blurring faces has the added threat of encouraging certain people or groups to de anonymize images through reconstruction or directly identifying individuals through recognition do you save my pre anonymized images no the goal of this tool is to protect your privacy and saving the images would be antithetical to that we don t save any images you give us or any of the anonymized images created from the ai model sometimes they re not perfect so saving them would still not be great if you like technical details the image is passed into the ai model on the cloud then the output is passed back and directly displayed in a base64 jpg on your screen the bot tweeted my image with the fists on it can you take it down yes absolutely please dm the bot or reply directly can you talk a bit more about your ai technical approach we build on state of the art crowd counting ai because it offers huge advantages to anonymizing crowds over traditional facial recognition models traditional methods can only find a few less than 20 or even less than 5 in a single image crowds of blm protesters can number in the hundreds and thousands and certainly around 50 in a single image the model we use in this work has been trained on over 1 2 million people in the open sourced research dataset called qnrf https www crcv ucf edu research data sets ucf qnrf with crowds ranging from the few to the the thousands false negatives are the worst error in our case the pretrained model weights live in the lsc cnn https github com val iisc lsc cnn that we build on precisely it s in a google drive folder linked from their readme other amazing tools we would love to showcase other parallel efforts please propose any we have missed here not only that if this is not the tool for you please check these tools out too image scrubber https everestpipkin github io image scrubber by everestpipkin censr ios and android app and more https www theverge com 21281897 how to hide faces scrub metadata photograph video protest built by and built on 0 this work is built by the stanford machine learning group we are krishna patel jq and sharon zhou 1 flask postgres template by sharonzhou https github com sharonzhou flask postgres template 2 image uploader by christianbayer https github com christianbayer image uploader 3 lsc cnn by vlad3996 https github com vlad3996 lsc cnn paper associated with this work article lsccnn20 author sam deepak babu and peri skand vishwanath and narayanan sundararaman mukuntha and kamath amogh and babu r venkatesh title locate size and count accurately resolving people in dense crowds via detection journal ieee transactions on pattern analysis and machine intelligence year 2020 offline mode see the offline branch https github com stanfordmlgroup blm tree offline to run this work offline using docker this awesome code was contributed by matthiaszimmermann https github com matthiaszimmermann | blm-protesters facial-recognition anonymized-images recognize-blurred-faces ai-models | ai |
altschool-cloud-journey | altschool cloud journey documenting my progress and journey in the altschool africa cloud engineering track | cloud |
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VMC_with_LPTFs | code accompanying the paper variational monte carlo with large patched transformers requirements a suitable conda https conda io environment named qsr can be created and activated with conda create name qsr conda install n qsr pip conda activate qsr pip install r requirements txt model builder training this script is used to train new models from scratch this is an example of a command to train an 8 times 8 rydberg lattice with v 7 delta omega 1 with a 2 times 2 patched transformer python train py train l 64 nloops 16 k 1024 sub directory 2x2 ptf patch 2x2 rydberg v 7 delta 1 omega 1 training parameters are shown when running python train py help train these are all possible training arguments training arguments l int total lattice size 8x8 would be l 64 q int number of minibatches per batch k int size of each minibatch b int total batch size should be q k nloops int number of loops within the off diag labels function higher values save ram and generally makes the code run faster up to 2x note you can only set this as high as your effective sequence length take l and divide by your patch size steps int number of training steps dir str output directory set to none for no output lr float learning rate seed int random seed for the run sgrad bool whether or not to sample with gradients otherwise create gradients in extra network run uses less ram when but slightly slower true grad bool set to false to approximate the gradients more efficient but approximate sub directory str string to add to the end of the output directory inside a subfolder rnn all optional rnn parameters can be viewed by running python train py help rnn these are the rnn parameters rnn optional arguments l int the total number of atoms in your lattice nh int rnn hidden size patch str number of atoms input predicted at once patch size the input sequence will have an effective length of l prod patch example values 2x2 2x3 2 4 rnntype string which type of rnn cell to use only elman and gru are valid options at the moment patched transformer ptf all optional ptf parameters can be viewed by running python train py help ptf these are your ptf parameters ptf optional arguments l int the total number of atoms in your lattice nh int transformer token size input patches are projected to match the token size patch str number of atoms input predicted at once patch size the input sequence will have an effective length of l prod patch example values 2x2 2x3 2 4 dropout float the amount of dropout to use in the transformer layers num layers int the number of transformer layers to use nhead int the number of heads to use in multi headed self attention this should divide nh repeat pre bool repeat the precondition input instead of projecting it out to match the token size large patched transformer lptf all optional lptf parameters can be viewed by running python train py help lptf lptf parameters must be followed by the sub model e g rnn and the corresponding parameters where the l parameter needs to match the patch parameter of the lptf e g lptf path 2x3 rnn l 2x3 these are your lptf parameters lptf optional arguments l int the total number of atoms in your lattice nh int transformer token size input patches are projected to match the token size note when using an rnn subsampler this nh must match the rnn s nh patch int number of atoms input predicted at once patch size the input sequence will have an effective length of l patch dropout float the amount of dropout to use in the transformer layers num layers int the number of transformer layers to use nhead int the number of heads to use in multi headed self attention this should divide nh subsampler sampler the inner model to use for probability factorization this is set implicitly by including rnn or ptf arguments rydberg hamiltonian the following parameters can be chosen for the rydberg hamiltonian lx 4 ly 4 v 7 0 omega 1 0 delta 1 0 | ai |
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salient | salient salient is a natural language processing and machine learning toolkit salient contains many common tasks from sentiment analysis part of speech tagging tokenization neural networks regression analysis wiktionary parsing logistic regression language modeling mphf radix trees vocabulary building and the potential for more awesomeness it can be used for many classification tasks categorization and many common text processing tasks all in node js d npm install salient features sentiment analysis includes negation semantics amplifiers sarcasm detection various machine learning algorithms with regularization cross validation learning curves normalization neural networks feedforward n layers with y units logistic regression linear regression language modeling minimal perfect hashing gaddag radix trees tries hidden markov models tri gram hmm part of speech tagger wiktionary parsing tokenization tweets urls word puncutation regexp emoticons wiki articles html corpus parsing building brown corpus parsing analyzer penn treebank corpus parsing analyzer twitter treebank corpus parsing analyzer iula spanish lsp treebank parsing analyzer universal part of speech tagset mappings tree tagger part of speech tagger text analyzer concepts relations concept relationship concept filtering stop words tagging corpus coverage testing vocabulary merging sentiment terms web crawling document search backed by redis tokenization there are plenty of libraries that perform tokenization this library is no different the only exception is that this library will also do some tokenization steps necessary to cleanup random html xml wiki twitter and other sources more examples are in the specs directory tokenizers in salient are built on top of each other and include the following tokenizer lib salient tokenizers tokenizer js abstract regexptokenizer lib salient tokenizers regexp tokenizer js extends tokenizer var salient require salient var tokenizer new salient tokenizers regexptokenizer pattern w tokenizer tokenize these are things these are things urltokenizer lib salient tokenizers url tokenizer js extends tokenizer leverages twitter text https github com twitter twitter text var salient require salient var tokenizer new salient tokenizers urltokenizer tokenizer tokenize some text a http en wikipedia org wiki liverpool f c disambiguation wikipedia url in it http en wikipedia org wiki liverpool f c disambiguation wordpuncttokenizer lib salient tokenizers wordpunct tokenizer js extends regexptokenizer handles time numerics including 1st 2nd etc numerics with commas decimals percents words with hyphenations words with and without accepts with and without apostrophes punctuations and optional emoticon preservation var salient require salient var tokenizer new salient tokenizers wordpuncttokenizer tokenizer tokenize from 12 00 am 11 59 pm on nov 12th you can make donation online to support wylie center from 12 00 am 11 59 pm on nov 12th you can make donation online to support wylie center preserve emoticons tokenizer new salient tokenizers wordpuncttokenizer preserveemoticons true tokenizer tokenize data here to clean wouldn t you say so d i have 20 45 are you ok data here to clean wouldn t you say so d i have 20 45 are you ok emoticontokenizer lib salient tokenizers emoticon tokenizer js extends regexptokenizer handles basic emoticons pattern shown in the given file var salient require salient var tokenizer new salient tokenizers emoticontokenizer tokenizer tokenize rt sampleusername this is typical articletokenizer lib salient tokenizers article tokenizer js extends tokenizer includes urltokenizer wordpuncttokenizer regexptokenizer leverages underscore http underscorejs org var salient require salient var tokenizer new salient tokenizers articletokenizer compresswhitespace true var text in 1927 working with this diagrammatic form of knots j w alexander and g b briggs and independently kurt reidemeister demonstrated that two knot diagrams belonging to the same knot can be related by a sequence of three kinds of moves on the diagram shown below these operations now called the reidemeister moves are ol style list style type upper roman r n li twist and untwist in either direction li r n li move one strand completely over another li r n li move a strand completely over or under a crossing li ol align center style text align center reidemeister moves style padding 1em image reidemeister move 1 png 130px file frame left png image reidemeister move 2 png 210px u type i u u type ii u style padding 1em colspan 2 image reidemeister move 3 png 360px colspan 2 u type iii u tokenizer clean text in 1927 working with this diagrammatic form of knots j w alexander and g b briggs and independently kurt reidemeister demonstrated that two knot diagrams belonging to the same knot can be related by a sequence of three kinds of moves on the diagram shown below these operations now called the reidemeister moves are twist and untwist in either direction move one strand completely over another move a strand completely over or under a crossing tweettokenizer lib salient tokenizers tweet tokenizer js extends articletokenizer leverages underscore http underscorejs org twitter text https github com twitter twitter text var salient require salient var tokenizer new salient tokenizers tweettokenizer tokenizer tokenize rt sampleusername this is typical rt sampleusername this is typical part of speech tagging part of speech tagging is done primarily through the use of the trigram hidden markov model while there are many methods used since then trigram hmm seems to be the easiest to implement while maintaining an effective accuracy this was built through the use of several resources online including bootstrapping the vocabulary using wiktionary https www wiktionary org this is a common alternative technique to the unsupervised learning technique by providing a bit of an edge to the model with an existing dictionary of sorts in some cases the dictionary can be generated from a part of speech corpus sometimes manually or automatically tagged on top of wiktionary i am using several corpus to build the english language model including brown corpus penn treebank twitter treebank these treebanks provide a resource for calculating and training the model for supervised learning cases the actually tagging portion is done using the viterbi path finding algorithm implemented for all standard models the spanish model is trained using the iula spanish lsp treebank you will notice both models are stored in the bin https github com nyxtom salient tree master bin directory more information is provided on the building of this model in trigram hidden markov model part of speech tagger https github com nyxtom salient blob master hmm trigram md var salient require salient var hmmtagger new salient tagging hmmtagger hmm tag how are you doing today the weather looks beautiful today adv verb pron verb noun det noun verb adj noun glossary glossary is sometimes used for looking up concepts terms or relationships between terms this is far from perfect but it gives a good usecase for some information retrieval you may find other libraries do this better but i m currently using this part of the library to build towards sentiment analysis use cases glossary has things that i found useful when building the sentiment analysis portion which include catalogging things like copulae verbs linking verbs terms that are often filtered i e stop terms question terms time sensitive nouns amplifiers clauses coordinating conjunctions negations conditionals ors and contractions all these proved very useful in the sentiment analysis stage where the particular algorithm i implemented is described in more detail below as you can see in the code sample below i have done a bit of chunking for terms and with some common filtering rules i can combine filtered terms determiners with their nouns etc additionally the output of glossary helps me debug when something gets parsed oddly nevertheless with this utility i can follow the flow of logic in a given sentence and make it easier to look at relationships between terms var salient require salient var glossary new salient glossary glossary glossary parse this is going to be an awesome test glossary tojson term this is going to be distinct is going to be tag verb children term is tag verb position 1 isverb true isqterm true iscop true islink true isfiltered true term going tag verb position 2 isverb true term to tag prt position 3 isprt true isto true isfiltered true term be tag verb position 4 isverb true isfiltered true beginsdet true isverb true orig term this distinct this tag det isdet true isfiltered true termmap this is going to be term an awesome test tag noun position 5 children term awesome tag adj position 6 isadj true isadjcard true term test tag noun position 7 isnoun true iscop true beginsdet true isnoun true orig term an tag det position 5 isdet true isfiltered true termmap an awesome test distinct awesome test the flow of logic is further helped when i include negations of any sort show below the inclusion of a negation breaks the flow of our logic and instead negates the rest of our sentence this is explored further in the sentiment analysis stage of the library var salient require salient var glossary new salient glossary glossary glossary parse this is never going to be an awesome test glossary tojson term this is distinct is tag verb children term is tag verb position 1 isverb true isqterm true iscop true islink true isfiltered true beginsdet true isverb true isfiltered true orig term this distinct this tag det isdet true isfiltered true termmap this is term never tag adv position 2 isadv true isneg true term going to be tag verb position 3 children term to tag prt position 4 isprt true isto true isfiltered true term be tag verb position 5 isverb true isfiltered true isverb true islink true orig term going tag verb position 3 isverb true termmap going to be term an awesome test tag noun position 6 children term awesome tag adj position 7 isadj true isadjcard true term test tag noun position 8 isnoun true iscop true beginsdet true isnoun true orig term an tag det position 6 isdet true isfiltered true termmap an awesome test distinct awesome test you can additionally use the output of the glossary parse function to retreive a simple concepts map this effectively is looking for noun terms within the parsed output of the glossary so the above example would look like so var salient require salient var glossary new salient glossary glossary glossary parse this is never going to be an awesome test glossary concepts awesome test sentiment analysis the approach i took to sentiment analysis builds on top of a rather simple naive bayes approach the naive bayes approach is such that we have a set of buckets which are classified between varying degrees of positive and negative and neutral the terms in each category should be up to n gram terms the sentiment algorithm combines the use of amplifiers terms that tend to amplify or show additional excitement on an existing term i e crazy good it makes use of most of the features obtained from the glossary above including negations the analyzer will look for scorable terms that our bayes buckets specify it will look for lcs longest common substring and determine whether the terms are even scorable due to varying rules due to filtering once we ve effectively scored all the possible terms in our text we can go through the text once again in a node by node sequence using a finite state machine to detect things like conditionals negations semantic clauses amplifiers inclusions and or final orientation terms such as hashtags which may negate the entire text common in the case of sarcastic tweets finally once we ve determined the final polarity of our text we give it a cumulative score this process also identifies semantic orientation on a per term basis which means we can go back and actually see the orientation of individual terms var salient require salient var analyser new salient sentiment bayessentimentanalyser analyser classify that product will never be good at disappointing their users 1 5 analyser classify i love how wall street screwed things up 1 5 analyser classify i m dying to have a snickers bar 1 analyser classify i am loving the 10 second page loads on this site not 2 while the above test cases are cool it doesn t mean this is some super magical system that can get it right all the time language is complex and finicky and sentiment analysis requires inductive reasoning along with a load of other ai problems however that being said you should see a reasonable amount of cases are shown in bin tests https github com nyxtom salient tree master bin tests notes it should be noted that most machine learning algorithms would be better suited in environments that can take advantage of many cores such as in the case of gpu accelerated machine learning such things are necessary in order to speed up the learning rate as well as the task at hand given that many complex linear algebra operations can be done efficiently in parallel as a result this project is more of an example test case implementation for a wide variety of machine learning and artificial intelligence problems for more robust implementations it is recommended that you glean from my implementations and others i e by reference to andrew ng s machine learning course and use that within the scope of your projects however there are other techniques such as map reduce that may be able to improve the performance of running some of these operations within this package on multiple cores and multiple systems in parallel license licensed under gplv2 copyright c 2015 thomas holloway nyxtom | ai |
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windows-drivers | raspberry pi board support package for windows 10 welcome to the raspberry pi board support package bsp for windows 10 this repository contains bsp components for the raspberry pi 2 3 and compute modules running 32 bit windows 10 iot core version 1809 it also contains bsp components for the raspberry pi 2 v1 2 3 4 and corresponding compute modules running 64 bit windows 10 this bsp repository is under community support it is not actively maintained by microsoft for raspberry pi 4 onwards it s designed for use with windows 10 version 2004 and later because of use of newer driver frameworks which are not available on earlier releases 64 bit firmware for the raspberry pi 4 firmware from the pi firmware task force https rpi4 uefi dev is used which provides uefi and acpi support it is available at https github com pftf rpi4 releases for the raspberry pi 2 v1 2 and raspberry pi 3 the pi firmware task force provides firmware at https github com pftf rpi3 releases which provides uefi and acpi support 32 bit firmware sample binaries of the firmware is included in rpi bootfirmware bspfiles packages rpi bootfirmware to enable quick prototyping the sources for these binaries are listed below 1 firmware binaries raspberrypi firmware https github com raspberrypi firmware 2 uefi sources rpi uefi https github com ms iot rpi uefi 32 bit efi customisations note this section is only applicable to 32 bit windows 10 iot core on raspberry pi 3 and earlier smbios requirements of windows 10 iot core oem licensing https docs microsoft com en us windows hardware manufacture iot license requirements smbios support requires a custom version of kernel img file with the proper smbios values see platformsmbiosdxe c https github com ms iot rpi uefi blob ms iot pi3boardpkg drivers platformsmbiosdxe platformsmbiosdxe c to update the smbios data steps to build the kernel img is provided in the rpi uefi github https github com ms iot rpi uefi build the drivers 1 clone https github com raspberrypi windows drivers 1 open visual studio with administrator privileges 1 in visual studio file open project solution select windows drivers build bcm2836 buildbcm2836 sln 1 set your build configuration release or debug 1 build build solution the resulting driver binaries will be located in the windows drivers build bcm2836 arm output folder or windows drivers build bcm2836 arm64 output if you picked an arm 64 bit configuration export the bsp note this section is only applicable to windows 10 iot core we provide a binexport ps1 script to scrape the bsp components together into a zip file for easy use with the iot adk addonkit 1 open powershell 2 navigate to rpi iotcore tools 3 run binexport ps1 with the appropriate arguments powershell binexport ps1 c release or binexport ps1 c release isdebug for debug binaries 4 the script will generate a zip file rpi bsp xx zip that can be imported into the iot adk addonkit shell using import iotbsp https github com ms iot iot adk addonkit blob master tools iotcoreimaging docs import iotbsp md powershell import iotbsp rpi c temp rpi bsp xx zip | server |
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Embedded | embedded embedded systems design projects coded for texas instruments tm4c123gh6pmi arm cortex m4 processor on the tiva c launchpad board | os |
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ptor | design the ptor tool is designed to validate the rpo rto of a postgresql ha instance by using this tool we can put some load into the primary instance which will get replicated to it s secondary while the data loading happens we can either turn off the primary or it s vm instance to trigger the underlying auto failover system once the auto failover happens ptor tool will evaluate the rpo data loss and rto recovery time of the ha system to use ptor tool we need below two instances 1 primary pgdsn which points to the primary endpoint 2 repo pgdsn which makes a copy of the transactions which parallel workers execute on the primary pgdsn we need this repo pgdsn instance to validate the data after we trigger the failover switchover ptor png quick test local quick test performed between the two local instance where primary and repo instances are in sync streaming replication mode in this demo we restarted the local primary instance to mimic the failover switchover asciicast https asciinema org a 2mrlvcml2cm7v4ewctm9rj0yf svg https asciinema org a 2mrlvcml2cm7v4ewctm9rj0yf other demos all the demos are done with a sync streaming replication between the primary and secondary nodes all the instances are configured to be in the same network pg auto failover configure primary pgdsn as a multi host connection string as like below host host1 host2 port 5432 5432 user postgres password password target session attrs read write demo ptor pg auto failover https youtu be 0vhxn0hbwu stolon configure primary pgdsn as to point the stolon proxy demo ptor stolon https youtu be sdrii00hnbm patroni configure primary pgdsn as to point the haproxy below is the haproxy cfg used for this demo global maxconn 100 defaults log global mode tcp retries 1 timeout client 30m timeout connect 1s timeout server 30m timeout check 1s listen stats mode http bind 2361 stats enable stats uri listen production bind 172 31 46 52 2360 option httpchk options master http check expect status 200 default server inter 1s fall 1 rise 1 on marked down shutdown sessions server postgresql 192 168 56 104 5432 172 31 34 9 5432 maxconn 100 check port 8008 server postgresql 192 168 56 105 5432 172 31 43 176 5432 maxconn 100 check port 8008 demo ptor patroni haproxy https youtu be nodmljx8 q0 installation below are the installation steps which are prepared on rhel instance if you are using debain flavour then use the platform specific package tools like apt get or brew to install the below components 1 install git sudo yum install git y 2 install golang sudo yum install golang y 3 install postgresql server optional sudo yum install postgresql server y this is for the repo server where we save a copy of primary transactions 4 download the copy of ptor source git clone https github com dineshkumar02 ptor git 5 build the ptor binary cd ptor make usage option usage repo pgdsn the repo postgresql connection string where it syncs primary data primary pgdsn the primary or service postgresql connection string where we run switchover failover parallel workers number of parallel workers to run data loading it will create these many individual tables init initialize the paralle workers tables reset delete all data from repo and primary instances warmup duration initial data loading duration in seconds insert percent percentage number of insert operations update percent percentage number of update operations delete percent percentage number of delete operations full data validation run full data validation on both repo and primary in the end of the test case async repo mode all primary events will get in sync to repo asynchronously this improves more data generation on the primary side rto conn timeout primary dns connection timeout value this helps in calculating the rto check primary latency check network connectivity latency between ptor and primary dns validation delay delay the data validation these many seconds this value is going to be added to the rto | os |
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llm-seminar | comp790 101 large language models instructor colin raffel http colinraffel com meeting time mondays and wednesdays 1 25 2 40pm classroom sn 011 office hours by appointment language models which are trained to predict text given other text underly many recent successes in natural language processing and artificial intelligence whether used for transfer learning using language modeling as a pre training objective before subsequent fine tuning on a downstream task or prompting formulating an input sequence that induces a model to perform a desired task without any training language modeling has proven to be an effective way of imbuing models with useful capabailities these capabilities have been observed to consistently improve as the size of the language model increases which has led to a focus on developing ever larger language models in this course we will survey the history of language model scaling as well as recent advances in building analyzing and using large lms the course will use a role playing seminar format https colinraffel com blog role playing seminar html described in more detail below prerequisites students must have experience with machine learning preferably deep learning and the basics of modern natural language processing before taking the class you should be able to read a recent machine learning or natural language processing conference paper and come away with a decent understanding of the basic concepts and ideas proposed in the paper but not necessarily a deep perfect understanding of every last detail course structure this class will use a role playing seminar https colinraffel com blog role playing seminar html format where students take on different roles and present papers to one another all grading will be based on these presentations and course participation there will be final project or other coursework readings each class will involve the presentation and discussion of two papers the pair of papers covered in each class session are meant to complement each other e g because one paper might be the historical precedent of the other or the papers were contempraneous or they present different viewpoints on the same topic before each class everyone is required to have read both papers students will be divided into four groups two groups will present on mondays and the other two groups will present on wednesdays in a given class session students in the presenting groups will each be given a rotating role described below this role defines the lens through which they read the paper and determines what they prepare for the in class discussion students in the non presenting groups are also required to read the papers complete a quick exercise described below and come to class ready to discuss all students will obtain a thorough understanding of the chosen papers and will develop their paper reading literature review and prototyping skills presentation roles this seminar is organized around the different roles students play each week reviewer archaeologist researcher hacker diagrammer and possibly blogger reviewer complete a full critical but not necessarily negative review of the paper follow the guidelines for neurips reviewers https neurips cc conferences 2022 reviewerguidelines under review form please complete the strengths and weaknesses and questions sections and assign an overall score you can skip the rest of the review including writing a summary since all students should have read the paper archaeologist determine where this paper sits in the context of previous and subsequent work find and report on one prior paper that we are not reading in this class that substantially influenced the current paper or one newer paper that we are not reading in this class that was heavily influenced by the current paper hacker implement a small part of the paper on a small dataset or toy problem prepare to share the core code of the algorithm to the class do not simply download and run an existing implementation you should implement at least a toy version of a method from the paper though you are welcome to use and give credit to an existing implementation for backbone code e g model building data loading training loop etc diagrammer create a diagram of one of the concepts or ideas from the paper or re make one of the plots in the paper to make it clearer please pick something that hasn t already been diagrammed from a previous paper blogger write a paragraph each about the two papers and an additional paragraph comparing and contrasting them the summary of each paper should cover the motivation behind the paper a description of any of the proposed methods and an overview of the key findings you should write a bit about how they are different and or build on one another the blogger will not present during the class session non presenter assignment if you aren t in the presenting group during a given class period please submit before class 1 a new title for either one of the papers and or a new name for an algorithm proposed in either paper 1 at least one question about either paper could be something you re confused about or something you d like to hear discussed more schedule the schedule below includes a preliminary list of the papers we will be reading these papers are subject to change though i will try to make changes only to papers that are at least two weeks away if you have any suggested changes feel free to tell me date group a paper group b paper mon 8 15 class introduction background logistics n a wed 8 17 attention is all you need https arxiv org abs 1706 03762 colin presents n a mon 8 22 a neural probabilistic language model https www jmlr org papers volume3 bengio03a bengio03a pdf the unreasonable effectiveness of recurrent neural networks http karpathy github io 2015 05 21 rnn effectiveness wed 8 24 generating sequences with recurrent neural networks https arxiv org abs 1308 0850 exploring the limits of language modeling https arxiv org abs 1602 02410 mon 8 29 semi supervised sequence learning https arxiv org abs 1511 01432 learning to generate reviews and discovering sentiment https arxiv org abs 1704 01444 wed 8 31 universal language model fine tuning for text classification https arxiv org abs 1801 06146 improving language understanding by generative pre training https s3 us west 2 amazonaws com openai assets research covers language unsupervised language understanding paper pdf mon 9 5 no class labor day n a wed 9 7 bert pre training of deep bidirectional transformers for language understanding https arxiv org abs 1810 04805 roberta a robustly optimized bert pretraining approach https arxiv org abs 1907 11692 mon 9 12 electra pre training text encoders as discriminators rather than generators https arxiv org abs 2003 10555 deberta decoding enhanced bert with disentangled attention https arxiv org abs 2006 03654 wed 9 14 exploring the limits of transfer learning with a unified text to text transformer https arxiv org abs 1910 10683 unified language model pre training for natural language understanding and generation https arxiv org abs 1905 03197 mon 9 19 cross lingual language model pretraining https arxiv org abs 1901 07291 byt5 towards a token free future with pre trained byte to byte models https arxiv org abs 2105 13626 wed 9 21 language models are unsupervised multitask learners https d4mucfpksywv cloudfront net better language models language models are unsupervised multitask learners pdf language models are few shot learners https arxiv org abs 2005 14165 mon 9 26 no class well being day n a wed 9 28 scaling language models methods analysis insights from training gopher https arxiv org abs 2112 11446 palm scaling language modeling with pathways https arxiv org abs 2204 02311 mon 10 3 what changes can large scale language models bring intensive study on hyperclova billions scale korean generative pretrained transformers https arxiv org abs 2109 04650 ernie 3 0 titan exploring larger scale knowledge enhanced pre training for language understanding and generation https arxiv org abs 2112 12731 wed 10 5 scaling laws for neural language models https arxiv org abs 2001 08361 training compute optimal large language models https arxiv org abs 2203 15556 mon 10 10 on the dangers of stochastic parrots can language models be too big https dl acm org doi 10 1145 3442188 3445922 release strategies and the social impacts of language models https arxiv org abs 1908 09203 wed 10 12 realtoxicityprompts evaluating neural toxic degeneration in language models https arxiv org abs 2009 11462 truthfulqa measuring how models mimic human falsehoods https arxiv org abs 2109 07958 mon 10 17 the pile an 800gb dataset of diverse text for language modeling https arxiv org abs 2101 00027 documenting large webtext corpora a case study on the colossal clean crawled corpus https arxiv org abs 2104 08758 wed 10 19 extracting training data from large language models https arxiv org abs 2012 07805 deduplicating training data makes language models better https arxiv org abs 2107 06499 mon 10 24 language models as knowledge bases https arxiv org abs 1909 01066 realm retrieval augmented language model pre training https arxiv org abs 2002 08909 wed 10 26 exploiting cloze questions for few shot text classification and natural language inference https arxiv org abs 2001 07676 the power of scale for parameter efficient prompt tuning https arxiv org abs 2104 08691 mon 10 31 multitask prompted training enables zero shot task generalization https arxiv org abs 2110 08207 benchmarking generalization via in context instructions on 1 600 language tasks https arxiv org abs 2204 07705 wed 11 2 exploring and predicting transferability across nlp tasks https arxiv org abs 2005 00770 don t stop pretraining adapt language models to domains and tasks https arxiv org abs 2004 10964 mon 11 7 training language models to follow instructions with human feedback https arxiv org abs 2203 02155 training a helpful and harmless assistant with reinforcement learning from human feedback https arxiv org abs 2204 05862 wed 11 9 how many data points is a prompt worth https arxiv org abs 2103 08493 do prompt based models really understand the meaning of their prompts https arxiv org abs 2109 01247 mon 11 14 calibrate before use improving few shot performance of language models https arxiv org abs 2102 09690 rethinking the role of demonstrations what makes in context learning work https arxiv org abs 2202 12837 wed 11 16 outrageously large neural networks the sparsely gated mixture of experts layer https arxiv org abs 1701 06538 switch transformers scaling to trillion parameter models with simple and efficient sparsity https arxiv org abs 2101 03961 mon 11 21 learning transferable visual models from natural language supervision https arxiv org abs 2103 00020 flamingo a visual language model for few shot learning https storage googleapis com deepmind media deepmind com blog tackling multiple tasks with a single visual language model flamingo pdf wed 11 23 no class thanksgiving break n a mon 11 28 efficient large scale language model training on gpu clusters using megatron lm https arxiv org abs 2104 04473 deepspeed extreme scale model training for everyone https www microsoft com en us research blog deepspeed extreme scale model training for everyone wed 11 30 holistic evaluation of language models https arxiv org abs 2211 09110 beyond the imitation game quantifying and extrapolating the capabilities of language models https arxiv org abs 2206 04615 sat 12 3 gpt neox 20b an open source autoregressive language model https arxiv org abs 2204 06745 bloom a 176b parameter open access multilingual language model https arxiv org abs 2211 05100 grading 1 presentations 84 points for each class session where you are presenting you will be graded out of 6 points you will receive full credit if you do a thorough job of undertaking your role and present it in a clear and compelling way 1 participation 14 points for each class session where you aren t presenting you ll be given up 1 point for completing the non presenter assignment and attending and participating in class 1 free points 2 points all students get 2 free points attendance late work and the honor code if you miss a class without completing the corresponding assignment you ll get a zero for that session if you miss a class where you are in a presenting role for that session you must still create the presentation for that role before the class and you must find someone else to present it for you if you miss a class where you d be in a non presenting role to get credit for that session you need to complete the non presenting assignment and send it to me before the start of class there s really no way to accept late work for the readings since it s vital that we re all reading the same papers at the same time all students are expected to follow the guidelines of the unc honor code http honor unc edu in the context of this class it is particularly important that you cite the source of different ideas facts or methods and do not claim someone else s work as your own if you are unsure about which actions violate that honor code or consult studentconduct unc edu conduct i ask that we all follow the neurips code of conduct https nips cc public codeofconduct and the recurse center social rules https www recurse com social rules since this is a discussion class it s especially important that we respect everyone s perspective and input in particular i value the perspectives of individuals from all backgrounds reflecting the diversity of our students i broadly define diversity to include race gender identity national origin ethnicity religion social class age sexual orientation political background and physical and learning ability i will strive to make this classroom an inclusive space for all students please let me know if there is anything i can do to improve acts of discrimination harassment interpersonal relationship violence sexual violence sexual exploitation stalking and related retaliation are prohibited at unc chapel hill if you have experienced these types of conduct you are encouraged to report the incident and seek resources on campus or in the community please contact the director of title ix compliance title ix coordinator adrienne allison adrienne allison unc edu report and response coordinators ew quimbaya winship eqw unc edu rebecca gibson rmgibson unc edu kathryn winn kmwinn unc edu counseling and psychological services caps confidential in campus health services at 919 966 3658 or the gender violence services coordinators confidential cassidy johnson cassidyjohnson unc edu holly lovern holly lovern unc edu to discuss your specific needs additional resources are available at http safe unc edu resources the university of north carolina at chapel hill facilitates the implementation of reasonable accommodations including resources and services for students with disabilities chronic medical conditions a temporary disability or pregnancy complications resulting in barriers to fully accessing university courses programs and activities accommodations are determined through the office of accessibility resources and service ars for individuals with documented qualifying disabilities in accordance with applicable state and federal laws see the ars website for contact information https ars unc edu or email ars unc edu unc chapel hill is strongly committed to addressing the mental health needs of a diverse student body the heels care network website https care unc edu is a place to access the many mental resources at carolina caps is the primary mental health provider for students offering timely access to consultation and connection to clinically appropriate services go to their website https caps unc edu or visit their facilities on the third floor of the campus health building for an initial evaluation to learn more any student who is impacted by discrimination harassment interpersonal relationship violence sexual violence sexual exploitation or stalking is encouraged to seek resources on campus or in the community reports can be made online to the eoc at https eoc unc edu report an incident please contact the university s title ix coordinator elizabeth hall interim titleixcoordinator unc edu report and response coordinators in the equal opportunity and compliance office reportandresponse unc edu counseling and psychological services confidential or the gender violence services coordinators gvsc unc edu confidential to discuss your specific needs additional resources are available at safe unc edu changes the professor reserves the right to make changes to the syllabus including project due dates and test dates these changes will be announced as early as possible | ai |
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SparkifyDB | h4 sparkify data modelling h4 p sparkify is collecting data in json format however they need a database to be able to run analytics reports on users and artists and songs played p h4 fact table h4 p the fact table consist of songplays values included in this table are as follow p blockquote songplay id start time user id level song id artist id session id location user agent blockquote h4 dimension table h4 p the dimension tables include data about artist song time and user each table consist of the folowing p blockquote artist information artist id name location latitude longitude blockquote blockquote song information song id title artist id year duration blockquote blockquote timestmap of songs being played start time hour day week month year weekday blockquote blockquote user that played the song user id first name last name gender level blockquote h4 why star schema h4 since quering the database and it being performant is the most important measure for spakify the star schema is the best choice here h4 query example h4 blockquote sql select from songplays limit 10 blockquote h4 files h4 p strong create table py strong is the code to create tables p p strong sql queries py strong creates the tables and contains the insert statments for database p p strong etl py strong creates the etl pipeline connects to database and writes json data into database p p strong etl ipynb strong notebook of etl this file is not necessary to run the program p p strong test ipynb strong connects to db and test if the data has been written correctly to db p h4 how to run the code h4 on your terminal please run the following blockquote python create table py br python etl py blockquote h4 enviroment h4 python 3 6 3 br psql postgresql 9 5 23 br psycopg2 2 7 4 h4 sources h4 http gkmc utah edu ebis class 2003s oracle dmb26 a73318 schemas htm br https www dataquest io blog loading data into postgres br https gist github com gwangjinkim f13bf596fefa7db7d31c22efd1627c7a br https github com san089 udacity data engineering projects blob master data modeling with postgres readme md br | server |
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Documents | documents github stars https img shields io github stars coderserdar documents style social github forks https img shields io github forks coderserdar documents style social github watchers https img shields io github watchers coderserdar documents style social github repo size https img shields io github repo size coderserdar documents style plastic github language count https img shields io github languages count coderserdar documents style plastic github top language https img shields io github languages top coderserdar documents style plastic github last commit https img shields io github last commit coderserdar documents color red style plastic github issues https img shields io github issues coderserdar documents this repo includes some kind of documentation files about information technologies you can find free e books and whitepapers about programming languages rdbms relational data base management systems mobile development web development data structures and algorithms clean code code quality analysis data science machine learning interview software testing orm object relational mapping tools network operating systems cyber security hacking non categorized technologies important note i remove the books which are in turkish from this repo if you want to access them you can lookup to turkcekaynaklar https github com coderserdar turkcekaynaklar repo address there are sub section in this repository like you can look at documents site map programming languages https github com coderserdar documents tree main programming 20languages delphi https github com coderserdar documents tree main programming 20languages delphi delphi 27th webinar free e books https github com coderserdar documents tree main programming 20languages delphi delphi 2027th 20webinar embarcadero codecentral free e books https github com coderserdar documents tree main programming 20languages delphi embarcadero 20codecentral embarcadero whitepapers https github com coderserdar documents tree main programming 20languages delphi embarcadero 20whitepapers other books https github com coderserdar documents tree main programming 20languages delphi other 20books books about rad studio https github com coderserdar documents tree main programming 20languages delphi rad 20studio developer direct mobile school 2014 https github com coderserdar documents tree main programming 20languages delphi rad 20studio developer 20direct 20mobile 20school 202014 windows and mac development coursebook https github com coderserdar documents tree main programming 20languages delphi rad 20studio windows 20and 20mac 20development 20coursebook books in theswissbay ch https github com coderserdar documents tree main programming 20languages delphi the 20swiss 20bay datasnap https github com coderserdar documents tree main programming 20languages delphi datasnap lazarus https github com coderserdar documents tree main programming 20languages lazarus assembly https github com coderserdar documents tree main programming 20languages assembly c https github com coderserdar documents tree main programming 20languages c c https github com coderserdar documents tree main programming 20languages c c https github com coderserdar documents tree main programming 20languages c 20sharp go https github com coderserdar documents tree main programming 20languages go java https github com coderserdar documents tree main programming 20languages java php https github com coderserdar documents tree main programming 20languages php python https github com coderserdar documents tree main programming 20languages python ruby https github com coderserdar documents tree main programming 20languages ruby visual basic https github com coderserdar documents tree main programming 20languages visual 20basic other haskell matlab perl etc https github com coderserdar documents tree main programming 20languages other interview some helpful documents before it job interviews https github com coderserdar documents tree main interview cyber security https github com coderserdar documents tree main interview cyber 20security data science https github com coderserdar documents tree main interview data 20science general https github com coderserdar documents tree main interview general machine learning https github com coderserdar documents tree main interview machine 20learning programming languages https github com coderserdar documents tree main interview programming 20languages sql https github com coderserdar documents tree main interview sql programming methodologies https github com coderserdar documents tree main programming 20methodologies ci cd continuous integration continuous development https github com coderserdar documents tree main programming 20methodologies ci cd jenkins https github com coderserdar documents tree main programming 20methodologies ci cd jenkins devops https github com coderserdar documents tree main programming 20methodologies devops git https github com coderserdar documents tree main programming 20methodologies git other non categorized books https github com coderserdar documents tree main programming 20methodologies other orm tools https github com coderserdar documents tree main orm 20 object 20relational 20mapping dapper https github com coderserdar documents tree main orm 20 object 20relational 20mapping dapper entity framework https github com coderserdar documents tree main orm 20 object 20relational 20mapping entity 20framework llblgen https github com coderserdar documents tree main orm 20 object 20relational 20mapping llblgen nhibernate https github com coderserdar documents tree main orm 20 object 20relational 20mapping nhibernate database https github com coderserdar documents tree main database general https github com coderserdar documents tree main database general interbase https github com coderserdar documents tree main database interbase microsoft sql server https github com coderserdar documents tree main database microsoft 20sql 20server mongodb https github com coderserdar documents tree main database mongodb mysql https github com coderserdar documents tree main database mysql oracle https github com coderserdar documents tree main database oracle postgresql https github com coderserdar documents tree main database postgresql mobile development https github com coderserdar documents tree main mobile 20development android https github com coderserdar documents tree main mobile 20development android flutter https github com coderserdar documents tree main mobile 20development flutter ios https github com coderserdar documents tree main mobile 20development ios kotlin https github com coderserdar documents tree main mobile 20development kotlin unity https github com coderserdar documents tree main mobile 20development unity other react native xamarin etc https github com coderserdar documents tree main mobile 20development other operating systems https github com coderserdar documents tree main operating 20systems general https github com coderserdar documents tree main operating 20systems general linux https github com coderserdar documents tree main operating 20systems linux windows https github com coderserdar documents tree main operating 20systems windows freebsd https github com coderserdar documents tree main operating 20systems freebsd articles https github com coderserdar documents tree main operating 20systems freebsd articles books https github com coderserdar documents tree main operating 20systems freebsd books web development https github com coderserdar documents tree main web 20development asp net core https github com coderserdar documents tree main web 20development asp 20 net 20core amazon web services https github com coderserdar documents tree main web 20development aws azure https github com coderserdar documents tree main web 20development azure docker https github com coderserdar documents tree main web 20development docker google cloud https github com coderserdar documents tree main web 20development google 20cloud html5 https github com coderserdar documents tree main web 20development html5 javascript https github com coderserdar documents tree main web 20development javascript js frameworks angular node react js etc https github com coderserdar documents tree main web 20development js 20frameworks kubernetes https github com coderserdar documents tree main web 20development kubernetes other non categorized books https github com coderserdar documents tree main web 20development other data structures algorithms https github com coderserdar documents tree main data 20structures 20 26 20algorithms clean code https github com coderserdar documents tree main clean 20code code quality https github com coderserdar documents tree main code 20quality analysis https github com coderserdar documents tree main analysis data science https github com coderserdar documents tree main data 20science project management https github com coderserdar documents tree main project 20management network https github com coderserdar documents tree main network testing https github com coderserdar documents tree main testing cyber security hacking https github com coderserdar documents tree main cyber 20security 20 26 20hacking machine learning https github com coderserdar documents tree main machine 20learning other https github com coderserdar documents tree main other excel https github com coderserdar documents tree main other excel apache kafka https github com coderserdar documents tree main other apache 20kafka statistics https github com coderserdar documents tree main other statistics power bi https github com coderserdar documents tree main other power 20bi and if you code with c type languages and interested in pascal language you should look guvacode https github com guvacode s pascal for c users https github com guvacode pascal for c users guide | delphi e-book lazarus free-pascal object-pascal algorithm algorithms cplusplus cpp17 data-structures rad-studio rad-studio-ide android flutter kotlin-android cyber-security cybersecurity database operating-system llblgen | server |
cloud-conversion-tool-formatter-api | description formatter is an api project in python with flask to formatter files and convert to another format this is a project for cloud software engineering made with python https img shields io badge python 2b5b84 style for the badge logo python logocolor white labelcolor 000000 flask https img shields io badge flask 000000 style for the badge logo flask logocolor white labelcolor 000000 | cloud |
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natural-language-processing-recipes | apress source code this repository accompanies natural language processing recipes https www apress com 9781484242667 by akshay kulkarni and adarsha shivananda apress 2019 comment cover cover image 9781484242667 jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates contributions see the file contributing md for more information on how you can contribute to this repository | ai |
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ml-workshop-3-of-4 | advanced machine learning with scikit learn pipelines and evaluation metrics part 3 of 4 other parts part 1 https github com amueller ml workshop 1 of 4 part 2 https github com amueller ml workshop 2 of 4 part 4 https github com amueller ml workshop 4 of 4 content review of scikit learn api https amueller github io ml workshop 3 of 4 slides 01 reminder sklearn api html gradient boosted trees https amueller github io ml workshop 3 of 4 slides 02 gradient boosting building workflows with pipelines https amueller github io ml workshop 3 of 4 slides 03 pipelines html evaluation metrics for classification https amueller github io ml workshop 3 of 4 slides 04 model evaluation html instructor andreas mueller http amuller github io amuellerml https twitter com amuellerml columbia university book introduction to machine learning with python http shop oreilly com product 0636920030515 do this repository will contain the teaching material and other info associated with the workshop advanced advanced machine learning with scikit learn part i ii please download the large movie review dataset from http ai stanford edu amaas data sentiment before coming to the workshop about the workshop scikit learn is a machine learning library in python that has become a valuable tool for many data science practitioners this training will cover some of the more advanced aspects of scikit learn such as building complex machine learning pipelines and advanced model evaluation model evaluation is an underappreciated aspect of machine learning but using the right metric to measure success is critical practitioners are often faced with imbalanced classification tasks where accuracy can be uninformative or misleading we will discuss other metrics when to use them and how to compute them with scikit learn we will also look into how to build processing pipelines using scikit learn to chain multiple preprocessing techniques together with supervised models and how to tune complex pipelines prerequisites this workshop assumes familiarity with jupyter notebooks and basics of pandas matplotlib and numpy it also assumes some familiarity with the api of scikit learn and how to do cross validations and grid search with scikit learn obtaining the tutorial material if you are familiar with git it is most convenient if you clone the github repository this is highly encouraged as it allows you to easily synchronize any changes to the material git clone https github com amueller ml workshop 3 of 4 if you are not familiar with git you can download the repository as a zip file by heading over to the github repository https github com amueller ml workshop 3 of 4 in your browser and click the green download button in the upper right images download repo png please note that i may add and improve the material until shortly before the tutorial session and we recommend you to update your copy of the materials one day before the tutorials if you have an github account and forked cloned the repository via github you can sync your existing fork with via the following commands git pull origin master installation notes this tutorial will require recent installations of numpy http www numpy org scipy http www scipy org matplotlib http matplotlib org pillow https python pillow org pandas http pandas pydata org scikit learn http scikit learn org stable 0 22 1 ipython http ipython readthedocs org en stable jupyter notebook http jupyter org mlxtend imbalanced learn the last one is important you should be able to type jupyter notebook in your terminal window and see the notebook panel load in your web browser try opening and running a notebook from the material to see check that it works for users who do not yet have these packages installed a relatively painless way to install all the requirements is to use a python distribution such as anaconda https www continuum io downloads which includes the most relevant python packages for science math engineering and data analysis anaconda can be downloaded and installed for free including commercial use and redistribution the code examples in this tutorial requires python 3 5 or later after obtaining the material we strongly recommend you to open and execute a jupyter notebook jupter notebook check env ipynb that is located at the top level of this repository inside the repository you can open the notebook by executing bash jupyter notebook check env ipynb inside this repository inside the notebook you can run the code cell by clicking on the run cells button as illustrated in the figure below images check env 1 png finally if your environment satisfies the requirements for the tutorials the executed code cell will produce an output message as shown below images check env 2 png | ai |
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30-days-of-web-development | p align center img src images 30 days of webdev hardcover 2x png p h3 align center your first 30 days of web development h3 p align center a free introduction to making heads or tails of web development in 30 delightful lessons written by the authors of a href https fullstack io web developers field guide how to become a web developer a field guide a a p p align center a href https fullstack io 30 days of webdev img src images download button png width 372 height 66 a p introduction interested in learning web development but having trouble getting started i wrote this book for the me of five years ago the person who wants to know how to become web developer my path was convoluted and inefficient but yours does not have to be over the next 30 days i show you how to navigate the sometimes confusing world of web development for free we ll walk all of the key ideas in web development starting with the very basics and defining terms that are often skipped by the end of this book you will be able to make an educated decision about your future and understand the steps and commitment it will take to get there this repository contains the entire source and content for the article series of 30 days of web development https fullstack io 30 days of webdev hosted by the authors of how to become a web developer a field guide https fullstack io web developers field guide what s inside prettier ignore a align center href day 01 img src day 01 public article image jpg width 140px a h4 align center a href day 01 can i become a web developer a h4 a href day 02 img src day 02 public article image jpg width 140px a h4 align center a href day 02 developer tools setup and tooling a h4 a href day 03 img src day 03 public article image jpg width 140px a h4 align center a href day 03 operationg systems a h4 a href day 04 img src day 04 public article image jpg width 140px a h4 align center a href day 04 gui and shells a h4 a href day 05 img src day 05 public article image jpg width 140px a h4 align center a href day 05 text editors a h4 a href day 06 img src day 06 public article image jpg width 140px a h4 align center a href day 06 html is a markup language a h4 a href day 07 img src day 07 public article image jpg width 140px a h4 align center a href day 07 html tags and elements a h4 a href day 08 img src day 08 public article image jpg width 140px a h4 align center a href day 08 playing with elements and tags a h4 a href day 09 img src day 09 public article image jpg width 140px a h4 align center a href day 09 giving html some style a h4 a href day 10 img src day 10 public article image jpg width 140px a h4 align center a href day 10 css and html together a h4 a href day 11 img src day 11 public article image jpg width 140px a h4 align center a href day 11 css syntax a h4 a href day 12 img src day 12 public article image jpg width 140px a h4 align center a href day 12 selector specificity with css a h4 a href day 13 img src day 13 public article image jpg width 140px a h4 align center a href day 13 playing with css selectors a h4 a href day 14 img src day 14 public article image jpg width 140px a h4 align center a href day 14 css units a h4 a href day 15 img src day 15 public article image jpg width 140px a h4 align center a href day 15 adding interactivity a h4 a href day 16 img src day 16 public article image jpg width 140px a h4 align center a href day 16 why is javascript important a h4 a href day 17 img src day 17 public article image jpg width 140px a h4 align center a href day 17 javascript in the devtools a h4 a href day 18 img src day 18 public article image jpg width 140px a h4 align center a href day 18 what is a programming language a h4 a href day 19 img src day 19 public article image jpg width 140px a h4 align center a href day 19 top 10 programming languages a h4 a href day 20 img src day 20 public article image jpg width 140px a h4 align center a href day 20 a closer look at the top 4 programming languages a h4 a href day 21 img src day 21 public article image jpg width 140px a h4 align center a href day 21 frameworks and libraries a h4 a href day 22 img src day 22 public article image jpg width 140px a h4 align center a href day 22 navigating job postings a h4 a href day 23 img src day 23 public article image jpg width 140px a h4 align center a href day 23 what is a terminal a h4 a href day 24 img src day 24 public article image jpg width 140px a h4 align center a href day 24 cli vs gui a h4 a href day 25 img src day 25 public article image jpg width 140px a h4 align center a href day 25 file paths part i a h4 a href day 26 img src day 26 public article image jpg width 140px a h4 align center a href day 26 file paths part ii a h4 a href day 27 img src day 27 public article image jpg width 140px a h4 align center a href day 27 file systems and types a h4 a href day 28 img src day 28 public article image jpg width 140px a h4 align center a href day 28 introduction to git a h4 a href day 29 img src day 29 public article image jpg width 140px a h4 align center a href day 29 introduction to github a h4 a href day 30 img src day 30 public article image jpg width 140px a h4 align center a href day 30 web developer field guide a h4 how to use this repository each day contains a lesson that teaches a concept for getting into web development some of the lessons have exercises and example code which is contained in the src folder of each day contributors all contributors list start do not remove or modify this section prettier ignore table tr td align center a href https github com monkeychip img src https avatars3 githubusercontent com u 6618863 v 4 width 140px h4 align center a href https github com monkeychip angel garbarino a h4 td td align center a href https newline co img src https avatars2 githubusercontent com u 4318 v 4 width 140px h4 align center a href https newline co nate murray a h4 td tr table all contributors list end how to become a web developer a field guide book a href https fullstack io web developers field guide img align right src images how to become a webdeveloper field guide 250h 2x png alt how to become a web developer a field guide width 201 height 250 a this repo was written and is maintained by the authors of how to become a web developer a field guide https fullstack io web developers field guide team in the book we cover in even more depth the next steps to becoming a web developer we have in depth exercises on learning html and css javascript the terminal git and more it s written with the absolute beginner in mind this series 30 days of web development covers only the early basics of web development if you re considering starting a career as a web developer but you ve been confused as to what to learn first then go grab a copy of how to become a web developer a field guide https fullstack io web developers field guide | front_end |
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zephyr-devices | zephyr devices a set of utility and driver classes to support ic peripherals in zephyr rtos adopts the same layout structure as the main zephyr repository here https github com zephyrproject rtos zephyr background the devices contained within this library have been used in the design of the herald hardware project s wearable and bluetooth beacons for ehealth and digital contact tracing they are provided here for wider community benefit information on the hardware designs can be found here https github com theheraldproject herald hardware zephyr board definitions we are providing board definitions for the latest bleeding edge board designs we are working on here we will contribute back to zephyr once each hardware design is ratified and physically tested the main branch here is release quality but the definitions may not have made it into zephyr upstream yet the develop branch is bleeding edge but where each merged commit has been tested as per our contributing guide see contributing md license and copyright all files are copyright 2021 herald project contributors and are provided under the apache 2 0 license license apache 2 0 https img shields io badge license apache2 0 yellow svg https opensource org licenses apache 2 0 see license txt and notice txt for details | zephyr rtos device drivers herald bluetooth proximity wearable wearable-devices health-care | os |
sktime | a href https www sktime net img src https github com sktime sktime blob main docs source images sktime logo svg width 175 align right a welcome to sktime a unified interface for machine learning with time series rocket version 0 24 0 out now check out the release notes here https www sktime net en latest changelog html sktime is a library for time series analysis in python it provides a unified interface for multiple time series learning tasks currently this includes time series classification regression clustering annotation and forecasting it comes with time series algorithms https www sktime net en stable estimator overview html and scikit learn compatible tools to build tune and validate time series models scikit learn https scikit learn org stable overview open 160 source bsd 3 clause https img shields io badge license bsd 203 clause blue svg https github com sktime sktime blob main license tutorials binder https mybinder org badge logo svg https mybinder org v2 gh sktime sktime main filepath examples youtube https img shields io static v1 logo youtube label youtube message tutorials color red https www youtube com playlist list plks3uggjlwhqnzu0leoelkvnjvvest2d0 community discord https img shields io static v1 logo discord label discord message chat color lightgreen https discord com invite 54aczafsn7 slack https img shields io static v1 logo linkedin label linkedin message news color lightblue https www linkedin com company scikit time ci cd github actions https img shields io github actions workflow status sktime sktime wheels yml logo github https github com sktime sktime actions workflows wheels yml codecov https img shields io codecov c github sktime sktime label codecov logo codecov https codecov io gh sktime sktime readthedocs https img shields io readthedocs sktime logo readthedocs https www sktime net en latest badge latest platform https img shields io conda pn conda forge sktime https github com sktime sktime code pypi https img shields io pypi v sktime color orange https pypi org project sktime conda https img shields io conda vn conda forge sktime https anaconda org conda forge sktime python versions https img shields io pypi pyversions sktime https www python org black https img shields io badge code 20style black 000000 svg https github com psf black downloads downloads https static pepy tech personalized badge sktime period week units international system left color grey right color blue left text weekly 20 pypi https pepy tech project sktime downloads https static pepy tech personalized badge sktime period month units international system left color grey right color blue left text monthly 20 pypi https pepy tech project sktime downloads https static pepy tech personalized badge sktime period total units international system left color grey right color blue left text cumulative 20 pypi https pepy tech project sktime citation zenodo https zenodo org badge doi 10 5281 zenodo 3749000 svg https doi org 10 5281 zenodo 3749000 books documentation documentation star tutorials new to sktime here s everything you need to know clipboard binder notebooks example notebooks to play with in your browser woman technologist user guides how to use sktime and its features scissors extension templates how to build your own estimator using sktime s api control knobs api reference the detailed reference for sktime s api tv video tutorial our video tutorial from 2021 pydata global hammer and wrench changelog changes and version history deciduous tree roadmap sktime s software and community development plan pencil related software a list of related software tutorials https www sktime net en latest tutorials html binder notebooks https mybinder org v2 gh sktime sktime main filepath examples user guides https www sktime net en latest user guide html video tutorial https github com sktime sktime tutorial pydata global 2021 api reference https www sktime net en latest api reference html changelog https www sktime net en latest changelog html roadmap https www sktime net en latest roadmap html related software https www sktime net en latest related software html speech balloon where to ask questions questions and feedback are extremely welcome we strongly believe in the value of sharing help publicly as it allows a wider audience to benefit from it type platforms bug bug reports github issue tracker sparkles feature requests ideas github issue tracker woman technologist usage questions github discussions stack overflow speech balloon general discussion github discussions factory contribution development dev chat channel discord globe with meridians community collaboration session discord fridays 3 pm utc dev meet ups channel github issue tracker https github com sktime sktime issues github discussions https github com sktime sktime discussions stack overflow https stackoverflow com questions tagged sktime discord https discord com invite 54aczafsn7 dizzy features our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety sktime provides a unified interface for distinct but related time series learning tasks it features dedicated time series algorithms https www sktime net en stable estimator overview html and tools for composite model building such as pipelining ensembling tuning and reduction empowering users to apply an algorithm designed for one task to another sktime also provides interfaces to related libraries for example scikit learn statsmodels tsfresh pyod and fbprophet among others for deep learning see our companion package sktime dl https github com sktime sktime dl statsmodels https www statsmodels org stable index html tsfresh https tsfresh readthedocs io en latest pyod https pyod readthedocs io en latest fbprophet https facebook github io prophet module status links forecasting stable tutorial https www sktime net en latest examples 01 forecasting html api reference https www sktime net en latest api reference forecasting html extension template https github com sktime sktime blob main extension templates forecasting py time series classification stable tutorial https github com sktime sktime blob main examples 02 classification ipynb api reference https www sktime net en latest api reference classification html extension template https github com sktime sktime blob main extension templates classification py time series regression stable api reference https www sktime net en latest api reference regression html transformations stable tutorial https github com sktime sktime blob main examples 03 transformers ipynb api reference https www sktime net en latest api reference transformations html extension template https github com sktime sktime blob main extension templates transformer py parameter fitting maturing api reference https www sktime net en latest api reference param est html extension template https github com sktime sktime blob main extension templates transformer py time series clustering maturing api reference https www sktime net en latest api reference clustering html extension template https github com sktime sktime blob main extension templates clustering py time series distances kernels maturing tutorial https github com sktime sktime blob main examples 03 transformers ipynb api reference https www sktime net en latest api reference dists kernels html extension template https github com sktime sktime blob main extension templates dist kern panel py time series alignment experimental api reference https www sktime net en latest api reference alignment html extension template https github com sktime sktime blob main extension templates alignment py annotation experimental extension template https github com sktime sktime blob main extension templates annotation py distributions and simulation experimental forecasting https github com sktime sktime tree main sktime forecasting time series classification https github com sktime sktime tree main sktime classification time series regression https github com sktime sktime tree main sktime regression time series clustering https github com sktime sktime tree main sktime clustering annotation https github com sktime sktime tree main sktime annotation time series distances kernels https github com sktime sktime tree main sktime dists kernels time series alignment https github com sktime sktime tree main sktime alignment transformations https github com sktime sktime tree main sktime transformations distributions and simulation https github com sktime sktime tree main sktime proba parameter fitting https github com sktime sktime tree main sktime param est hourglass flowing sand install sktime for troubleshooting and detailed installation instructions see the documentation https www sktime net en latest installation html operating system macos x linux windows 8 1 or higher python version python 3 8 3 9 3 10 3 11 and 3 12 only 64 bit package managers pip conda via conda forge pip https pip pypa io en stable conda https docs conda io en latest pip using pip sktime releases are available as source packages and binary wheels available wheels are listed here https pypi org simple sktime bash pip install sktime or with maximum dependencies bash pip install sktime all extras for curated sets of soft dependencies for specific learning tasks bash pip install sktime forecasting for selected forecasting dependencies pip install sktime forecasting transformations forecasters and transformers or similar valid sets are forecasting transformations classification regression clustering param est networks annotation alignment cave in general not all soft dependencies for a learning task are installed only a curated selection conda you can also install sktime from conda via the conda forge channel the feedstock including the build recipe and configuration is maintained in this conda forge repository https github com conda forge sktime feedstock bash conda install c conda forge sktime or with maximum dependencies bash conda install c conda forge sktime all extras as conda does not support dependency sets flexible choice of soft dependencies is unavailable via conda zap quickstart forecasting python from sktime datasets import load airline from sktime forecasting base import forecastinghorizon from sktime forecasting model selection import temporal train test split from sktime forecasting theta import thetaforecaster from sktime performance metrics forecasting import mean absolute percentage error y load airline y train y test temporal train test split y fh forecastinghorizon y test index is relative false forecaster thetaforecaster sp 12 monthly seasonal periodicity forecaster fit y train y pred forecaster predict fh mean absolute percentage error y test y pred 0 08661467738190656 time series classification python from sktime classification interval based import timeseriesforestclassifier from sktime datasets import load arrow head from sklearn model selection import train test split from sklearn metrics import accuracy score x y load arrow head x train x test y train y test train test split x y classifier timeseriesforestclassifier classifier fit x train y train y pred classifier predict x test accuracy score y test y pred 0 8679245283018868 wave how to get involved there are many ways to join the sktime community we follow the all contributors https github com all contributors all contributors specification all kinds of contributions are welcome not just code documentation gift heart contribute how to contribute to sktime school satchel mentoring new to open source apply to our mentoring program date meetings join our discussions tutorials workshops and sprints woman mechanic developer guides how to further develop sktime s code base construction enhancement proposals design a new feature for sktime medal sports contributors a list of all contributors raising hand roles an overview of our core community roles money with wings donate fund sktime maintenance and development classical building governance how and by whom decisions are made in sktime s community contribute https www sktime net en latest get involved contributing html donate https opencollective com sktime extension templates https github com sktime sktime tree main extension templates developer guides https www sktime net en latest developer guide html contributors https github com sktime sktime blob main contributors md governance https www sktime net en latest get involved governance html mentoring https github com sktime mentoring meetings https calendar google com calendar u 0 embed src sktime toolbox gmail com ctz utc enhancement proposals https github com sktime enhancement proposals roles https www sktime net en latest about team html bulb project vision by the community for the community developed by a friendly and collaborative community the right tool for the right task helping users to diagnose their learning problem and suitable scientific model types embedded in state of art ecosystems and provider of interoperable interfaces interoperable with scikit learn statsmodels tsfresh and other community favorites rich model composition and reduction functionality build tuning and feature extraction pipelines solve forecasting tasks with scikit learn regressors clean descriptive specification syntax based on modern object oriented design principles for data science fair model assessment and benchmarking build your models inspect your models check your models and avoid pitfalls easily extensible easy extension templates to add your own algorithms compatible with sktime s api | time-series machine-learning scikit-learn time-series-classification time-series-regression forecasting time-series-analysis data-science data-mining hacktoberfest | ai |
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lightning-component-and-design-system | lightning component and design system a href https login salesforce com packaging installpackage apexp p0 04t28000000mx8h target blank img alt deploy to salesforce src https raw githubusercontent com afawcett githubsfdeploy master src main webapp resources img deploy png a activity time line img src http f st hatena com images fotolife t tyoshikawa1106 20150830 20150830235051 png button img src http f st hatena com images fotolife t tyoshikawa1106 20150830 20150830235243 png datepickers img src http f st hatena com images fotolife t tyoshikawa1106 20150830 20150830235430 png icons img src http f st hatena com images fotolife t tyoshikawa1106 20150830 20150830235337 png | os |
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yoga | p align center img src packages doc src images lotus png p all contributors badge start do not remove or modify this section all contributors https img shields io badge all contributors 49 orange svg style flat square contributors all contributors badge end github actions https github com gympass yoga workflows yoga 20 20gympass 20design 20system badge svg design system at gympass our main intent is to support our projects we have open sourced our project for those who are interested in checkout how we do things and organize our code and documentation here what does it mean yoga is a scientific system of practices made to help each one of us achieve our highest potential and experience documentation yoga is documented at http gympass github io yoga https gympass github io yoga architecture our codebase is a monorepo and individually versioned libraries here s an overview of our packages package version size gympass yoga packages yoga npm version https badgen net npm v gympass yoga https www npmjs com package gympass yoga bundle size https badgen net bundlephobia minzip gympass yoga https bundlephobia com result p gympass yoga gympass yoga tokens packages tokens npm version https badgen net npm v gympass yoga tokens https www npmjs com package gympass yoga tokens bundle size https badgen net bundlephobia minzip gympass yoga tokens https bundlephobia com result p gympass yoga tokens gympass yoga common packages common npm version https badgen net npm v gympass yoga common https www npmjs com package gympass yoga common bundle size https badgen net bundlephobia minzip gympass yoga common https bundlephobia com result p gympass yoga common gympass yoga icons packages icons npm version https badgen net npm v gympass yoga icons https www npmjs com package gympass yoga icons bundle size https badgen net bundlephobia minzip gympass yoga icons https bundlephobia com result p gympass yoga icons gympass yoga helpers packages helpers npm version https badgen net npm v gympass yoga helpers https www npmjs com package gympass yoga helpers bundle size https badgen net bundlephobia minzip gympass yoga helpers https bundlephobia com result p gympass yoga helpers gympass yoga system packages system npm version https badgen net npm v gympass yoga system https www npmjs com package gympass yoga system bundle size https badgen net bundlephobia minzip gympass yoga system https bundlephobia com result p gympass yoga system contributing this repository should and will grow its contents will be used for many people in our current and future projects as such we follow some practices to keep a common architecture in our changes code of conduct https github com stumpsyn policies blob master citizen code of conduct md we adopted the citizen code of condute which is widely used in many projects and communities such the rust comunity https www rust lang org policies code of conduct please read the full text https github com stumpsyn policies blob master citizen code of conduct md so that you can understand what actions will and will not be tolerated contributing guide contributing md read our contributing guide contributing md to learn about our development process how to propose bugfixes and improvements and how to build and test your changes to yoga contributors thanks goes to these wonderful people emoji key https allcontributors org docs en emoji key all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tbody tr td align center valign top width 14 28 a href https twitter com ggdaltoso img src https avatars0 githubusercontent com u 6536985 v 4 s 100 width 100px alt gabriel daltoso br sub b gabriel daltoso b sub a br a href https github com gympass yoga commits author ggdaltoso title code a a href ideas ggdaltoso title ideas planning feedback a a href https github com gympass yoga commits author ggdaltoso title documentation a a href https github com gympass yoga pulls q is 3apr reviewed by 3aggdaltoso title reviewed pull requests a td td align center valign top width 14 28 a href https twitter com allyssonsantos img src https avatars1 githubusercontent com u 13424727 v 4 s 100 width 100px alt allysson dos santos br sub b allysson dos santos b sub a br a href https github com gympass yoga commits author allyssonsantos title code a a href ideas allyssonsantos title ideas planning feedback a a href https github com gympass yoga commits author allyssonsantos title documentation a a href https github com gympass yoga pulls q is 3apr reviewed by 3aallyssonsantos title reviewed pull requests a td td align center valign top width 14 28 a href https br linkedin com in victor matheus jesus caetano 9633b5118 img src https avatars0 githubusercontent com u 11219999 v 4 s 100 width 100px alt victor caetano br sub b victor caetano b sub a br a href https github com gympass yoga commits author victormath12 title code a a href ideas victormath12 title ideas planning feedback 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title code a a href ideas tck1 title ideas planning feedback a a href https github com gympass yoga commits author tck1 title documentation a td td align center valign top width 14 28 a href https github com luispiresgympass img src https avatars0 githubusercontent com u 58981184 v 4 s 100 width 100px alt luis pires br sub b luis pires b sub a br a href https github com gympass yoga commits author luispiresgympass title code a td td align center valign top width 14 28 a href https github com invilliaanajacobsen img src https avatars2 githubusercontent com u 57181206 v 4 s 100 width 100px alt invilliaanajacobsen br sub b invilliaanajacobsen b sub a br a href https github com gympass yoga issues q author 3ainvilliaanajacobsen title bug reports a td tr tr td align center valign top width 14 28 a href https www linkedin com in caioalexandrebr img src https avatars1 githubusercontent com u 31045534 v 4 s 100 width 100px alt caio alexandre br sub b caio alexandre b sub a br a href https 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44280864 v 4 s 100 width 100px alt alyce cristine br sub b alyce cristine b sub a br a href https github com gympass yoga commits author alycecristines title code a a href https github com gympass yoga commits author alycecristines title documentation a a href https github com gympass yoga issues q author 3aalycecristines title bug reports a td td align center valign top width 14 28 a href https github com nypacheco img src https avatars githubusercontent com u 12848917 v 4 s 100 width 100px alt nath lia pacheco br sub b nath lia pacheco b sub a br a href https github com gympass yoga commits author nypacheco title code a a href https github com gympass yoga commits author nypacheco title documentation a td td align center valign top width 14 28 a href https github com matheushdsbr img src https avatars githubusercontent com u 32910717 v 4 s 100 width 100px alt matheus henrique br sub b matheus henrique b sub a br a href https github com gympass yoga commits author matheushdsbr title 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commits author wendlereis title code a td tr tr td align center valign top width 14 28 a href https github com dehmirandac2 img src https avatars githubusercontent com u 8313529 v 4 s 100 width 100px alt deborah miranda br sub b deborah miranda b sub a br a href https github com gympass yoga issues q author 3adehmirandac2 title bug reports a td td align center valign top width 14 28 a href https mmartins vercel app img src https avatars githubusercontent com u 46993493 v 4 s 100 width 100px alt matheus martins br sub b matheus martins b sub a br a href https github com gympass yoga commits author mmartinsoliv title code a a href ideas mmartinsoliv title ideas planning feedback a a href https github com gympass yoga commits author mmartinsoliv title documentation a a href https github com gympass yoga pulls q is 3apr reviewed by 3ammartinsoliv title reviewed pull requests a td td align center valign top width 14 28 a href https www linkedin com in leticiasoaresfrontenddeveloper img src https avatars githubusercontent com u 11762938 v 4 s 100 width 100px alt leticia soares br sub b leticia soares b sub a br a href https github com gympass yoga commits author leticiasoares title code a a href https github com gympass yoga commits author leticiasoares title documentation a td td align center valign top width 14 28 a href https www linkedin com in marcosricardo0101 img src https avatars githubusercontent com u 27781419 v 4 s 100 width 100px alt marcos ricardo br sub b marcos ricardo b sub a br a href https github com gympass yoga commits author marcosricardo title documentation a a href https github com gympass yoga commits author marcosricardo title code a td td align center valign top width 14 28 a href https github com falkaniere img src https avatars githubusercontent com u 39073602 v 4 s 100 width 100px alt jonatas falkaniere br sub b jonatas falkaniere b sub a br a href https github com gympass yoga commits author falkaniere title code a a href ideas falkaniere title ideas planning feedback a td td align center valign top width 14 28 a href https davimdantas github io img src https avatars githubusercontent com u 38892983 v 4 s 100 width 100px alt davi marins dantas br sub b davi marins dantas b sub a br a href https github com gympass yoga commits author davimdantas title documentation a td td align center valign top width 14 28 a href https github com naabraz img src https avatars githubusercontent com u 18318587 v 4 s 100 width 100px alt natalia braz br sub b natalia braz b sub a br a href https github com gympass yoga issues q author 3anaabraz title bug reports a td tr tr td align center valign top width 14 28 a href http ericcleao img src https avatars githubusercontent com u 5889973 v 4 s 100 width 100px alt eric cerqueira le o br sub b eric cerqueira le o b sub a br a href https github com gympass yoga commits author ericcleao title code a a href https github com gympass yoga commits author ericcleao title documentation a td td align center valign top width 14 28 a href https github com alinerigoni img src https avatars githubusercontent com u 31771420 v 4 s 100 width 100px alt aline rigoni br sub b aline rigoni b sub a br a href https github com gympass yoga commits author alinerigoni title code a td td align center valign top width 14 28 a href https github com lucasfernandesbr img src https avatars githubusercontent com u 54141141 v 4 s 100 width 100px alt lucas fernandes souza br sub b lucas fernandes souza b sub a br a href https github com gympass yoga commits author lucasfernandesbr title code a td td align center valign top width 14 28 a href https github com hesugiyama img src https avatars githubusercontent com u 14081572 v 4 s 100 width 100px alt henrique sugiyama br sub b henrique sugiyama b sub a br a href https github com gympass yoga commits author hesugiyama title code a td td align center valign top width 14 28 a href https github com frgiovanna img src https avatars githubusercontent com u 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com in leonardo gomes 7187a919b img src https avatars githubusercontent com u 61520601 v 4 s 100 width 100px alt leonardo gomes br sub b leonardo gomes b sub a br a href https github com gympass yoga commits author leosilvagomes title code a td td align center valign top width 14 28 a href https github com rafaelcoletagympass img src https avatars githubusercontent com u 100871379 v 4 s 100 width 100px alt rafael pizzaia coleta br sub b rafael pizzaia coleta b sub a br a href https github com gympass yoga commits author rafaelcoletagympass title code a td td align center valign top width 14 28 a href https github com diegomarcuz img src https avatars githubusercontent com u 37422384 v 4 s 100 width 100px alt diego marcuz br sub b diego marcuz b sub a br a href https github com gympass yoga issues q author 3adiegomarcuz title bug reports a td td align center valign top width 14 28 a href http guilhermecardoso dev br img src https avatars githubusercontent com u 15979107 v 4 s 100 width 100px alt luis guilherme cardoso rosa br sub b luis guilherme cardoso rosa b sub a br a href https github com gympass yoga commits author lguilhermecardoso title code a td td align center valign top width 14 28 a href https github com ruanramalho img src https avatars githubusercontent com u 58890915 v 4 s 100 width 100px alt ruan ramalho br sub b ruan ramalho b sub a br a href https github com gympass yoga issues q author 3aruanramalho title bug reports a td td align center valign top width 14 28 a href https github com caioaugustor img src https avatars githubusercontent com u 120468000 v 4 s 100 width 100px alt caio augusto br sub b caio augusto b sub a br a href https github com gympass yoga issues q author 3acaioaugustor title bug reports a td td align center valign top width 14 28 a href https www linkedin com in juliaoharabr img src https avatars githubusercontent com u 93061504 v 4 s 100 width 100px alt j lia ohara br sub b j lia ohara b sub a br a href https github com gympass 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lorenzo b sub a br a href https github com gympass yoga commits author luanlorenzo title code a a href https github com gympass yoga commits author luanlorenzo title documentation a td td align center valign top width 14 28 a href https tomaskeong vercel app img src https avatars githubusercontent com u 47691990 v 4 s 100 width 100px alt tom s keong br sub b tom s keong b sub a br a href https github com gympass yoga commits author tomaskeong title code a td td align center valign top width 14 28 a href https github com leonardokl img src https avatars githubusercontent com u 7108030 v 4 s 100 width 100px alt leonardo luiz br sub b leonardo luiz b sub a br a href https github com gympass yoga commits author leonardokl title code a a href https github com gympass yoga commits author leonardokl title documentation a td td align center valign top width 14 28 a href https github com marcos creuz img src https avatars githubusercontent com u 10004004 v 4 s 100 width 100px alt marcos creuz 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nlp-architect | warning discontinuation of project this project will no longer be maintained by intel this project has been identified as having known security escapes intel has ceased development and contributions including but not limited to maintenance bug fixes new releases or updates to this project intel no longer accepts patches to this project p align center br img src https raw githubusercontent com intellabs nlp architect master docs source source assets nlp architect logo trans png width 400 br p h2 align center a deep learning nlp nlu library by a href https www intel ai research intel ai lab a h2 p align center a href https github com intellabs nlp architect blob master license img alt github src https img shields io github license intellabs nlp architect svg color blue style flat square a a href https intellabs github io nlp architect img alt website src https img shields io website http intellabs github io nlp architect svg down color red down message offline style flat square up message online a a href https github com intellabs nlp architect releases img alt github release src https img shields io github release intellabs nlp architect svg style flat square a a href https pepy tech project nlp architect img src https pepy tech badge nlp architect a p h4 align center a href overview overview a a href models models a a href installing nlp architect installation a a href https github com intellabs nlp architect tree master examples examples a a href https intellabs github io nlp architect a a href https intellabs github io nlp architect documentation a a href https github com intellabs nlp architect tree master tutorials tutorials a a href https intellabs github io nlp architect developer guide html contributing a h4 nlp architect is an open source python library for exploring state of the art deep learning topologies and techniques for optimizing natural language processing and natural language understanding neural networks overview nlp architect is an nlp library designed to be flexible easy to extend allow for easy and rapid integration of nlp models in applications and to showcase optimized models features core nlp models used in many nlp tasks and useful in many nlp applications novel nlu models showcasing novel topologies and techniques optimized nlp nlu models showcasing different optimization algorithms on neural nlp nlu models model oriented design train and run models from command line api for using models for inference in python procedures to define custom processes for training inference or anything related to processing cli sub system for running procedures based on optimized deep learning frameworks tensorflow pytorch dynet essential utilities for working with nlp models text string pre processing io data manipulation metrics embeddings installing nlp architect we recommend to install nlp architect in a new python environment to use python 3 6 with up to date pip setuptools and h5py install using pip install core library only sh pip install nlp architect install from source github includes core library examples solutions and tutorials sh git clone https github com intellabs nlp architect git cd nlp architect pip install e install in developer mode running examples and solutions to run provided examples and solutions please install the library with all flag which will install extra packages required requires installation from source sh pip install all models nlp models that provide best or near in class performance word chunking https intellabs github io nlp architect tagging sequence tagging html word chunker named entity recognition https intellabs github io nlp architect tagging sequence tagging html named entity recognition dependency parsing https intellabs github io nlp architect bist parser html intent extraction https intellabs github io nlp architect intent html sentiment classification https intellabs github io nlp architect sentiment html supervised sentiment language models https intellabs github io nlp architect lm html language modeling with tcn transformers https intellabs github io nlp architect transformers html for nlp tasks natural language understanding nlu models that address semantic understanding aspect based sentiment analysis absa https intellabs github io nlp architect absa html joint intent detection and slot tagging https intellabs github io nlp architect intent html noun phrase embedding representation np2vec https intellabs github io nlp architect np2vec html most common word sense detection https intellabs github io nlp architect word sense html relation identification https intellabs github io nlp architect identifying semantic relation html cross document coreference https intellabs github io nlp architect cross doc coref html noun phrase semantic segmentation https intellabs github io nlp architect np segmentation html optimizing nlp nlu models and misc optimization techniques quantized bert 8bit https intellabs github io nlp architect quantized bert html knowledge distillation using transformers https intellabs github io nlp architect transformers distillation html sparse and quantized neural machine translation gnmt https intellabs github io nlp architect sparse gnmt html solutions end to end applications using one or more models term set expansion https intellabs github io nlp architect term set expansion html uses the included word chunker as a noun phrase extractor and np2vec to create semantic term sets topics and trend analysis https intellabs github io nlp architect trend analysis html analyzing trending phrases in temporal corpora aspect based sentiment analysis absa https intellabs github io nlp architect absa solution html documentation full library documentation https intellabs github io nlp architect of nlp models algorithms solutions and instructions on how to run each model can be found on our website https intellabs github io nlp architect nlp architect library design philosophy nlp architect is a model oriented library designed to showcase novel and different neural network optimizations the library contains nlp nlu related models per task different neural network topologies which are used in models procedures for simplifying workflows in the library pre defined data processors and dataset loaders and misc utilities the library is designed to be a tool for model development data pre process build model train validate infer save or load a model the main design guidelines are deep learning framework agnostic nlp nlu models per task different topologies used in models showcase end to end applications solutions utilizing one or more nlp architect model generic dataset loaders textual data processing utilities and miscellaneous utilities that support nlp model development loaders text processors io metrics etc procedures for defining processes for training inference optimization or any kind of elaborate script pythonic api for using models for inference extensive model documentation and tutorials note nlp architect is an active space of research and development throughout future releases new models solutions topologies and framework additions and changes will be made we aim to make sure all models run with python 3 6 we encourage researchers and developers to contribute their work into the library citing if you use nlp architect in your research please use the following citation misc izsak peter 2018 1477518 title nlp architect by intel ai lab month nov year 2018 doi 10 5281 zenodo 1477518 url https doi org 10 5281 zenodo 1477518 disclaimer the nlp architect is released as reference code for research purposes it is not an official intel product and the level of quality and support may not be as expected from an official product nlp architect is intended to be used locally and has not been designed developed or evaluated for production usage or web deployment additional algorithms and environments are planned to be added to the framework feedback and contributions from the open source and nlp research communities are more than welcome contact contact the nlp architect development team through github issues or email nlp architect intel com documentation https intellabs github io nlp architect tensorflow https www tensorflow org pytorch https pytorch org dynet https dynet readthedocs io en latest | deeplearning nlp nlu tensorflow dynet deep-learning pytorch bert transformers quantization | ai |
No-8-PinterestSwift-1007-stars-on-Github- | no 8 pinterestswift 1007 stars on github 88 gif no 9 youtube transition 786 stars on github watch a video on the right corner like youtube ios app written in swift 3 99 gif no 10 twicket segmented control 680 stars on github custom uisegmentedcontrol replacement for ios written in swift 10 gif pop up ui no 11 sclalertview swift 3056 stars on github beautiful animated alert view written in swift 11 png no 12 swiftmessages 1356 stars on github very flexible alert messages written in swift 12 png no 13 xlactioncontroller 1346 stars on github fully customizable and extensible action sheet controller written in swift 3 13 png no 14 popover 852 stars on github balloon pop up library like facebook app written in pure swift 14 png no 15 presentr 635 stars on github wrapper for custom viewcontroller presentations 15 png feed ui no 16 foldingcell 4285 stars on github an expanding content cell inspired by folding paper material 16 gif no 17 expandingcollection 2425 stars on github a card peek pop controller 17 gif no 18 dgelasticpulltorefresh 2308 stars on github elastic pull to refresh component written in swift 18 gif no 19 persei 2269 stars on github animated top menu for uitableview uicollectionview uiscrollview written in swift 19 gif no 20 iglistkit 2443 stars on github a data driven uicollectionview framework for building fast and flexible lists instagram engineering 20 png no 21 pulltomakesoup 1301 stars on github custom animated pull to refresh that can be easily added to uiscrollview 21 gif no 22 dznemptydataset 6552 stars on github empty state ui library 22 png no 23 instructions 2256 stars on github create walkthroughs and guided tours in swift 23 png no 24 presentation 1680 stars on github make tutorials release notes and animated pages 24 gif color ui no 25 chameleon 7071 stars on github flat color framework for swift developers 25 png no 26 hue 1612 stars on github all in one coloring utility that you ll ever need to write in swift 26 png no 27 dynamiccolor 1310 stars on github extension to manipulate colors easily in swift 27 png image ui no 28 faceaware 1424 stars on github an extension that gives uiimageview the ability to focus on faces within an image when using aspectfill 28 png no 29 complimentarygradientview 384 stars on github create complementary gradients generated from dominant and prominent colors in supplied image 29 png graph ui no 30 charts 11433 stars on github beautiful charts for ios built in swift 30 png no 31 scrollable graphview 3065 stars on github an adaptive scrollable graph view for ios to visualize simple discrete datasets written in swift 31 gif icon ui no 32 paper switch 1849 stars on github rampaperswitch is a swift module which paints over the parent view when the switch is turned on 32 gif no 33 circle menu 1768 stars on github a simple elegant menu with a circular layout 33 gif schedule ui no 34 jtapplecalendar 1026 stars on github the unofficial swift apple calendar library view control for ios amp tvos 34 gif no 35 datetimepicker 455 stars on github a nicer ios ui component for picking date and time 35 png form ui no 36 eureka 4117 stars on github elegant ios form builder in swift 36 png layout ui no 37 neon 3439 stars on github a powerful swift programmatic ui layout framework for iphone amp ipad 37 png message ui no 38 nmessenger 1492 stars on github a fast lightweight messenger component built on asyncdisplaykit and written in swift 38 png search ui no 39 reel search 1364 stars on github a search controller that allows you to choose options from a list 39 gif cocoachina cocoachina app cocoachina 21 ios reactivecocoa 21 swift app swift vapor ios charts swift objective c ios 10 day by day 1 message ios app ui ios xcode8 swift3 bilibili ios push pop 21 knightguo mark 0 0 mr 1 0 vanquisher mark 0 0 baoyewei99 swift github mark 0 0 mark 0 0 su mark 0 0 jasonyuan1986 mark 0 0 davie mark 0 0 fairly make 0 0 mark 0 0 undeadsun mark 1 1 scquiet mark 0 0 jswift lt ios ios http www jianshu com p 510500f3aebd http www qingdan us product 13 2 0 ycru88 mark 0 0 lgypaopao mark 0 0 followme mark 0 0 dreamflyingcow mark 0 0 dpc3231 mark 0 0 aibugu10 mark mark 0 0 aibugu10 ma 0 0 ios gcd ios gcd 6654 ios ios 6002 mbp mbp 5926 macbook pro macbook pro 5567 app app 5190 ios app ios app 4396 3874 java ios java ios 3744 2016 ios 2016 ios 3055 macbook pro macbook pro 3023 rachaelvvv runloop ios mark knightguo 39 swift ui star jonorzhang xib mark ontheway mr 39 swift ui star ios hanxiao mark vanquisher 39 swift ui star mark xue e xcode swift github mark baoyewei99 39 swift ui star apicloud app uiimagepickercontroller audioqueue ios esri arcgis desktop 10 4 1 5686 app store app | os |
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awesome-3D-vision | https github com uzh rpg event based vision resources base 3d 3d 3d 3d 3d 3d 3d 3d ai ai 3d ai sick 3d tof 3d 3d 3d 3d 3d 3d 1 http www opticsjournal net articles abstract aid oje133f6606f9fe076 1 http www cnki com cn article cjfdtotal hbyd201201014 htm 2 fully automatic camera calibration method based on circular markers http www cnki com cn article cjfdtotal yqxb200902028 htm 3 accurate camera calibration using iterative refinement of control points http xueshu baidu com usercenter paper show paperid a68a76813662e8a8ee64f377a8516adb site xueshu se 4 accurate feature extraction and control point correction for camera calibration with a mono plane target http xueshu baidu com usercenter paper show paperid 7a1bfac77a6adb17287b5449a327cd70 site xueshu se 5 http www opticsjournal net articles abstract aid oj191105000133w3z6b9 6 http www opticsjournal net articles abstract aid oj1811210000185b8dag 7 scheimpflug http www opticsjournal net articles abstract aid oj1808090000414z6c9f 8 http www opticsjournal net articles abstract aid oj181115000101pwszv2 1 http xueshu baidu com usercenter paper show paperid ac40e02979ac1aa62cfaf5b3e9365a7b site xueshu se 1 http www opticsjournal net articles abstract aid oj200119000058dkgmjp 3d 360 a practical toolbox for calibrating omnidirectional cameras sift surf harris 3d 1 2 3 machine vision 2016 automated visual inspection theory practice and applications https github com timzhang642 3d machine learning https github com timzhang642 3d machine learning https github com sunglok 3dv tutorial slam slam https github com openslam awesome slam list https github com openslam awesome slam list https github com tzutalin awesome visual slam https github com tzutalin awesome visual slam https github com kanster awesome slam https github com youjiexia awesome slam recent slam research https github com yichencityu recent slam research https github com youngguncho awesome slam datasets https github com youngguncho awesome slam datasets https github com marknabil sfm visual slam https github com marknabil sfm visual slam https github com ckddls1321 slam resources https github com ckddls1321 slam resources slam slam vo ransac pose imu inertial measurement unit ekf ukf pf toro g2o slam books slam simultaneous localization and mapping for mobile robots introduction and methods http www igi global com book simultaneous localization mapping mobile robots 66380 by juan antonio fern ndez madrigal and jos luis blanco claraco 2012 simultaneous localization and mapping exactly sparse information filters http www worldscientific com worldscibooks 10 1142 8145 by zhan wang shoudong huang and gamini dissanayake 2011 an invitation to 3 d vision from images to geometric models http vision ucla edu masks by yi ma stefano soatto jana kosecka and shankar s sastry 2005 multiple view geometry in computer vision http www robots ox ac uk vgg hzbook by richard hartley and andrew zisserman 2004 numerical optimization http home agh edu pl pba pdfdoc numerical optimization pdf by jorge nocedal and stephen j wright 1999 courses lectures slam tutorial icra 2016 http www dis uniroma1 it labrococo tutorial icra 2016 geometry and beyond representations physics and scene understanding for robotics http rss16 representations mit edu at robotics science and systems 2016 robotics upenn https www coursera org specializations robotics on coursera by vijay kumar 2016 robot mapping unifreiburg http ais informatik uni freiburg de teaching ws15 mapping by gian diego tipaldi and wolfram burgard 2015 2016 robot mapping unibonn http www ipb uni bonn de robot mapping by cyrill stachniss 2016 introduction to mobile robotics unifreiburg http ais informatik uni freiburg de teaching ss16 robotics by wolfram burgard michael ruhnke and bastian steder 2015 2016 computer vision ii multiple view geometry tum http vision in tum de teaching ss2016 mvg2016 by daniel cremers spring 2016 advanced robotics ucberkeley http www cs berkeley edu pabbeel by pieter abbeel fall 2015 mapping localization and self driving vehicles https www youtube com watch v x5czmlamncs at cmu ri seminar by john leonard 2015 the problem of mobile sensors setting future goals and indicators of progress for slam http ylatif github io movingsensors sponsored by australian centre for robotics and vision 2015 robotics upenn https alliance seas upenn edu meam620 wiki index php n main homepage by philip dames and kostas daniilidis 2014 autonomous navigation for flying robots http vision in tum de teaching ss2014 autonavx on edx by jurgen sturm and daniel cremers 2014 robust and efficient real time mapping for autonomous robots https www youtube com watch v w3ua1yg2fk at cmu ri seminar by michael kaess 2014 kinectfusion real time 3d reconstruction and interaction using a moving depth camera https www youtube com watch v brgedqdiouq by david kim 2012 code 1 orb slam https github com raulmur orb slam 2 lsd slam https github com tum vision lsd slam 3 orb slam2 https github com raulmur orb slam2 4 dvo dense visual odometry https github com tum vision dvo slam 5 svo semi direct monocular visual odometry https github com uzh rpg rpg svo 6 g2o general graph optimization https github com rainerkuemmerle g2o 7 rgbd slam https github com felixendres rgbdslam v2 project language license coslam http drone sjtu edu cn dpzou project coslam php c gnu general public license dso direct sparse odometry https github com jakobengel dso c gplv3 dtslam deferred triangulation slam https github com plumonito dtslam c modified bsd lsd slam https github com tum vision lsd slam c ros gnu general public license maplab rovioli https github com ethz asl maplab c ros apachev2 0 okvis open keyframe based visual inertial slam https github com ethz asl okvis c bsd orb slam https github com raulmur orb slam2 c gplv3 rebvo realtime edge based visual odometry for a monocular camera https github com juantarrio rebvo c gnu general public license svo semi direct visual odometry https github com uzh rpg rpg svo c ros gnu general public license books computer vision models learning and inference http www computervisionmodels com simon j d prince 2012 computer vision theory and application http szeliski org book rick szeliski 2010 computer vision a modern approach 2nd edition http www amazon com computer vision modern approach 2nd dp 013608592x ref dp ob title bk david forsyth and jean ponce 2011 multiple view geometry in computer vision http www robots ox ac uk vgg hzbook richard hartley and andrew zisserman 2004 visual object recognition synthesis lecture http www morganclaypool com doi abs 10 2200 s00332ed1v01y201103aim011 kristen grauman and bastian leibe 2011 computer vision for visual effects http cvfxbook com richard j radke 2012 high dynamic range imaging acquisition display and image based lighting http www amazon com high dynamic range imaging second dp 012374914x reinhard e heidrich w debevec p pattanaik s ward g myszkowski k 2010 numerical algorithms methods for computer vision machine learning and graphics https people csail mit edu jsolomon share book numerical book pdf justin solomon 2015 courses eeng 512 csci 512 computer vision http inside mines edu whoff courses eeng512 william hoff colorado school of mines 3d computer vision past present and future https www bilibili com video av62437998 visual object and activity recognition https sites google com site ucbcs29443 alexei a efros and trevor darrell uc berkeley computer vision http courses cs washington edu courses cse455 12wi steve seitz university of washington visual recognition spring 2016 http vision cs utexas edu 381v spring2016 fall 2016 http vision cs utexas edu 381v fall2016 kristen grauman ut austin language and vision http www tamaraberg com teaching spring 15 tamara berg unc chapel hill convolutional neural networks for visual recognition http vision stanford edu teaching cs231n fei fei li and andrej karpathy stanford university computer vision http cs nyu edu fergus teaching vision index html rob fergus nyu computer vision https courses engr illinois edu cs543 sp2015 derek hoiem uiuc computer vision foundations and applications http vision stanford edu teaching cs131 fall1415 index html kalanit grill spector and fei fei li stanford university high level vision behaviors neurons and computational models http vision stanford edu teaching cs431 spring1314 fei fei li stanford university advances in computer vision http 6 869 csail mit edu fa15 antonio torralba and bill freeman mit computer vision http www vision rwth aachen de course 11 bastian leibe rwth aachen university computer vision 2 http www vision rwth aachen de course 9 bastian leibe rwth aachen university computer vision http klewel com conferences epfl computer vision pascal fua epfl computer vision 1 http cvlab dresden de courses computer vision 1 carsten rother tu dresden computer vision 2 http cvlab dresden de courses cv2 carsten rother tu dresden multiple view geometry https youtu be rdkwklfgmfo list pltbdjv 4f ejn6udz34tht9eviw7lbeo4 daniel cremers tu munich github link 1 https github com christoschristofidis awesome deep learning https github com christoschristofidis awesome deep learning 2 https github com endymecy awesome deeplearning resources https github com endymecy awesome deeplearning resources github link 1 https github com josephmisiti awesome machine learning https github com josephmisiti awesome machine learning 3d 1 semantic editor https github com mr 520dai semantic segmentation editor ct voxelgrid 1 k http www cnki com cn article cjfdtotal jsjy200904032 htm 2 point cloud denoising based on tensor tucker decomposition https arxiv org abs 1902 07602v2 3 3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model https arxiv org abs 1803 07252v2 3 4 paper 1 2 3 4 5 6 7 iss3d harris3d narf sift3d pcl narf pfh fpfh shot vfh cvfh 3d shape context spin image pfh fpfh fpfh pfh ransac bezier b b nurbs bezier 1 2 3 4 5 6 http www cqvip com qk 97059x 201405 661608237 html 7 http d wanfangdata com cn conference 7057652 4 1 2 3 4 1 3d shapenets a deep representation for volumetric shapes http 3dvision princeton edu projects 2014 3dshapenets paper pdf 2 partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding https arxiv org abs 1812 02713 3 revisiting point cloud classification a new benchmark dataset and classification model on real world data https arxiv org pdf 1908 04616 pdf 4 escape from cells deep kd networks for the recognition of 3d point cloud models http openaccess thecvf com content iccv 2017 papers klokov escape from cells iccv 2017 paper pdf iccv2017 5 iccv2017 http openaccess thecvf com content iccv 2017 papers park colored point cloud iccv 2017 paper pdf colored point cloud registration revisited 6 icra2017 https ieeexplore ieee org document 7989618 segmatch segment based place recognition in 3d point clouds 7 iros2017 https ieeexplore ieee org document 8202239 3d object classification with point convolution network 8 cvpr2018 http openaccess thecvf com content cvpr 2018 papers hua pointwise convolutional neural cvpr 2018 paper pdf pointwise convolutional neural networks 9 cvpr2018 http openaccess thecvf com content cvpr 2018 papers li so net self organizing network cvpr 2018 paper pdf so net self organizing network for point cloud analysis 10 cvpr2018 http openaccess thecvf com content cvpr 2018 papers uy pointnetvlad deep point cvpr 2018 paper pdf pointnetvlad deep point cloud based retrieval for large scale place recognition 11 cvpr2018 http openaccess thecvf com content cvpr 2018 papers le pointgrid a deep cvpr 2018 paper pdf pointgrid a deep network for 3d shape understanding 12 cvpr2019 https raoyongming github io files sfcnn pdf spherical fractal convolutional neural networks for point cloud recognition 13 mm https dl acm org citation cfm id 3343031 3351009 mmjn multi modal joint networks for 3d shape recognition x y alpha beta 4 3 3 3 1x3 6 1 3 icp icp icp point to plane icp point to line icp mbicp gicp ndt 3d multil layer ndt fpcs kfpsc sac ia line segment matching icl 1 an icp variant using a point to line metric https authors library caltech edu 18274 1 censi2008p84782008 ieee international conference on robotics and automation vols 1 9 pdf 2 generalized icp https www researchgate net publication 221344436 generalized icp 3 linear least squares optimization for point to plane icp surface registration https www researchgate net publication 228571031 linear least squares optimization for point to plane icp surface registration 4 metric based iterative closest point scan matching for sensor displacement estimation http webdiis unizar es jminguez mbicp tro pdf 5 nicp dense normal based point cloud registration http jacoposerafin com wp content uploads serafin15iros pdf 6 efficient global point cloud alignment using bayesian nonparametric mixtures http openaccess thecvf com content cvpr 2017 papers straub efficient global point cvpr 2017 paper pdf cvpr2017 7 3dmatch learning local geometric descriptors from rgb d reconstructions http openaccess thecvf com content cvpr 2017 papers zeng 3dmatch learning local cvpr 2017 paper pdf cvpr2017 8 cvpr2018 http openaccess thecvf com content cvpr 2018 papers lawin density adaptive point cvpr 2018 paper pdf density adaptive point set registration 9 cvpr2018 http openaccess thecvf com content cvpr 2018 papers vongkulbhisal inverse composition discriminative cvpr 2018 paper pdf inverse composition discriminative optimization for point cloud registration 10 cvpr2018 http openaccess thecvf com content cvpr 2018 papers deng ppfnet global context cvpr 2018 paper pdf ppfnet global context aware local features for robust 3d point matching 11 eccv2018 http openaccess thecvf com content eccv 2018 papers lei zhou learning and matching eccv 2018 paper pdf learning and matching multi view descriptors for registration of point clouds 12 eccv2018 http openaccess thecvf com content eccv 2018 papers zi jian yew 3dfeat net weakly supervised eccv 2018 paper pdf 3dfeat net weakly supervised local 3d features for point cloud registration 13 eccv2018 http openaccess thecvf com content eccv 2018 papers yinlong liu efficient global point eccv 2018 paper pdf efficient global point cloud registration by matching rotation invariant features through translation search 14 iros2018 https ieeexplore ieee org stamp stamp jsp tp arnumber 8593558 robust generalized point cloud registration with expectation maximization considering anisotropic positional uncertainties 15 cvpr2019 https arxiv org abs 1903 05711 pointnetlk point cloud registration using pointnet 16 cvpr2019 https arxiv org abs 1904 03483 sdrsac semidefinite based randomized approach for robust point cloud registration without correspondences 17 cvpr2019 https arxiv org abs 1811 06879v2 the perfect match 3d point cloud matching with smoothed densities 18 cvpr https arxiv org abs 1811 10136 filterreg robust and efficient probabilistic point set registration using gaussian filter and twist parameterization 19 cvpr2019 http openaccess thecvf com content cvpr 2019 papers deng 3d local features for direct pairwise registration cvpr 2019 paper pdf 3d local features for direct pairwise registration 20 iccv2019 https arxiv org abs 1905 04153v2 deepicp an end to end deep neural network for 3d point cloud registration 21 iccv2019 http openaccess thecvf com content iccv 2019 papers wang deep closest point learning representations for point cloud registration iccv 2019 paper pdf deep closest point learning representations for point cloud registration 22 icra2019 https arxiv org abs 1904 09742 2d3d matchnet learning to match keypoints across 2d image and 3d point cloud 23 cvpr2019 https arxiv org abs 1811 06879v2 the perfect match 3d point cloud matching with smoothed densities 24 cvpr2019 http openaccess thecvf com content cvpr 2019 papers deng 3d local features for direct pairwise registration cvpr 2019 paper pdf 3d local features for direct pairwise registration 25 iccv2019 http openaccess thecvf com content iccv 2019 papers zhou robust variational bayesian point set registration iccv 2019 paper pdf robust variational bayesian point set registration 26 icra2019 https arpg colorado edu papers hmrf icp pdf robust low overlap 3 d point cloud registration for outlier rejection 27 learning multiview 3d point cloud registration cvpr2020 28 1 iros2017 https ieeexplore ieee org document 8206584 analyzing the quality of matched 3d point clouds of objects topic ransac ndt ransac k means normalize cut 3d hough transform 1 http ir ciomp ac cn handle 181722 57569 mode full submit simple show full item record 2 http www cnki com cn article cjfdtotal zzgx201005012 htm 3 http cdmd cnki com cn article cdmd 10213 1015979890 htm 4 sceneencoder scene aware semantic segmentation of point clouds with a learnable scene descriptor https arxiv org abs 2001 09087v1 5 from planes to corners multi purpose primitive detection in unorganized 3d point clouds https arxiv org abs 2001 07360 context cs ro 6 learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation http arxiv org abs 2001 01349 7 jsnet joint instance and semantic segmentation of 3d point clouds https arxiv org abs 1912 09654v1 8 pointnet deep learning on point sets for 3d classification and segmentation https arxiv org abs 1612 00593v2 9 pointnet deep hierarchical feature learning on point sets in a metric space https arxiv org abs 1706 02413v1 10 syncspeccnn synchronized spectral cnn for 3d shape segmentation cvpr2017 11 icra2017 https ieeexplore ieee org document 7989618 segmatch segment based place recognition in 3d point clouds 12 3dv2017 http segcloud stanford edu segcloud 2017 pdf segcloud semantic segmentation of 3d point clouds 13 cvpr2018 http openaccess thecvf com content cvpr 2018 papers huang recurrent slice networks cvpr 2018 paper pdf recurrent slice networks for 3d segmentation of point clouds 14 cvpr2018 http openaccess thecvf com content cvpr 2018 papers wang sgpn similarity group cvpr 2018 paper pdf sgpn similarity group proposal network for 3d point cloud instance segmentation 15 cvpr2018 http openaccess thecvf com content cvpr 2018 papers landrieu large scale point cloud cvpr 2018 paper pdf large scale point cloud semantic segmentation with superpoint graphs 16 eccv2018 http openaccess thecvf com content eccv 2018 papers xiaoqing ye 3d recurrent neural eccv 2018 paper pdf 3d recurrent neural networks with context fusion for point cloud semantic segmentation 17 cvpr2019 https arxiv org abs 1904 00699v1 jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields 18 cvpr2019 https arxiv org abs 1903 00709 partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation 19 iccv2019 http openaccess thecvf com content iccv 2019 papers lahoud 3d instance segmentation via multi task metric learning iccv 2019 paper pdf 3d instance segmentation via multi task metric learning 20 iros2019 https arxiv org pdf 1909 01643v1 pdf pass3d precise and accelerated semantic segmentation for 3d point cloud hausdorff https cloud tencent com product facerecognition from 10680 topic kinectfusion delauary triangulatoins delauary 4d 1 http www opticsjournal net articles abstract aid oj4723ebf4b0b0762 2 scalable surface reconstruction from point clouds with extreme scale and density diversity cvpr2017 http openaccess thecvf com content cvpr 2017 papers mostegel scalable surface reconstruction cvpr 2017 paper pdf 3 iccv2017 http openaccess thecvf com content iccv 2017 papers nan polyfit polygonal surface iccv 2017 paper pdf polyfit polygonal surface reconstruction from point clouds 4 iccv2017 http openaccess thecvf com content iccv 2017 papers ladicky from point clouds iccv 2017 paper pdf from point clouds to mesh using regression 5 eccv2018 http openaccess thecvf com content eccv 2018 papers kejie li efficient dense point eccv 2018 paper pdf efficient dense point cloud object reconstruction using deformation vector fields 6 eccv2018 http openaccess thecvf com content eccv 2018 papers benjamin eckart fast and accurate eccv 2018 paper pdf hgmr hierarchical gaussian mixtures for adaptive 3d registration 7 aaai2018 https aaai org ocs index php aaai aaai18 paper view 16530 16302 learning efficient point cloud generation for dense 3d object reconstruction 8 cvpr2019 https www researchgate net publication 332240602 robust point cloud based reconstruction of large scale outdoor scenes robust point cloud based reconstruction of large scale outdoor scenes 9 aaai2019 https arxiv org abs 1811 11731 capnet continuous approximation projection for 3d point cloud reconstruction using 2d supervision 10 mm https dl acm org citation cfm id 3350960 l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention 11 surfnet generating 3d shape surfaces using deep residual networks 1 cvpr2018 http openaccess thecvf com content cvpr 2018 papers yun reflection removal for cvpr 2018 paper pdf reflection removal for large scale 3d point clouds 2 icml2018 https arxiv org abs 1707 02392 learning representations and generative models for 3d point clouds 3 3dv https arxiv org abs 1808 00671 pcn point completion network 4 cvpr2019 https arxiv org abs 1812 02713 partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding 5 cvpr2019 http www linliang net wp content uploads 2019 04 cvpr2019 pointclound pdf clusternet deep hierarchical cluster network with rigorously rotation invariant representation for point cloud analysis 6 iccv2019 https arxiv org pdf 1812 07050 pdf lpd net 3d point cloud learning for large scale place recognition and environment analysis 7 icra2019 https ras papercept net conferences conferences icra19 program icra19 contentlistweb 2 html speeding up iterative closest point using stochastic gradient descent 1 kitti http www cvlibs net datasets kitti the kitti vision benchmark suite 2 modelnet http modelnet cs princeton edu the princeton modelnet 3 shapenet https www shapenet org a collaborative dataset between researchers at princeton stanford and ttic 4 partnet https shapenet org download parts the partnet dataset provides fine grained part annotation of objects in shapenetcore 5 partnet http kevinkaixu net projects partnet html partnet benchmark from nanjing university and national university of defense technology 6 s3dis http buildingparser stanford edu dataset html download the stanford large scale 3d indoor spaces dataset 7 scannet http www scan net org richly annotated 3d reconstructions of indoor scenes 8 stanford 3d https graphics stanford edu data 3dscanrep the stanford 3d scanning repository 9 uwa dataset http staffhome ecm uwa edu au 00053650 databases html 10 princeton shape benchmark http shape cs princeton edu benchmark the princeton shape benchmark 11 sydney urban objects dataset http www acfr usyd edu au papers sydneyurbanobjectsdataset shtml this dataset contains a variety of common urban road objects scanned with a velodyne hdl 64e lidar collected in the cbd of sydney australia there are 631 individual scans of objects across classes of vehicles pedestrians signs and trees 12 asl datasets repository eth https projects asl ethz ch datasets doku php id home this site is dedicated to provide datasets for the robotics community with the aim to facilitate result evaluations and comparisons 13 large scale point cloud classification benchmark eth http www semantic3d net this benchmark closes the gap and provides a large labelled 3d point cloud data set of natural scenes with over 4 billion points in total 14 robotic 3d scan repository http asrl utias utoronto ca datasets 3dmap the canadian planetary emulation terrain 3d mapping dataset is a collection of three dimensional laser scans gathered at two unique planetary analogue rover test facilities in canada 15 radish http radish sourceforge net the robotics data set repository radish for short provides a collection of standard robotics data sets 16 iqmulus terramobilita contest http data ign fr benchmarks urbananalysis the database contains 3d mls data from a dense urban environment in paris france composed of 300 million points the acquisition was made in january 2013 17 oakland 3 d point cloud dataset http www cs cmu edu vmr datasets oakland 3d cvpr09 doc this repository contains labeled 3 d point cloud laser data collected from a moving platform in a urban environment 18 robotic 3d scan repository http kos informatik uni osnabrueck de 3dscans this repository provides 3d point clouds from robotic experiments log files of robot runs and standard 3d data sets for the robotics community 19 ford campus vision and lidar data set http robots engin umich edu softwaredata ford the dataset is collected by an autonomous ground vehicle testbed based upon a modified ford f 250 pickup truck 20 the stanford track collection https cs stanford edu people teichman stc this dataset contains about 14 000 labeled tracks of objects as observed in natural street scenes by a velodyne hdl 64e s2 lidar 21 pascal3d http cvgl stanford edu projects pascal3d html beyond pascal a benchmark for 3d object detection in the wild 22 3d mnist https www kaggle com daavoo 3d mnist the aim of this dataset is to provide a simple way to get started with 3d computer vision problems such as 3d shape recognition 23 wad http wad ai 2019 challenge html apolloscape http apolloscape auto tracking html the datasets are provided by baidu inc 24 nuscenes https d3u7q4379vrm7e cloudfront net object detection the nuscenes dataset is a large scale autonomous driving dataset 25 presil https uwaterloo ca waterloo intelligent systems engineering lab projects precise synthetic image and lidar presil dataset autonomous depth information semantic segmentation images point wise segmentation point clouds ground point labels point clouds and detailed annotations for all vehicles and people paper https arxiv org abs 1905 00160 26 3d match http 3dmatch cs princeton edu keypoint matching benchmark geometric registration benchmark rgb d reconstruction datasets 27 blvd https github com vcciv blvd a 3d detection b 4d tracking c 5d interactive event recognition and d 5d intention prediction icra 2019 paper https arxiv org abs 1903 06405v1 28 pedx https arxiv org abs 1809 03605 3d pose estimation of pedestrians more than 5 000 pairs of high resolution 12mp stereo images and lidar data along with providing 2d and 3d labels of pedestrians icra 2019 paper https arxiv org abs 1809 03605 29 h3d https usa honda ri com h3d full surround 3d multi object detection and tracking dataset icra 2019 paper https arxiv org abs 1903 01568 30 matterport3d https niessner github io matterport rgb d 10 800 panoramic views from 194 400 rgb d images annotations surface reconstructions camera poses and 2d and 3d semantic segmentations keypoint matching view overlap prediction normal prediction from color semantic segmentation and scene classification 3dv 2017 paper https arxiv org abs 1709 06158 code https github com niessner matterport blog https matterport com blog 2017 09 20 announcing matterport3d research dataset 31 synthcity https arxiv org abs 1907 04758 synthcity is a 367 9m point synthetic full colour mobile laser scanning point cloud nine categories 32 lyft level 5 https level5 lyft com dataset source post page include high quality human labelled 3d bounding boxes of traffic agents an underlying hd spatial semantic map 33 semantickitti http semantic kitti org sequential semantic segmentation 28 classes for autonomous driving all sequences of kitti odometry labeled iccv 2019 paper https arxiv org abs 1904 01416 34 npm3d http npm3d fr paris lille 3d the paris lille 3d has been produced by a mobile laser system mls in two different cities in france paris and lille 35 the waymo open dataset https waymo com open the waymo open dataset is comprised of high resolution sensor data collected by waymo self driving cars in a wide variety of conditions 36 a 3d an autonomous driving dataset in challeging environments https github com i2rdl2 astar 3d a 3d an autonomous driving dataset in challeging environments 37 pointda 10 dataset https github com canqin001 pointdan domain adaptation for point clouds 38 oxford robotcar https robotcar dataset robots ox ac uk the dataset captures many different combinations of weather traffic and pedestrians https github com openmvg awesome 3dreconstruction list sfm deep learning sfm rgb paper 1 unsupervised monocular depth estimation with left right consistency 2 unsupervised learning of depth and ego motion from video 3 deep ordinal regression network for monocular depth estimation 4 depth from videos in the wild 5 attention based context aggregation network for monocular depth estimation 6 depth map prediction from a single image using a multi scale deep network nips2014 7 predicting depth surface normals and semantic labels with a common multi scale convolutional architecture iccv2015 8 deeper depth prediction with fully convolutional residual networks 9 multi scale continuous crfs as sequential deep networks for monocular depth estimation cvpr2017 10 single view stereo matching project with code project paper framework 3dr2n2 a unified approach for single and multi view 3d object reconstruction https github com chrischoy 3d r2n2 eccv 2016 https arxiv org abs 1604 00449 theano learning a predictable and generative vector representation for objects https github com rohitgirdhar generativepredictablevoxels eccv 2016 https arxiv org abs 1603 08637 caffe learning a probabilistic latent space of object shapes via 3d generative adversarial modeling https github com zck119 3dgan release nips 2016 http 3dgan csail mit edu papers 3dgan nips pdf torch 7 perspective transformer nets learning single view 3d object reconstruction without 3d supervision https github com xcyan nips16 ptn nips 2016 https papers nips cc paper 6206 perspective transformer nets learning single view 3d object reconstruction without 3d supervision pdf torch 7 deep disentangled representations for volumetric reconstruction eccv 2016 https arxiv org pdf 1610 03777 pdf multi view 3d models from single images with a convolutional network https github com lmb freiburg mv3d eccv 2016 https lmb informatik uni freiburg de publications 2016 tdb16a paper mv3d pdf tensorflow single image 3d interpreter network https github com jiajunwu 3dinn eccv 2016 http 3dinterpreter csail mit edu papers 3dinn eccv pdf torch 7 weakly supervised generative adversarial networks for 3d reconstruction https github com jgwak mcrecon 3dv 2017 https arxiv org pdf 1705 10904 pdf theano hierarchical surface prediction for 3d object reconstruction https github com chaene hsp 3dv 2017 https arxiv org pdf 1704 00710 pdf torch 7 octree generating networks efficient convolutional architectures for high resolution 3d outputs https github com lmb freiburg ogn iccv 2017 https arxiv org pdf 1703 09438 pdf caffe multi view supervision for single view reconstruction via differentiable ray consistency https github com shubhtuls drc cvpr 2017 https arxiv org pdf 1704 06254 pdf torch 7 surfnet generating 3d shape surfaces using deep residual networks https github com sinhayan surfnet cvpr 2017 http openaccess thecvf com content cvpr 2017 papers sinha surfnet generating 3d cvpr 2017 paper pdf matlab a point set generation network for 3d object reconstruction from a single image https github com fanhqme pointsetgeneration cvpr 2017 http openaccess thecvf com content cvpr 2017 papers fan a point set cvpr 2017 paper pdf tensorflow o cnn octree based convolutional neural networks for 3d shape analysis https github com microsoft o cnn siggraph 2017 https wang ps github io o cnn files cnn3d pdf caffe rethinking reprojection closing the loop for pose aware shape reconstruction from a single image iccv 2017 https jerrypiglet github io pdf iccv2017 pdf scaling cnns for high resolution volumetric reconstruction from a single image iccv 2017 http openaccess thecvf com content iccv 2017 workshops papers w17 johnston scaling cnns for iccv 2017 paper pdf large scale 3d shape reconstruction and segmentation from shapenet core55 iccv 2017 https arxiv org pdf 1710 06104 pdf learning a hierarchical latent variable model of 3d shapes https github com lorenmt vsl 3dv 2018 https arxiv org pdf 1705 05994 pdf tensorflow learning efficient point cloud generation for dense 3d object reconstruction https github com ericlin79119 3d point cloud generation aaai 2018 https chenhsuanlin bitbucket io 3d point cloud generation paper pdf tensorflow deformnet free form deformation network for 3d shape reconstruction from a single image https github com jackd template ffd accv 2018 https jhonykaesemodel com papers learning ffd accv2018 camera ready pdf tensorflow image2mesh a learning framework for single image 3dreconstruction https github com jhonykaesemodel image2mesh accv 2018 https arxiv org pdf 1711 10669 pdf pytorch neural 3d mesh renderer https github com hiroharu kato mesh reconstruction cvpr 2018 https arxiv org pdf 1711 07566 pdf chainer multi view consistency as supervisory signal for learning shape and pose prediction https github com shubhtuls mvcsnp cvpr 2018 https arxiv org pdf 1801 03910 pdf torch 7 matryoshka networks predicting 3d geometry via nested shape layers https bitbucket org visinf projects 2018 matryoshka cvpr 2018 https arxiv org pdf 1804 10975 pdf pytorch atlasnet a papier m ch approach to learning 3d surface generation https github com thibaultgroueix atlasnet cvpr 2018 https arxiv org pdf 1802 05384 pdf pytorch pixel2mesh generating 3d mesh models from single rgb images https github com nywang16 pixel2mesh eccv 2018 http openaccess thecvf com content eccv 2018 papers nanyang wang pixel2mesh generating 3d eccv 2018 paper pdf tensorflow multiresolution tree networks for 3d point cloud processing https github com matheusgadelha mrtnet eccv 2018 https arxiv org pdf 1807 03520 pdf pytorch adaptive o cnn a patch based deep representation of 3d shapes siggraph asia 2018 https wang ps github io ao cnn files aocnn pdf learning implicit fields for generative shape modeling https github com czq142857 implicit decoder cvpr 2019 https arxiv org pdf 1812 02822 pdf tensorflow occupancy networks learning 3d reconstruction in function space https github com autonomousvision occupancy networks cvpr 2019 https arxiv org pdf 1812 03828 pdf pytorch deepsdf learning continuous signed distance functions for shape representation https github com hassony2 shape sdf cvpr 2019 https arxiv org pdf 1901 05103 pdf pytorch 3d lcd dlp dmd led 3d 3d https zhuanlan zhihu com p 29971801 https zhuanlan zhihu com p 29971801 https www baidu com link url nj4az5qbljqztzws4w1jgzfkyrn8ilk1ywb0rvi1 jr axeoldarahgyfwsncz9h wd eqid e120080700017e0b000000055e5d1f70 https www jianshu com p b0e030f3a522 https www jianshu com p c8d0afd817cc structured light 3d surface imaging a tutorial https link zhihu com target http 3a www rtbasics com downloads ieee structured light pdf http www opticsjournal net articles abstract aid oj9aea62c85c7ac99d real time structured light profilometry a review a state of the art in structured light patterns for surface profilometry phase shifting algorithms for fringe projection profilometry a review overview of the 3d profilometry of phase shifting fringe projection temporal phase unwrapping algorithms for fringe projection profilometry a comparative review lectures video 1 build your own 3d scanner optical triangulation for beginners https link zhihu com target http 3a mesh brown edu byo3d 2 https github com nikolaseu thesis https github com nikolaseu thesis 3 cs6320 3d computer vision spring 2015 http www sci utah edu gerig cs6320 s2015 cs6320 3d computer vision html 1 http www opticsjournal net articles abstract aid oj590c414efec735a 2 http www opticsjournal net articles abstract aid oj6ca613096af02c49 3 http www opticsjournal net articles abstract aid oj8214561beb0d211d 4 http www opticsjournal net articles abstract aid oj200109000160x5a8d0 5 http www opticsjournal net articles abstract aid oj180307000127tqwtzv 6 http www opticsjournal net articles abstract aid oj180908000212okrntq 7 http www opticsjournal net articles abstract aid oj180808000091urxu1w 8 http www opticsjournal net articles abstract aid oj180319000030hdjgmj 9 http www opticsjournal net articles abstract aid oj180808000039ahdkgm 10 http www opticsjournal net articles abstract aid oj170329000177szv2y5 11 http www opticsjournal net articles abstract aid oj091028000118b8dagd 1 one shot pattern projection for dense and accurate 3d acquisition in structured light 2 a single shot structured light means by encoding both color and geometrical features 3 dynamic 3d surface profilometry using a novel colour pattern encoded with a multiple triangular mode 4 review of single shot 3d shape measurement by phase calculation based fringe projection techniques 5 robust pattern decoding in shape coded structured light 3d 1 2 pattern codification strategies in structured light systems 3 binary coded linear fringes for three dimensional shape profiling 4 3d shape measurement based on complementary gray code light 5 phase shifting algorithms for fringe projection profilometry a review 6 overview of the 3d profilometry of phase shifting fringe projection 7 temporal phase unwrapping algorithms for fringe projection profilometry a comparative review 8 a multi frequency inverse phase error compensation method for projectornon linear in3d shape measurement 3d 3d 1 principles of shape from specular reflection 2 deflectometry 3d metrology from nanometer to meter 3 three dimensional shape measurement of a highly reflected specular surface with structured light method 4 three dimensional shape measurements of specular objects using phase measuring deflectometry 3d 3d eye in hand 3d ccd cmos 3d other papers 2 3 enhanced phase measurement profilometry for industrial 3d inspection automation https www researchgate net publication 273481900 enhanced phase measurement profilometry for industrial 3d inspection automation 4 profilometry of three dimensional discontinuous solids by combining two steps temporal phase unwrapping co phased profilometry and phase shifting interferometry http xueshu baidu com usercenter paper show paperid 3056d2277236112a708e4746a73e1e1d site xueshu se 5 360 degree profilometry of discontinuous solids co phasing projectors and camera 6 coherent digital demodulation of single camera n projections for 3d object shape measurement co phased profilometr 7 high speed 3d image acquisition using coded structured light projection https www researchgate net publication 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stripe matching for colour encoded structured light 14 absolute phase mapping for one shot dense pattern projection https www researchgate net profile joaquim salvi publication 224165341 absolute phase mapping for one shot dense pattern projection links 56ffaee708ae650a64f805dd pdf 15 3d digital stereophotogrammetry a practical guide to facial image acquisition 16 method and apparatus for 3d imaging using light pattern having multiple sub patterns 17 high speed laser three dimensional imager 18 three dimensional dental imaging method and apparatus having a reflective member 19 3d surface profile imaging method and apparatus using single spectral light condition 20 three dimensional surface profile imaging method and apparatus using single spectral light condition 21 high speed three dimensional imaging method 22 a hand held photometric stereo camera for 3 d modeling 23 high resolution real time 3d absolute coordinate measurement based on a phase shifting method 24 a fast three step 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permissive micmac https github com micmacign c cecill b mve https github com simonfuhrmann mve c bsd 3 clause license parts under the gpl 3 license openmvg https github com openmvg openmvg c mpl2 permissive opensfm https github com mapillary opensfm python simplified bsd license permissive theiasfm https github com sweeneychris theiasfm c new bsd license permissive tof tof tof 3d tof d tof 3d tof tof i tof i tof 1 tof https zhuanlan zhihu com p 51218791 2 3d tof https zhuanlan zhihu com p 85519428 paper 1 https arxiv org pdf 1511 07212 pdf multi view stereo multiple view stereo mvs slam sfm mvs pipelines 1 2 3 3d 4 mvs https zhuanlan zhihu com p 73748124 paper 1 learning inverse depth regression for multi view stereo with correlation cost volume https arxiv org pdf 1912 11746v1 pdf 2 cascade cost volume for high resolution multi view stereo and stereo matching http arxiv org abs 1912 06378v2 3 point based multi view stereo network http arxiv org abs 1908 04422v1 4 recurrent mvsnet for 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agrawal s tulsiani c mertz s lucey m hebert 2019 11 dpsnet end to end deep plane sweep stereo https openreview net pdf id ryeyhi0ctq sunghoon im hae gon jeon stephen lin in so kweon 2019 12 point based multi view stereo network http hansf me projects pmvsnet rui chen songfang han jing xu hao su iccv 2019 multiple view mesh texturing 1 seamless image based texture atlases using multi band blending http imagine enpc fr publications papers icpr08a pdf c all ne j p pons and r keriven icpr 2008 2 let there be color large scale texturing of 3d reconstructions http www gcc tu darmstadt de home proj texrecon m waechter n moehrle m goesele eccv 2014 texture mapping 1 3d textured model encryption via 3d lu chaotic mapping https arxiv org abs 1709 08364v1 courses image manipulation and computational photography http inst eecs berkeley edu cs194 26 fa14 alexei a efros uc berkeley computational photography http graphics cs cmu edu courses 15 463 2012 fall 463 html alexei a efros cmu computational photography https courses engr illinois edu cs498dh3 derek hoiem uiuc computational photography http cs brown edu courses csci1290 james hays brown university digital computational photography http stellar mit edu s course 6 sp12 6 815 fredo durand mit computational camera and photography http ocw mit edu courses media arts and sciences mas 531 computational camera and photography fall 2009 ramesh raskar mit media lab computational photography https www udacity com course computational photography ud955 irfan essa georgia tech courses in graphics http graphics stanford edu courses stanford university computational photography http cs nyu edu fergus teaching comp photo index html rob fergus nyu introduction to visual computing http www cs toronto edu kyros courses 320 kyros kutulakos university of toronto computational photography http www cs toronto edu kyros courses 2530 kyros kutulakos university of toronto computer vision for visual effects https www ecse rpi edu rjradke cvfxcourse html rich radke rensselaer polytechnic institute introduction to image processing https www ecse rpi edu rjradke improccourse html rich radke rensselaer polytechnic institute software matlab functions for multiple view geometry http www robots ox ac uk vgg hzbook code peter kovesi s matlab functions for computer vision and image analysis http staffhome ecm uwa edu au 00011811 research matlabfns index html opengv http laurentkneip github io opengv geometric computer vision algorithms minimalsolvers http cmp felk cvut cz mini minimal problems solver multi view environment http www gcc tu darmstadt de home proj mve visual sfm http ccwu me vsfm bundler sfm http www cs cornell edu snavely bundler openmvg open multiple view geometry http imagine enpc fr moulonp openmvg multiple view geometry structure from motion library softwares patch based multi view stereo v2 http www di ens fr pmvs clustering views for multi view stereo http www di ens fr cmvs floating scale surface reconstruction http www gris informatik tu darmstadt de projects floating scale surface recon large scale texturing of 3d reconstructions http www gcc tu darmstadt de home proj texrecon multi view stereo reconstruction http vision middlebury edu mview project code project language license colmap https github com colmap colmap c cuda bsd 3 clause license permissive can use cgal gnu general public license contamination gpuima fusibile https github com kysucix c cuda gnu general public license contamination hpmvs https github com alexlocher hpmvs c gnu general public license contamination micmac http logiciels ign fr micmac c cecill b mve https github com simonfuhrmann mve c bsd 3 clause license parts under the gpl 3 license openmvs https github com cdcseacave openmvs c cuda optional agpl3 pmvs https github com pmoulon cmvs pmvs c cuda gnu general public license contamination smvs shading aware multi view stereo https github com flanggut smvs c bsd 3 clause license kinect kinectfusion kintinuous elasticfusion infinitam bundlefusion 1 surface reconstruction from 3d line segments planar reconstruction https github com bigteacher 777 awesome planar reconstruction papers planercnn planercnn 3d plane detection and reconstruction from a single image cvpr2019 oral https arxiv org pdf 1812 04072 pdf pytorch https github com nvlabs planercnn planarreconstruction single image piece wise planar 3d reconstruction via associative embedding cvpr2019 https arxiv org pdf 1902 09777 pdf pytorch https github com svip lab planarreconstruction planerecover recovering 3d planes from a single image via convolutional neural networks eccv2018 https faculty ist psu edu zzhou paper eccv18 plane pdf tensorflow https github com fuy34 planerecover planenet planenet piece wise planar reconstruction from a single rgb image cvpr2018 https arxiv org pdf 1804 06278 pdf tensorflow https github com art programmer planenet datasets scannet dataset planenet train https drive google com file d 1nydrgi02ao18wmxyepgvkwgqtm3ys3 4 view test https drive google com file d 1kfd kregqqlsrnf66t447r9wgdqsth 3 view scannet dataset planercnn link https www dropbox com s u2wl4ji700u4shq scannet planes zip dl 0 nyu depth dataset link https cs nyu edu silberman datasets nyu depth v2 html synthia dataset link https psu app box com s 6ds04a85xqf3ud3uljjxnedmux169ebf 3d 1 nonlinear 3d face morphable model https arxiv org abs 1804 03786 2 on learning 3d face morphable model from in the wild images https arxiv org abs 1808 09560 3 cascaded regressor based 3d face reconstruction from a single arbitrary view image https arxiv org abs 1509 06161v1 4 jointface alignment and 3d face reconstruction http xueshu baidu com usercenter paper show paperid 4dcdab9e3941e82563f82009a2ef3125 site xueshu se 5 photo realistic facial details synthesis from single image https arxiv org pdf 1903 10873 pdf 6 fml face model learning from videos https arxiv org pdf 1812 07603 pdf 7 large pose 3d face reconstruction from a single image via direct volumetric https arxiv org abs 1703 07834 8 joint 3d face reconstruction and dense alignment with position map regression network https arxiv org pdf 1803 07835 pdf 9 joint 3d face reconstruction and dense face alignment from a single image with 2d assisted self supervised learning https arxiv org abs 1903 09359 10 face alignment across large poses a 3d solution 1 texture synthesis using convolutional neural networks 2015 paper https arxiv org pdf 1505 07376 pdf 2 two shot svbrdf capture for stationary materials siggraph 2015 paper https mediatech aalto fi publications graphics twoshotsvbrdf 3 reflectance modeling by neural texture synthesis 2016 paper https mediatech aalto fi publications graphics neuralsvbrdf 4 modeling surface appearance from a single photograph using self augmented convolutional neural networks 2017 paper http msraig info sanet sanet htm 5 high resolution multi scale neural texture synthesis 2017 paper https wxs ca research multiscale neural synthesis 6 reflectance and natural illumination from single material specular objects using deep learning 2017 paper https homes cs washington edu krematas publications reflectance natural illumination pdf 7 joint material and illumination estimation from photo sets in the wild 2017 paper https arxiv org pdf 1710 08313 pdf 8 texturegan controlling deep image synthesis with texture patches 2018 cvpr paper https arxiv org pdf 1706 02823 pdf 9 gaussian material synthesis 2018 siggraph paper https users cg tuwien ac at zsolnai gfx gaussian material synthesis 10 non stationary texture synthesis by adversarial expansion 2018 siggraph paper http vcc szu edu cn research 2018 texsyn 11 synthesized texture quality assessment via multi scale spatial and statistical texture attributes of image and gradient magnitude coefficients 2018 cvpr paper https arxiv org pdf 1804 08020 pdf 12 lime live intrinsic material estimation 2018 cvpr paper https gvv mpi inf mpg de projects lime 13 learning material aware local descriptors for 3d shapes 2018 paper http www vovakim com papers 18 3dv shapematfeat pdf 1 make it home automatic optimization of furniture arrangement 2011 siggraph paper http people sutd edu sg saikit projects furniture index html 2 interactive furniture layout using interior design guidelines 2011 paper http graphics stanford edu pmerrell furniturelayout htm 3 synthesizing open worlds with constraints using locally annealed reversible jump mcmc 2012 paper http graphics stanford edu lfyg owl pdf 4 example based synthesis of 3d object arrangements 2012 siggraph asia paper http graphics stanford edu projects scenesynth 5 sketch2scene sketch based co retrieval and co placement of 3d models 2013 paper http sweb cityu edu hk hongbofu projects sketch2scene sig13 wwwge ysb0 6 action driven 3d indoor scene evolution 2016 paper https www cs sfu ca haoz pubs ma siga16 action pdf 7 the clutterpalette an interactive tool for detailing indoor scenes 2015 paper https www cs umb edu craigyu papers clutterpalette pdf 8 relationship templates for creating scene variations 2016 paper http geometry cs ucl ac uk projects 2016 relationship templates 9 im2cad 2017 paper http homes cs washington edu izadinia im2cad html 10 predicting complete 3d models of indoor scenes 2017 paper https arxiv org pdf 1504 02437 pdf 11 complete 3d scene parsing from single rgbd image 2017 paper https arxiv org pdf 1710 09490 pdf 12 adaptive synthesis of indoor scenes via activity associated object relation graphs 2017 siggraph asia paper http arts buaa edu cn projects sa17 13 automated interior design using a genetic algorithm 2017 paper https publik tuwien ac at files publik 262718 pdf 14 scenesuggest context driven 3d scene design 2017 paper https arxiv org pdf 1703 00061 pdf 15 a fully end to end deep learning approach for real time simultaneous 3d reconstruction and material recognition 2017 paper https arxiv org pdf 1703 04699v1 pdf 16 human centric indoor scene synthesis using stochastic grammar 2018 cvpr paper http web cs ucla edu syqi publications cvpr2018synthesis cvpr2018synthesis pdf supplementary http web cs ucla edu syqi publications cvpr2018synthesis cvpr2018synthesis supplementary pdf code https github com siyuanqi human centric scene synthesis 17 floornet a unified framework for floorplan reconstruction from 3d scans 2018 paper https arxiv org pdf 1804 00090 pdf code http art programmer github io floornet html 18 scancomplete large scale scene completion and semantic segmentation for 3d scans 2018 paper https arxiv org pdf 1712 10215 pdf 19 configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars 2018 paper https arxiv org pdf 1704 00112 pdf 20 holistic 3d scene parsing and reconstruction from a single rgb image eccv 2018 paper http siyuanhuang com holistic parsing main html 21 automatic 3d indoor scene modeling from single panorama 2018 cvpr paper http openaccess thecvf com content cvpr 2018 papers yang automatic 3d indoor cvpr 2018 paper pdf 22 single image piece wise planar 3d reconstruction via associative embedding 2019 cvpr paper https arxiv org pdf 1902 09777 pdf code https github com svip lab planarreconstruction 23 3d scene reconstruction with multi layer depth and epipolar transformers iccv 2019 paper https research dshin org iccv19 multi layer depth rgb rgb d keypoint based dense correspondence 1 labelfusion https github com robotlocomotion labelfusion 6d dnn 1 discriminative mixture of templates for viewpoint classification 2 gradient response maps for realtime detection of textureless objects 3 comparing images using the hausdorff distance 4 densefusion 6d object pose estimation by iterative dense fusion 5 posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes 6 viewpoints and keypoints 7 implicit 3d orientation learning for 6d object detection from rgb images 8 render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views keypoint based pnp 6d 1 surf speeded up robust features 2 object recognition from local scaleinvariant features 3 3d object modeling and recognition using local affine invariant image descriptors and multi view spatial constraints 4 stacked hourglass networks for human pose estimation 5 making deep heatmaps robust to partial occlusions for 3d object pose estimation 6 bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth 7 real time seamless single shot 6d object pose prediction 8 discovery of latent 3d keypoints via end toend geometric reasoning 9 pvnet pixel wise voting network for 6dof pose estimation dense correspondence and 1 independent object class detection using 3d feature maps 2 depthencoded hough voting for joint object detection and shape recovery 3 aware object detection and pose estimation 4 learning 6d object pose estimation using 3d object coordinates 5 global hypothesis generation for 6d object pose estimation 6 deep learning of local rgb d patches for 3d object detection and 6d pose estimation 7 cdpn coordinates based disentangled pose network for real time rgb based 6 dof object pose estimation 8 pix2pose pixel wise coordinate regression of objects for 6d pose estimation 9 normalized object coordinate space for categorylevel 6d object pose and size estimation 10 recovering 6d object pose and predicting next bestview in the crowd 11 1 pointfusion 2 frustum pointnets 3 votenet rgb rgb d 1 survey on visual servoing for manipulation http xueshu baidu com usercenter paper show paperid ec62933c2dc0edd1c24ba39d2e28d9f4 site xueshu se 2 a review on vision based control of robot manipulators 2d 1 kinematics based incremental visual servo for robotic capture of non cooperative target https www baidu com link url bppedlcj7n1gr2u8fdlvnjz0hzrl15k0olrxzpjpyzppzelq79znoqkbno7cg9cdth2fmc c wbgc1xjxfrjdacdbkiztlvvifj1fgnbfvilu ur8gakybpywphgnxfxasqskf5napfdvamsz7z4a6yky2aqay1dqf2ldxbyxpaisboov2pibj7twb73kraikcfcpu2zvodrzvhg0rgm9k wd eqid f29332170003bec5000000055e59ff3f 2 position and attitude control of eye in hand system by visual servoing using binocular visual space http xueshu baidu com usercenter paper show paperid 1788c32af8c3c191062de46f599fcf55 site xueshu se 3 progressive 3d reconstruction of unknown objects using one eye in hand camera 4 3d 1 a hybrid positioning method for eye in hand industrial robot by using 3d reconstruction and ibvs http xueshu baidu com usercenter paper show paperid 233c5ff9ae58ec24cc2d9572af577412 site xueshu se 2 5d 1 moment based 2 5 d visual servoing for textureless planar part grasping https ieeexplore ieee org document 8584462 1 hms net hierarchical multi scale sparsity invariant network for sparse depth completion https arxiv org abs 1808 08685 2 sparse and noisy lidar completion with rgb guidance and uncertainty https arxiv org abs 1902 05356 3 3d lidar and stereo fusion using stereo matching network with conditional cost volume normalization https arxiv org pdf 1904 02917 pdf 4 deep rgb d canonical correlation analysis for sparse depth completion https arxiv org pdf 1906 08967 pdf 5 confidence propagation through cnns for guided sparse depth regression https arxiv org abs 1811 01791 6 learning guided convolutional network for depth completion https arxiv org pdf 1908 01238 pdf 7 dfinenet ego motion estimation and depth refinement from sparse noisy depth input with rgb guidance http arxiv org abs 1903 06397 8 plin a network for pseudo lidar point cloud interpolation https arxiv org abs 1909 07137 9 depth completion from sparse lidar data with depth normal constraints https arxiv org pdf 1910 06727v1 pdf kinect paper 1 kinect 2 github 1 https github com dovamir awesome design patterns | ai |
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CHP015-Unity-step-by-step- | chp015 unity step by step unity in embedded system design and robotics a step by step guide | os |
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coding-conventions | coding conventions this repository hosts the coding conventions that will be used during the courses informatica and information technology | coding conventions | server |
CapstoneDesign | capstonedesign 1 https github com gydnjsdl426 capstonedesign assets 51254788 b01fca3a 4311 437e adba 3474b6498171 2 https github com gydnjsdl426 capstonedesign assets 51254788 652d648e 948d 40bb aa87 d2e115b0f794 3 https github com gydnjsdl426 capstonedesign assets 51254788 43f82caf e859 4f15 9c8b 8f528f0c1926 4 https github com gydnjsdl426 capstonedesign assets 51254788 9209bc1f 2f47 4c2b 9fbb ea15529a6785 5 https github com gydnjsdl426 capstonedesign assets 51254788 785bb902 2d05 4397 85a2 77d493998884 7 https github com gydnjsdl426 capstonedesign assets 51254788 e904dd50 dce5 45bd 895d f541cddcc552 8 https github com gydnjsdl426 capstonedesign assets 51254788 24059678 f9b6 45d4 af89 e730280f2ac1 9 https github com gydnjsdl426 capstonedesign assets 51254788 84861e50 c933 47a1 b2e9 6fe6dd17de68 10 https github com gydnjsdl426 capstonedesign assets 51254788 3bfc46a4 561d 46b1 9b39 87314bf248de 11 https github com gydnjsdl426 capstonedesign assets 51254788 39826d72 7349 4512 abcf 3763b9ed7d28 12 https github com gydnjsdl426 capstonedesign assets 51254788 95fec21e 2280 4c63 aa6a 2fdc658d8dc1 13 https github com gydnjsdl426 capstonedesign assets 51254788 7e335f89 ef2f 4fab b251 b843dbdd3905 14 https github com gydnjsdl426 capstonedesign assets 51254788 9c90c8a9 b8ba 4f95 8aaa 48d8388e887d | os |
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blockchain-tutorial | go go https github com jeiwan blockchain go building blockchain in go https jeiwan cc posts building blockchain in go part 1 part 1 basic prototype part 2 proof of work part 3 cli persistence and cli part 4 1 transactions1 part 5 addresses part 6 2 transactions2 part 7 network part 1 basic prototype https jeiwan cc posts building blockchain in go part 1 part 2 proof of work https jeiwan cc posts building blockchain in go part 2 part 3 persistence and cli https jeiwan cc posts building blockchain in go part 3 part 4 transactions 1 https jeiwan cc posts building blockchain in go part 4 part 5 addresses https jeiwan cc posts building blockchain in go part 5 part 6 transactions 2 https jeiwan cc posts building blockchain in go part 6 part 7 network https jeiwan cc posts building blockchain in go part 7 contribution https github com mingrammer blockchain tutorial pr | blockchain tutorial golang | blockchain |
HAMTech | hamtech ham information technology | server |
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Camerafeed | camerafeed a simple app or can be used as a module to track people as they move in a video you can also draw triplines that will emit events when a person crosses them you can use the camerafeed module in your own app or you can run it as a stand alone app that pulls from settings ini getting started 1 first things first you gotta install python 3 4 and opencv 3 0 luckily computer vision expert adrian has made his fantastic tutorials available http www pyimagesearch com opencv tutorials resources guides 2 clone the repo bash git clone git github com liquidg3 camerafeed git 3 jump into the repo bash cd camerafeed 4 install additional dependencies bash pip setup py develop 5 run the app bash python run py settings checkout settings ini for everything you can customize when running camerafeed as an app ini video source the video to load put 0 to use your computer s camera source footage sample trimmed m4v ini the following are for cropping the video try and only show parts you need frame x1 250 frame y1 40 frame x2 550 frame y2 240 ini set a max width and the video will be proportionality scaled to this size smaller is usually better max width 500 ini black and white b and w false ini settings for hog detectmultiscale hog win stride 4 padding 6 scale 1 05 ini use background removal mog enabled false ini when tracking a person person life 20 how many frames to wait before considering the person gone max distance 50 how far can a person move between detections ini for crossing lines triplines total lines 1 line1 start 100 60 line1 end 200 160 line1 buffer 10 line1 direction 1 north line1 direction 2 south ini line2 start 50 180 line2 end 200 150 line2 buffer 10 line2 direction 1 out line2 direction 2 in recommended reading 1 pedestrian detection opencv http www pyimagesearch com 2015 11 09 pedestrian detection opencv 2 hog detectmultiscale parameters explained http www pyimagesearch com 2015 11 16 hog detectmultiscale parameters explained | ai |
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instagram | project 4 instagram instagram is a photo sharing app using parse as its backend time spent 14 hours spent in total user stories the following required functionality is completed x user can sign up to create a new account using parse authentication x user can log in and log out of his or her account x the current signed in user is persisted across app restarts x user can take a photo add a caption and post it to instagram x user can view the last 20 posts submitted to instagram x user can pull to refresh the last 20 posts submitted to instagram x user can tap a post to view post details including timestamp and caption the following optional features are implemented x run your app on your phone and use the camera to take the photo x user can load more posts once he or she reaches the bottom of the feed using infinite scrolling x show the username and creation time for each post x user can use a tab bar to switch between a home feed tab all posts and a profile tab only posts published by the current user user profiles x allow the logged in user to add a profile photo x display the profile photo with each post x tapping on a post s username or profile photo goes to that user s profile page after the user submits a new post show a progress hud while the post is being uploaded to parse user can comment on a post and see all comments for each post in the post details screen user can like a post and see number of likes for each post in the post details screen style the login page to look like the real instagram login page style the feed to look like the real instagram feed implement a custom camera view the following additional features are implemented x alerts appear in login screen containing specific error messages if login sign up fails x post timestamps are formatted similar to twitter please list two areas of the assignment you d like to discuss further with your peers during the next class examples include better ways to implement something how to extend your app in certain ways etc 1 autolayout in collection views 2 how to use navigation controllers correctly video walkthrough here s a walkthrough of implemented user stories instagram gif 1 gif instagram gif 2 gif instagram gif 3 gif instagram gif 4 gif instagram gif 5 gif instagram gif 6 gif gif created with kap https getkap co credits list an 3rd party libraries icons graphics or other assets you used in your app afnetworking https github com afnetworking afnetworking networking task library notes describe any challenges encountered while building the app license copyright 2021 marin hyatt licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | os |
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people_database | peopledatabase a database to keep track of all the people in engineering it will include users departments this is a requirement for almost every other thing we do so it is being done first | server |
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AJ | aj it information technology | server |
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htoolv3 | htool project ios macos reverse engineering tool htool macos https github com h3adshotzz htoolv3 actions workflows main macos yml badge svg branch working htool linux https github com h3adshotzz htoolv3 actions workflows main linux yml badge svg branch working htool is a command line static analysis and reverse engineering tool supporting mach o binaries and a range of ios macos and other darwin based os firmware files htool provides a mach o parser firmware file analyser and disassembler htool inherits much of it s mach o functionality from libhelper while a new library libarch was developed primarily for htool to provide it a small lightweight and extremely fast arm aarch64 disassembler htool is a closed source project license under the is this on confidential and proprietary license v1 with the development primarily at this stage for the bachelor of science degree in computer science for myself | os |
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embedded-systems | embedded systems codes and design | os |
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udacity_pandas_postgres | discuss the purpose of this database in the context of the startup sparkify and their analytical goals the purpose of this database is to be able to analyse how sparkify is being used by it s users it has been decided to transform the data and load it into an postgresql database as traversing directories and doing analysis on json text files is not the easiest and especially as any analysis code would be tied to this storage format which could easily change in a few years with this information easily available it will be possible for them to maybe tailor their user interface for their users depending on location or even build recomended playlists based on the user s previous listening activity the company also wants a central place to store all this information as keeping the information in different directories can become cumbersome and hard to maintain state and justify your database schema design and etl pipeline we have a fact table called songplays and dimension tables named users artists songs and time songplays has been used as a fact table as the main entity event the company is interested in is the actual streaming of a song a songplay this will make it very quick to execute queries on song streams and only require joins if additional information such as artist song name are needed python with pandas was used in the etl as it makes it very quick to select filter out certain information example queries check the most played songs in order select song id count song id from songplays group by song id order by count song id desc check the most played artists select artist id count artist id from songplays group by artist id order by count artist id desc running the project run script create tables py run script etl py create tables py will drop any existing tables and crecreate the table to give you a fresh environment etl py script will then traverse the log data and song data directories and transform the data from the json files contained in these directories into the postgresql database tables we have created and populate them | server |
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uwappdev.github.io | uwappdev github io the website for the app development rso at the university of washington pull request guidelines students submitting pull requests should include a clear description of what was changed and why if a new page was added to the website please give yourself credit running locally this site uses jekyll https jekyllrb com to run it locally 1 read through the jekyll quickstart https jekyllrb com docs 2 update config yml by commenting out remote theme and uncommenting theme hematite 3 follow the jekyll site building instructions https jekyllrb com docs step by step 01 setup build to build and serve the site locally if you get an error mentioning webrick please run bundle add webrick | hacktoberfest hacktoberfest2021 flutter | front_end |
edafa | a href https www buymeacoffee com khalel target blank img src https cdn buymeacoffee com buttons v2 default yellow png alt buy me a coffee style height 60px important width 217px important a edafa github https img shields io github license mashape apistatus svg contributions welcome https img shields io badge contributions welcome brightgreen svg style flat https github com andrewekhalel edafa issues br edafa is a simple wrapper that implements test time augmentations tta on images for computer vision problems like segmentation classification super resolution pansharpening etc ttas guarantees better results in most of the tasks test time augmentation tta applying different transformations to test images and then average for more robust results pipeline https preview ibb co kh61v0 pipeline png installation shell pip install edafa getting started the easiest way to get up and running is to follow example notebooks https github com andrewekhalel edafa tree master examples for segmentation and classification showing tta effect on performance how to use edafa the whole process can be done in 4 steps 1 import predictor class based on your task category segmentation segpredictor or classification classpredictor python from edafa import segpredictor 2 inherit predictor class and implement the main function predict patches self patches where your model takes image patches numpy ndarray and return prediction numpy ndarray python class mypredictor segpredictor def init self model args kwargs super init args kwargs self model model def predict patches self patches return self model predict patches 3 create an instance of you class python p mypredictor model patch size model output channels conf file path 4 call predict images to run the prediction process python p predict images images overlap 0 configuration file configuration file is a json file containing two pieces of information 1 augmentations to apply augs supported augmentations no no augmentation rot90 rotate 90 degrees rot180 rotate 180 degrees rot270 rotate 270 degrees flip ud flip upside down flip lr flip left right bright change image brightness randomly contrast change image contrast randomly gaussian add random gaussian noise gamma perform gamma correction with random gamma 2 combination of the results mean supported mean types arith arithmetic mean geo geometric mean 3 number of bits image default is 8 bits bits example of a conf file in json format json augs no flip ud flip lr mean arith bits 8 example of a conf file in yaml format yaml augs no flip ud flip lr mean arith bits 8 you can either pass file path json or yaml or the actual json text to conf parameter contribution all contributions are welcomed please make sure that all tests passed before pull request to run tests shell nosetests | augmentation computer-vision segmentation super-resolution tensorflow keras pytorch classification wrapper | ai |
DU-DamageReport | development ended standard view https github com dorianthegrey du damagereport blob main img dr logo1 png damage report v3 31 du damagereport a multi screen and hud capable touch enabled easy to install ship damage reporting script for dual universe find that one damaged element faster have an eye on the elements of your ship which are hidden by honeycomb always know what has been damaged after hitting that tower due to lag near a marketplace know your fuel situation highlight damaged or broken elements in 3d space or find them in ship outline views customize all colors and background to fit your bridge colors and style note the linking order is important or your touchscreen will not work see installation instructions below recommendation run it with only one screen or two because otherwise you will likely run into script cpu usage problems before optimizations are done very soon we will be linking a youtube video here explaning the installation and usage of the script in detail for now please see the installation and usage description below the following screenshots as well as a note about the roadmap and known issues created by dorian gray thanks to bayouking1 and kalazzerx for managing their forks of this script during my long absence to support the community also thanks to bayouking1 for fixing rocket fuel calculations thanks to novaquark for creating the best mmo of the century thanks to jericho dmentia and archaegeo from du open source initiative for learning a lot about du lua from their fine scripts thanks to theblacklist for his testing and his many wonderful suggestions svg patterns by hero patterns du atlas data from jayle break 1a https github com dorianthegrey du damagereport blob main img 1a png 1 https github com dorianthegrey du damagereport blob main img 1 png 2 https github com dorianthegrey du damagereport blob main img 2 png 3 https github com dorianthegrey du damagereport blob main img 3 png 4 https github com dorianthegrey du damagereport blob main img 4 png 5 https github com dorianthegrey du damagereport blob main img 5 png 6 https github com dorianthegrey du damagereport blob main img 6 png important notes this script is comparably intense in regards to du cpu resources required using many screens using it on a ship with many 1000 elements clicking rapidly on the settings page and or having many damaged elements will most likely cause a script shutdown due to the limited cpu time we get using less screens and clicking a tiny bit slower during color selection will help for now i am not limiting the number of screens you can use up to 8 but you will see that 1 3 screens works a lot smoother than more finally all scripts you run in parallel share one cpu limit so i advise to switch this script off while you are using e g a heavy flight hud script you have been warned having said all of this i successfully used the script on a 1100 element l core ship on one screen without problems installation 1 you need a ship aka a dynamic core a databank and a screen or two three 2 place a progamming board and a databank on your ship 3 link the programming board to your ships core then link the programming board to the databank then link the programming board to your screen you can link it to more than one screens but i highly recommend you start with 1 3 screens only as adding more screens will most probably will make you run into script shutdowns due to the cpu limit 4 optionally you can run the script without connecting any screens at all but you will only be able to use the hud mode and will miss out on most features of the script 5 copy the latest config file of https github com dorianthegrey du damagereport it s called damagereport x xx conf click on the file click on raw copy everything this is the latest file https raw githubusercontent com dorianthegrey du damagereport main damagereport 3 31 conf 6 rightclick on your programming board advanced paste lua configuation from clipboard 7 activate programming board 8 rightclick on your programming board advanced edit lua parameters here you can enter the name of your ship do not remove the quotation marks 9 still in the edit lua parameters see 8 please set your talents according to the name description of the talents you see the description if you hover with your mouse over the name please note that any line item marked as skill requires you to enter your own level of that type while stat requires you to enter the level of the specific item that has been placed on your ship either by you or the builder if you do not set up your talents stats or don t set them up correctly the fuel mode will not show you correct data likewise the scrap calculations will not be correct usage very soon we will be linking a youtube video here explaning the installation and usage of the script in detail activate the script at the programming board not through a detection zone or a switch du prevents huds to be drawn to players that haven t activated a script through either a programming board or a control seat the script will work you will just not be able to use the hud mode also please rememeber to set your talents correctly in the edit lua parameters rightclick programming board advanced edit lua parameters also check disallowkeypresses if you want damage report to process any keypresses this will limit the usability of your hud mode significantly but if you prever to use the keys for other scripts this is a solution you can always toggle it back on if you have a bigger repair task ahead of you the bottom row shows several modes you can switch the screen into 1 time will display the time on this screen as long as the script runs 2 dmg this mode displays the damaged and broken elements of your ship should there be any clicking on the scrap tier in the header switches through scrap tiers 1 to 4 to calculate your repair time and amount of specific scraps needed please set your skills in the settings or it will not show correct data clicking on damage name or broken name will turn the display to use element types instead of names should you prefer that clicking on damage type or broken type will then turn the display back again to use names instead 3 dmgo this will draw a ship outline based on the position of the elements of your ship you can switch the screen into top side or front views 4 fuel this will show the fuel tank situation on your ship please set your skills in the settings or it will not show correct data on the right side there s two additional buttons 5 sets this is the settings page of the script via touchscreen you are able to change all colors and the background as well as background opacity left side lastly you can also activate simulate damage to elements so the script assumes certain damage on your ship s elements that way you can look at all the displays and the hud how they look like when your ship took damage without having to crash your ship please note that all settings you change will be saved in the databank 6 hud this activates the hud mode the hud mode is an overlay on your ui displaying certain damage data the hud mode is not connected to any of the 4 main modes 1 to 4 but is always the same while using the hud you will also see certain buttons you can use to control the script left arrow will toggle the hud display that s the same than you clicking the hud button on a screen up down arrows will select a damaged broken element from your list should you have broken elements selected elements will be highlighted in 3d space with arrows so you can easier find them right arrow will deselect the selected element ctrl arrows will move the hud on your screen so you can place it whereever you want alt 8 will reset the hud back to it s original position alt 1 alt 2 alt 3 and alt 4 will select the corresponding scrap tier level for the repair time and needed scrap calculations alt 9 will shutdown the script this is useful if e g you still had your script running because you repaired the ship and then want to move away it s always better to manually shut it off than simply running out of range as the screens will be cleared in general just play around with the script a bit you ll get the hang of it quickly roadmap there s plenty of bullet points on the roadmap but as of now i am only focusing on optimizing the code so you will run into less cpu shutdown issues and or will be able to run more screens in parallel so i am working on integrating coroutines 3d projection system known issues 1 you will run into script shutdowns due to the cpu usage use less screens to have a higher chance of not running into this problem optimizations are being worked on but you can only go so far with the limited processing time we get if this is too much of a problem for you at the current stage please use version 2 33 instead in versions 2 more time date formats are coming soon 3 your touchscreen is not working first make sure you are not holding a tool in your hand when trying to click don t laugh it happened to all of us if that is not the problem you most probably linked the elements in a wrong order as in the installation 1 programming board to core 2 programming board to databank 3 programming board to screen 4 paste conf into programming board under gnu public license 3 0 | front_end |
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MAD | mad mobile application developement using android lab work wink up till week 5 android studio 3 5 week 6 and later android studio 3 6 select week week 1 helloworld counter week 1 week 2 register color week 2 week 3 currency lifecycle week 3 week 4 atm spin week 4 week 5 calculator week 5 week 6 implicit explicit week 6 week 7 share video week 7 week 1 1 helloworld activity main xml https github com nizam19 mad blob master week1 helloworld app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week1 helloworld app src main java com example helloworld mainactivity java 2 counter activity main xml https github com nizam19 mad blob master week1 counter app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week1 counter app src main java com example counter mainactivity java week 2 1 register activity main xml https github com nizam19 mad blob master week2 register app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week2 register app src main java com example register mainactivity java 2 color activity main xml https github com nizam19 mad blob master week2 color app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week2 color app src main java com example color mainactivity java week 3 1 currency activity main xml https github com nizam19 mad blob master week3 currency app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week3 currency app src main java com example currency mainactivity java 2 lifecycle activity main xml https github com nizam19 mad blob master week3 lifecycle app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week3 lifecycle app src main java com example lifecycle mainactivity java week 4 1 atm activity main xml https github com nizam19 mad blob master week4 atm app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week4 atm app src main java com example atm mainactivity java 2 spin activity main xml https github com nizam19 mad blob master week4 spin app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week4 spin app src main java com example spin mainactivity java week 5 1 calculator activity main xml https github com nizam19 mad blob master week5 calculator app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week5 calculator app src main java com example calculator mainactivity java week 6 1 implicit activity main xml https github com nizam19 mad blob master week6 implicit app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week6 implicit app src main java com example implicit mainactivity java 2 explicit activity main xml https github com nizam19 mad blob master week6 explicit app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week6 explicit app src main java com example explicit mainactivity java activity second xml https github com nizam19 mad blob master week6 explicit app src main res layout activity second xml secondactivity java https github com nizam19 mad blob master week6 explicit app src main java com example explicit secondactivity java week 7 1 share activity main xml https github com nizam19 mad blob master week7 share app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week7 share app src main java com example share mainactivity java 2 video activity main xml https github com nizam19 mad blob master week7 video app src main res layout activity main xml mainactivity java https github com nizam19 mad blob master week7 video app src main java com example video mainactivity java | front_end |
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go-opera | opera evm compatible chain secured by the lachesis consensus algorithm building the source building opera requires both a go version 1 14 or later and a c compiler you can install them using your favourite package manager once the dependencies are installed run shell make opera the build output is build opera executable running opera going through all the possible command line flags is out of scope here but we ve enumerated a few common parameter combos to get you up to speed quickly on how you can run your own opera instance launching a network you will need a genesis file to join a network which may be found in https github com fantom foundation lachesis launch launching opera readonly non validator node for network specified by the genesis file shell opera genesis file g configuration as an alternative to passing the numerous flags to the opera binary you can also pass a configuration file via shell opera config path to your config toml to get an idea how the file should look like you can use the dumpconfig subcommand to export your existing configuration shell opera your favourite flags dumpconfig validator new validator private key may be created with opera validator new command to launch a validator you have to use validator id and validator pubkey flags to enable events emitter shell opera nousb validator id your id validator pubkey 0xyour pubkey opera will prompt you for a password to decrypt your validator private key optionally you can specify password with a file using validator password flag participation in discovery optionally you can specify your public ip to straighten connectivity of the network ensure your tcp udp p2p port 5050 by default isn t blocked by your firewall shell opera nat extip 1 2 3 4 dev running testnet the network is specified only by its genesis file so running a testnet node is equivalent to using a testnet genesis file instead of a mainnet genesis file shell opera genesis path to testnet g launch node it may be convenient to use a separate datadir for your testnet node to avoid collisions with other networks shell opera genesis path to testnet g datadir path to datadir launch node opera datadir path to datadir account new create new account opera datadir path to datadir attach attach to ipc testing lachesis has extensive unit testing use the go tool to run tests shell go test if everything goes well it should output something along these lines ok github com fantom foundation go opera app 0 033s github com fantom foundation go opera cmd cmdtest no test files ok github com fantom foundation go opera cmd opera 13 890s github com fantom foundation go opera cmd opera metrics no test files github com fantom foundation go opera cmd opera tracing no test files github com fantom foundation go opera crypto no test files github com fantom foundation go opera debug no test files github com fantom foundation go opera ethapi no test files github com fantom foundation go opera eventcheck no test files github com fantom foundation go opera eventcheck basiccheck no test files github com fantom foundation go opera eventcheck gaspowercheck no test files github com fantom foundation go opera eventcheck heavycheck no test files github com fantom foundation go opera eventcheck parentscheck no test files ok github com fantom foundation go opera evmcore 6 322s github com fantom foundation go opera gossip no test files github com fantom foundation go opera gossip emitter no test files ok github com fantom foundation go opera gossip filters 1 250s github com fantom foundation go opera gossip gasprice no test files github com fantom foundation go opera gossip occuredtxs no test files github com fantom foundation go opera gossip piecefunc no test files ok github com fantom foundation go opera integration 21 640s also it is tested with fuzzing fuzzing md operating a private network fakenet fakenet is a private network optimized for your private testing it ll generate a genesis containing n validators with equal stakes to launch a validator in this network all you need to do is specify a validator id you re willing to launch pay attention that validator s private keys are deterministically generated in this network so you must use it only for private testing maintaining your own private network is more involved as a lot of configurations taken for granted in the official networks need to be manually set up to run the fakenet with just one validator which will work practically as a poa blockchain use shell opera fakenet 1 1 to run the fakenet with 5 validators run the command for each validator shell opera fakenet 1 5 first node use 2 5 for second node if you have to launch a non validator node in fakenet use 0 as id shell opera fakenet 0 5 after that you have to connect your nodes either connect them statically or specify a bootnode shell opera fakenet 1 5 bootnodes enode verylonghex 1 2 3 4 5050 running the demo for the testing purposes the full demo may be launched using shell cd demo start sh start the opera processes stop sh stop the demo clean sh erase the chain data check readme md in the demo directory for more information | abft lachesis byzantine-fault-tolerance evm blockchain-network | blockchain |
Real-time-Bluetooth-Networks | real time bluetooth networks programming assignments related to the course real time bluetooth networks utaustinx ut rtbn 12 01x at edx the final project was to develop a bluetooth controlled fitness device link https youtu be ebxb1p4wd2o | realtime bluetooth tm4c123 rtos | os |
Thera | welcome to thera https github com alibaba thera wiki readme cn build status https travis ci org alibaba thera svg branch master https travis ci org alibaba thera https img alicdn com tps tb1mei7ovxxxxxcxxxxxxxxxxxx 1024 460 png thera is a develop tool aiming to improve the developing experience of hybrid mobile app which use weex luaview react native solition it is built on top of atom thera is founded by alibaba and managed by the open source community we embrace open sources hoping you join us you can follow us on twitter fire a bug on github issues vote for new features and make pull requests use thera to develop hybrid apps you can use weex react native to develop hybrid apps we choose weex to introduce its features project navigator on the left after created a project you can navigate files in the navigation bar generally there are scripts project description thera test files and resources editor in the middle thera supports both hint content assist grammar and fast find key location property panel on the right inspector dynamic view properties information on editing console at the bottom you can check console log in logcat and thera status in status besides performance use fps memory load time inspector to locate performance issue team work integrate with test tools and cloud service automatically test data mock code management etc alt text https img alicdn com tps tb1xiklovxxxxb apxxxxxxxxxx 1903 1133 png quick start guide this guide introduces how to create a new project and run it on simulator or device step 1 create a new project follow the video below little red ridning hood https gw alicdn com tps tb1pbiiovxxxxblafxxxxxxxxxx 1223 674 png https vimeo com 206175744 create a project click to watch after install thera click start a new thera project for weex to create a weex project set the project name thera will create a main we file as the entrance of the project set the project location where the project is located note the project location should be able to access otherwise creating will fail you can check recent created projects in the project navigator and remove them in folder several files will be created automatically after crating project thera recommend you using main we as the project entrance file using mock json to mock local data and build directory to save weex transforming result you can modify the settings in the thera project configuration file thera launch json some files are used by thera and cloud service you can ignore them step 2 edit please follow the video little red ridning hood https img alicdn com tps tb1gzoyovxxxxb4xvxxxxxxxxxx 1223 674 png https vimeo com 206176073 edit click to watch context dependent code completion code highlight of weex vue realtime hint when editing weex based on linter note use we file extension or specify weex file format to enable features above step 3 run please follow the video little red ridning hood https img alicdn com tps tb1qsmfovxxxxbrxvxxxxxxxxxx 1223 674 jpg https vimeo com 206177328 run time click to watch thera will detect local simulators after click run button thera will start corresponding simulator and install oreo preview app for you to check result on real time thera will start a local delegate server named dumpling in order to make connection between thera and preview app dumpling use local port 7001 by default you can change it in thera configuration when multiple device connected to thera you can choose one device simulator or real device at a time to check variables properties performance note please make sure you have installed simulators we recommend you check simulators on xcode genymotion first and restart thera if simulators cannot be recognized because starting simulator at first time may fail installing macos download the latest thera release https github com alibaba thera releases windows linux the new skyscraper is still under construction building from source guide to build from source link https github com alibaba thera wiki how to build macos macos only thera packages https github com therapackages is our packages contribution guide please follow our contribution guide https github com alibaba thera wiki contribution guide restrictions vue 2 0 is not supported for now pmc members committers dean wu xiaoshu wb at tmall com deng xu dennis dx at tmall com guo miaoyou miaoyou gmy at tmall com lin xueqiu xueqiu lxq at tmall com hu chun shiji hc at tmall com license mit https github com alibaba thera blob master license md f q portal https github com alibaba thera wiki f q | ide mobile-hybrid mobile hybrid thera weex luaview react-native android ios | front_end |
Finansal-Bilgi-Teknolojileri | finansal bilgi teknolojileri finansal bilgi teknolojileri proje devi | server |
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AI-Blocks | ai blocks logo png a powerful and intuitive wysiwyg interface that allows anyone to create machine learning models img src https raw githubusercontent com mrnothing ai blocks master screenshots sc5 png width 800 the concept of ai blocs is to have a simple scene with draggable objects that have scripts attached to them the model can be run directly on the editor or be exported to a standalone script that runs on tensorflow img src https raw githubusercontent com mrnothing ai blocks master screenshots sc2 png width 500 variables are parsed from python scripts and can be edited from the ai blocs properties panel img src https raw githubusercontent com mrnothing ai blocks master screenshots sc3 png width 500 install the project requires python https www python org and tensorflow https www tensorflow org to run projects you can still create and edit projects without these dependencies to run ai blocs download the project archive and launch ai blocs exe to run your model simply press the play button and let the magic happen img src https raw githubusercontent com mrnothing ai blocks master screenshots sc6 png download all releases can be found here https github com mrnothing ai blocks releases build and run first type npm install to run the project from the sources npm run test to build the project from the sources npm run pack documentation documentation and video tutorials can be found on the website https mrnothing github io ai blocks index html license the program is distributed under the following license https creativecommons org licenses by nc 3 0 | ai-blocs tensorflow python machine-learning wysiwyg deep-learning artificial-intelligence neural-networks modular | ai |
ePortfolio | myprofile introduction to information technology a1 | server |
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mslearn-train-package-module-iot-edge | this repository contains a labs to help you get started with creating and deploying azure machine learning module vs code remote containers follow these steps to open this sample in a container using the vs code remote containers extension 1 if this is your first time using a development container please ensure your system meets the pre reqs i e have docker installed in the getting started steps https aka ms vscode remote containers getting started 2 to use this repository you can either open the repository in an isolated docker volume press kbd f1 kbd and select the remote containers try a sample command choose the python sample wait for the container to start and try things out note under the hood this will use the remote containers clone repository in container volume command to clone the source code in a docker volume instead of the local filesystem volumes https docs docker com storage volumes are the preferred mechanism for persisting container data or open a locally cloned copy of the code clone this repository to your local filesystem press kbd f1 kbd and select the remote containers open folder in container command select the cloned copy of this folder wait for the container to start and try things out 3 rebuild or update your container you may want to make changes to your container such as installing a different version of a software or forwarding a new port you ll rebuild your container for your changes to take effect open browser automatically as an example change let s update the portsattributes in the devcontainer devcontainer json file to open a browser when our port is automatically forwarded open the devcontainer devcontainer json file modify the onautoforward attribute in your portsattributes from notify to openbrowser press kbd f1 kbd and select the remote containers rebuild container or codespaces rebuild container command so the modifications are picked up labs after you ve completed the setup steps above you can use your visual studio online environment to complete the labs note labs that involve running code include all of the code you ll need you ll just need to copy and paste a few values and run the code that is provided so don t worry if you re not a programmer we ve used python code in the labs because that can be runs interactively in the notebooks themselves contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla opensource microsoft com when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g status check comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments legal notices microsoft and any contributors grant you a license to the microsoft documentation and other content in this repository under the creative commons attribution 4 0 international public license https creativecommons org licenses by 4 0 legalcode see the license license file and grant you a license to any code in the repository under the mit license https opensource org licenses mit see the license code license code file microsoft windows microsoft azure and or other microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of microsoft in the united states and or other countries the licenses for this project do not grant you rights to use any microsoft names logos or trademarks microsoft s general trademark guidelines can be found at http go microsoft com fwlink linkid 254653 privacy information can be found at https privacy microsoft com en us microsoft and any contributors reserve all other rights whether under their respective copyrights patents or trademarks whether by implication estoppel or otherwise | server |
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rune-breaker | rune breaker this project aims to test the effectiveness of one of maplestory s anti botting mechanisms the rune system in the process a model that can solve up to 99 15 errors and biases of the runes was created the project provides an end to end pipeline that encompasses every step necessary to replicate this model additionally the article below goes over all the details involved during the development of the model toward the end it also considers possible improvements and alternatives to the current rune system make sure to read it this project was created solely for research and learning purposes the usage of this project for malicious purposes in game is against nexon s terms of service and thus discouraged summary 1 introduction introduction what are runes what are runes what is the challenge what is the challenge how can it be solved how can it be solved 2 the pipeline the pipeline overview overview labeling labeling preprocessing preprocessing dataset dataset classification model classification model 3 results and observations results and observations performance performance observations observations 4 final considerations final considerations introduction maplestory is a massively multiplayer online role playing game published by nexon and available in many countries around the world what are runes in an attempt to protect the game from botting maplestory employs a kind of captcha https en wikipedia org wiki captcha system from time to time a rune is spawned somewhere on the player s map when the user walks into it and presses the activation button the game displays a panel containing four arrows see an example below picture of a rune being activated docs rune example png the player is then asked to type the direction of the four arrows if the directions are correct the rune disappears and the player receives a buff however if the user does not activate it within a certain amount of time they will stop receiving any rewards for hunting monsters hampering the performance of bots that cannot handle runes that said our goal is to create a computer program capable of determining the direction of the arrows when given a screenshot from the game what is the challenge if arrows were static textures it would be possible to apply a template matching algorithm and reliably determine the arrow directions interestingly that is how runes were initially implemented picture of the old rune system docs old rune system png nevertheless the game was later updated http maplestory nexon net news 24928 updated v 188 tune up patch notes to add randomness to the arrows for instance both the position of the panel and the position of the arrows within it are randomized moreover every color is randomized and often mixed in a semi transparent gradient finally there are four different arrow shapes which then suffer rotation and scaling transformations picture of heavily obfuscated arrows docs difficult rune example png as shown in the picture above these obfuscation mechanisms can make it hard to determine the arrow directions even for humans together these techniques make it more difficult for machines to define the location the shape and the orientation of the arrows is it possible to overcome these challenges how can it be solved as we have just discussed there are several layers of difficulty that must be dealt with to enable a computer program to determine the arrow directions with that in mind splitting the problem into different steps is a good idea first the program should be able to determine the location of each arrow on the screen after that the software should classify them as down left right or up locating the arrows some investigation reveals that it is possible to determine the location of the arrows using less sophisticated techniques even though this task can be performed by an artificial neural network the simplest option is to explore solutions within traditional computer vision classifying the arrow directions on the other hand classifying arrow directions is a great candidate for machine learning solutions first of all each arrow could fit inside a grayscale 60 60 1 image which is a relatively small input size moreover it is very convenient to gather data since it is possible to generate 32 different sample images for each screenshot the only requirement is to rotate and flip each of the four arrows in the screenshot the pipeline in this section we will see how to put these steps together to create a program that classifies rune arrows overview first we should define two pipelines the model pipeline and the runtime pipeline see the diagram next pipeline diagram docs pipelines png the model pipeline is responsible for taking a set of screenshots as input and performing a sequence of steps to create an arrow classification model on the other hand the runtime model represents a hypothetical botting software this software would use parts of the model pipeline in addition to the trained classifier to activate runes during the game runtime considering the purposes of this project only the model pipeline will be discussed each step of this pipeline will be thoroughly explained in the sections below also the experimental results obtained during the development of this project will be presented labeling first we should collect game screenshots and label them according to the direction and shape of the arrows to make this process easier the label py preprocessing label py script was created the next figure shows the application screen when running the command below python preprocessing label py labeling application screen docs labeler png when run the program iterates over all images inside the data screenshots directory for each screenshot the user should press the arrow keys in the correct order to label the directions and 1 2 or 3 to label the arrow shape there are five different types of arrows which can be arranged within three groups as shown below arrow shapes docs arrow types png this distinction is made to avoid problems related to data imbalance this topic will be better explained later on in the dataset dataset section after a screenshot is labeled the application moves it to the data labeled folder finally the file is renamed with a name that contains its labels and a unique id e g wide rdlu 123 png where each letter in rdlu represents a direction note instead of manually collecting and labeling game screenshots it is also possible to automatically generate artificial samples by directly using the game assets preprocessing with our screenshots correctly labeled we can move to the preprocessing stage simply put this step is responsible for locating the arrows within the labeled screenshots and producing preprocessed output images these images are the arrow samples that will feed the classification model later on this process is handled by the preprocess py preprocessing preprocess py script its internals can be visualized through the following diagram preprocessing diagram docs preprocessing png each screenshot undergoes a sequence of transformations that simplify the image in several aspects next the transformed image is analyzed and the position of each arrow is determined lastly the script generates the output samples let s examine each one of these operations individually cropping our first objective is to make it easier to find the location of the arrows within a screenshot consider the following input image input screenshot docs input image png the input has a width of 800 pixels a height of 600 pixels and 3 rgb color channels 800 600 3 1 400 000 which is a considerable amount of data to be processed this makes detecting objects difficult and may ultimately harm the accuracy of our model what can be done to overcome this obstacle one alternative relies on processing each arrow separately we can accomplish this by first restricting the search to a small portion of the screen next the coordinates of the leftmost arrow are determined after that these coordinates are used to define the next search region then the process repeats until all arrows have been found this is possible because although the positions of the arrows are random they still fall within a predictable zone this is the first search region for our input image first search region docs search region png as can be seen the search problem has been reduced to an image much smaller than the original one 120 150 3 54 000 however the resulting picture still has three color channels this issue will be addressed next transforming the input the key to identifying the arrows is edge detection https en wikipedia org wiki edge detection nevertheless the obfuscation and the complexity of the input image make the process more difficult to make things simpler we can compress the information of the image from three color channels into a single grayscale channel however we should not directly combine the r g and b channels since the arrows may be of almost any color we cannot define a single formula that works well for any image instead we will combine the hsv representation of the image which separates color and intensity information into three different components after some experimentation the following linear transformation yielded the best results grayscale 0 0445 h 0 6568 s 0 2987 v additionally we can apply gaussian blur to the image to eliminate small noise the result of these transformations is shown below grayscale search region docs grayscale png although the new image has only one channel instead of three 120 150 1 18 000 the outlines of the arrow can still be identified we have roughly the same information with fewer data this same result is observed in several other examples even if at different degrees however we can still do more each pixel in the current image now ranges from 0 to 255 since it is a grayscale image in a binarized image on the other hand each pixel is either 0 black or 255 white in this case the contours are objectively clear and can be easily processed by an algorithm or a neural network since the image has many variations of shading and lighting the most appropriate way to binarize it is to use adaptive thresholding fortunately opencv https opencv org provides this functionality out of the box which can be configured with several parameters applying this algorithm to the grayscale image produces the following result binarized search region docs binarized png the current result is already considerably positive yet notice that there are some small patches scattered throughout the image to make it easier for the detection algorithm and for the arrow classifier we can use the morphology operations provided by the skimage library https scikit image org to mitigate this noise the next figure shows the resulting image search region with reduced noise docs reduced noise png the image is now ready to be analyzed next we will discuss the details of the algorithm that determines the position of the arrow locating the arrow the first step of the process consists in computing the contours of the image to do this an algorithm is used to transform the edges in the image into sets of points forming many polygons see the figure below contours of the search region docs contours png each polygon constitutes a different contour but then how to determine which contours belong to the arrow it turns out that directly identifying the arrow is not the best alternative frequently the obfuscation heavily distorts the outline of the arrow which makes identifying it very difficult fortunately some outlines are much simpler to identify the circles that surround the arrow there are two reasons for this first these contours tend to keep their shape intact despite the obfuscation second circumferences have much clearer morphological characteristics still another question remains how to identify these circles then there are several ways to solve this problem one of the first alternatives that may come to mind is the circle hough transform https en wikipedia org wiki circle hough transform unfortunately this method is known to be unstable when applied to noisy images preliminary tests confirmed that this method produces unreliable results for our input images because of this i decided to use morphological analysis instead algorithm our goal is to determine which contours correspond to the surrounding circles and then calculate the position of the arrows which sit in the middle of the circumferences to do this we can use the following algorithm 1 first the program removes all contours whose area is too small or too large compared to a real surrounding circle 2 then a score is calculated for each remaining contour this score is based on the similarity between the contour a perfect ellipse and a perfect circle 3 next the contour with the best score is selected 4 finally if the score of the selected contour exceeds a certain threshold the algorithm outputs its center otherwise it outputs the center of the search area as a fallback which makes the algorithm more robust the code snippet below illustrates the process py candidates c for c in contours if area c min and area c max best candidate max candidates key lambda c score c if score best candidate threshold return center best candidate return center search region similarity score the key to the accuracy of the operation above is the quality of the score its principle is simple the surrounding circles tend to resemble a perfect circumference more than other objects do with that in mind it is possible to measure the likeliness that a contour is a surrounding circle using the following metrics 1 the difference between the convex hull of the contour and its minimum enclosing ellipse considering that the area of the ellipse is larger s1 area ellipse area hull area ellipse 2 the difference between the minimum enclosing ellipse and its minimum enclosing circle considering that the area of the circle is larger s2 area circle area ellipse area circle the resulting score is one minus the arithmetic mean of these two metrics score 1 s1 s2 2 by doing so the closer a contour is to a perfect circle the closer the score will be to one the more distinct the closer it will get to zero iteration output the figure below shows the filtered candidates green the best contour among them blue and the coordinate returned by one iteration of the algorithm red arrow center docs selected contours png of course there are errors associated with this algorithm sometimes the operation yields false positives due to the obfuscation causing the center of the arrow to be defined incorrectly in some cases these errors are significant enough to prevent the arrow from being in the output image as we will see next nevertheless these cases are rather uncommon and have a relatively small impact on the accuracy of the model finally the coordinate returned by the algorithm is then used to define the next search region as shown below after that the algorithm repeats until it processes all four arrows p align center x sub search sub y sub search sub x sub center sub strong d strong y sub center sub p generating the output after finding the positions of the arrows the program should generate an output to do so it first crops a region around the center of each arrow the dimension of the cropped area is 60 60 which is just enough to fit an arrow see the results in the next figure preprocessing output docs output png but as mentioned earlier we can augment the output by rotating and flipping the arrows by doing this we both multiply the sample size by eight and eliminate class imbalance which is extremely helpful the next figure shows the 32 generated samples augmented preprocessing output docs augmented output png application interface before discussing the experimental results let s talk about the preprocessing application interface when the user runs the script it displays the following window for each screenshot in the data labeled folder python preprocessing preprocess py preprocess script window docs preprocessor window png for each one of them the user is given the option to either process or skip the screenshot if the user chooses to process it the script produces output samples and places them inside the data samples folder the user should skip a screenshot when it is impossible to determine the direction of an arrow in other words the screenshot should be skipped when the arrow is completely corrupted or when the algorithm misses its location see a couple of examples preprocessing errors docs errors png results using 800 labeled screenshots as input and following the guidelines mentioned in the previous section the following results were obtained approved 798 out of 800 images 99 samples summary down left right up total round 1872 1872 1872 1872 7488 wide 2632 2632 2632 2632 10528 narrow 1880 1880 1880 1880 7520 total 6384 6384 6384 6384 25536 in other words the preprocessing stage had an accuracy of 99 75 plus there are now 25 536 arrow samples that we can use to create the dataset that will be used by the classification model great dataset in this step of the pipeline the objective is to split the generated samples into training validation and testing sets each set has its own folder inside the data directory the produced dataset will then be used to train the arrow classification model note that the quality of the dataset has a direct impact on the performance of the model thus it is essential to perform this step correctly let s start by defining and understanding the purpose of each set training set as the name suggests this is the dataset that will be directly used to fit the model in the training process validation set this is the dataset used to evaluate the generalization of the model and to optimize any hyperparameters the classifier may use testing set this is the set used to determine the final performance of the model with these definitions in place let s address some issues one of the main obstacles that classification models may face is data imbalance when the training set is asymmetric the model may not generalize well which causes it to perform poorly for the underrepresented samples therefore the training set should have an approximately equal number of samples from each shape and direction additionally it is necessary to define the proportion of samples in each set an 80 10 10 division is usually a good starting point the make dataset py operations make dataset py script is responsible for creating a dataset that meets all the criteria above note to avoid data leakage the script also ensures that each sample and its flipped counterpart are always in the same set running the script with the ratio set to 0 9 produces the following results python operations make dataset py r 0 9 training set down left right up total round 1684 1684 1684 1684 6736 wide 1684 1684 1684 1684 6736 narrow 1684 1684 1684 1684 6736 total 5052 5052 5052 5052 20208 validation set down left right up total round 94 94 94 94 376 wide 474 474 474 474 1896 narrow 98 98 98 98 392 total 666 666 666 666 2664 testing set down left right up total round 94 94 94 94 376 wide 474 474 474 474 1896 narrow 98 98 98 98 392 total 666 666 666 666 2664 notice that the training set is perfectly balanced on both axes you can also see that the split between sets follows approximately 80 10 10 now that the dataset is ready we can move on to the main stage the construction of the classification model classification model in this stage the dataset created in the previous step is used to adjust and train a machine learning model that classifies arrows into four directions when given an input image the trained classifier can be applied in the runtime pipeline to determine the arrow directions within the game model simplification docs model png to quickly validate the viability of the idea we can use google s teachable machine https teachablemachine withgoogle com train image which allows any user to train a neural network image classifier an experiment using only 1200 arrow samples results in a model with an overall accuracy of 90 it is quite clear that a classification model based on a neural network is feasible in fact we could export the model trained by google s tool and call it a day however since each rune has four arrows the total accuracy is 66 fortunately there is plenty of room for improvement for instance this model transforms our 60 60 1 input into 224 224 3 making the process much more complex than it needs to be additionally google s tool lack many configuration options with this in mind the best alternative is to create a model from scratch building the model our model will use a convolutional neural network cnn which is a type of neural network widely used for image classification we will use keras to create and train the model take a look at the code that generates the structure of the neural network py def make model model sequential convolution block 1 model add conv2d 32 3 3 padding same input shape 60 60 1 model add activation relu model add maxpooling2d pool size 2 2 convolution block 2 model add conv2d 48 3 3 padding same model add activation relu model add maxpooling2d pool size 2 2 convolution block 3 model add conv2d 64 3 3 padding same model add activation relu model add maxpooling2d pool size 2 2 model add flatten model add dense 64 activation relu model add dropout 0 2 output layer model add dense 4 activation softmax model compile optimizer adam loss categorical crossentropy metrics accuracy return model the architecture was inspired by one of the best performing mnist classification models on kaggle https www kaggle com while simple it should be more than enough for our purposes more data augmentation in addition to rotation and flipping as we saw earlier we can apply other transformations to the arrow samples we should do this to help reducing overfitting especially regarding the position and size of the arrows keras provides this and many other functionalities out of the box using an image data generator we can automatically augment the data from both training and validation sets during the fitting process see the code snippet below which was taken from the training script py aug imagedatagenerator width shift range 0 125 height shift range 0 125 zoom range 0 2 training the model with that set we can fit the model to the data with the train py model train py script based on a few input parameters the script creates the neural network performs data augmentation fits the model to the data and saves the trained model to a file moreover to improve the performance of the training process the program applies mini batch gradient descent and early stopping let s train a model named binarized model128 h5 with the batch size set to 128 python model train py m binarized model128 h5 b 128 settings value max epochs 240 patience 80 batch size 128 creating model creating generators found 20208 images belonging to 4 classes found 2664 images belonging to 4 classes fitting model epoch 228 240 28s loss 0 0095 accuracy 0 9974 val loss 0 0000e 00 val accuracy 0 9945 best epoch 228 saving model model saved to model binarized model128 h5 finished as we can see the model had a 99 45 accuracy in the best epoch for the validation set the next figure shows the accuracy and loss charts for this training session evolution per epoch charts docs charts png results and observations now that our model has been trained and validated it is time to evaluate its performance using the testing set in the sections below we will show some metrics calculated for the classifier and make some considerations upon them note you can access the complete results here results performance to calculate the performance of the model on the testing set we can run the classify py model classify py script with the following parameters python model classify py m binarized model128 h5 d testing confusion matrix down left right up down 664 0 2 0 left 0 665 0 1 right 0 0 666 0 up 0 0 1 665 classification summary precision recall f1 down 1 0000 0 9970 0 9985 left 1 0000 0 9985 0 9992 right 0 9955 1 0000 0 9977 up 0 9985 0 9985 0 9985 classification summary correct incorrect accuracy round 376 0 1 0000 wide 1892 4 0 9979 narrow 392 0 1 0000 total 2660 4 0 9985 notice the f1 scores and the accuracy per arrow type both of them are balanced which means that the model is not biased toward any shape or direction the results are remarkable the model was able to correctly classify 99 85 of the arrows now let s calculate the overall accuracy of the pipeline preprocessing four arrows total 0 9975 0 9985 0 9940 0 9915 in other words the pipeline is expected to solve a rune 99 15 of the time that is roughly equivalent to only a single mistake for every hundred runes amazing note similar results may be accomplished with much less than 800 screenshots observations before we wrap up it is critical to make some observations about the development process and get some insight into the results the bottleneck we can see which arrows the model was unable to classify by applying the v flag to the classification script analyzing the results we see that most of them were almost off screen and few of them were heavily distorted thus we conclude that the main bottleneck of the pipeline lies in the preprocessing stage if improvements are made then they should focus on the algorithms responsible for transforming and locating the arrows as we have mentioned earlier one alternative to the algorithm that finds the position of the arrows is to use machine learning this could increase the accuracy of the pipeline especially when dealing with hard to see images another possible improvement may be to optimize the parameters in the preprocessing stage for runes that are very hard to solve it is also possible to improve the performance of the model in the runtime pipeline for instance the software may take three screenshots from the rune at different moments and classify all of them after that the directions of the arrows can be determined by combining the three classifications this may reduce the error rate caused by specific frames such as when damage numbers stay behind the arrows errors and biases for the sake of integrity we must understand some of the underlying errors and biases behind the result we just saw the real performance of the model can only be measured in practice for instance the input screenshots are biased toward the maps and situations in which they were taken even though the images are significantly diverse moreover remember that some of the screenshots were filtered during the preprocessing stage the human interpretation of whether or not an arrow is corrupted may influence the resulting metrics also when a screenshot is skipped it is assumed that the model would not be able to solve the rune which is not always the case there is still a probability that the model correctly classifies an arrow by chance 25 if you consider it a random event further testing in a small test with 20 new screenshots the model was able to solve all the runes however in another experiment using exclusively screenshots with lots of action especially ones with damage numbers flying around the model was able to solve 16 out of 20 runes 80 of the runes and 95 of the arrows in reality the performance of the model varies according to many factors such as the player s class the spawn location of the rune and the monsters nearby nonetheless the overall performance is very good for the purposes of the model additionally we can still apply the improvements discussed in the bottleneck section the bottleneck final considerations with a moderate amount of work we created a model capable of solving runes up to 99 15 of the time we have also seen some ways to improve this result even further but what do these results tell us conclusion first of all we proved that current artificial intelligence technology can easily bypass the employed visual obfuscation reaching human level performance we also know that these obfuscation methods bring significant usability drawbacks since there have been multiple complaints from the player base including colorblind people moreover while the current system forced us to develop a new model from scratch one would expect it to be more elaborate and expensive also even if a perfect model existed bot developers would still have to create an algorithm or a hack to control the character and move it to the rune this is not only a whole different problem but possibly a more difficult one which already provides a notable level of protection this makes us wonder whether the part of the rune system update that introduced the visual obfuscation had a significant impact on the botting ecosystem additionally commercial level botting tools have been immune to visual tricks since runes first appeared as we will see they do not need to process any image therefore we are left with two issues first the current rune system hurts user interaction without providing meaningful security benefits second it is somewhat ineffective toward some of the botting software available out there how to improve it the solution to the first problem is straightforward reduce or remove the semi transparency effects and use colorblind friendly colors only the second issue on the other hand is trickier first there is software capable of bypassing the anti botting system internally second recent advancements in ai technology have been closing the gap between humans and machines even though it is simple to patch the game visuals and prevent this project from functioning it is just as easy to make it work again it is no coincidence that traditional captchas have disappeared from the web in the last years the best alternative seems to be to move away from this type of captcha despite that there are still some opportunities that can be explored within this type of anti botting system let s see some of them overall improvements the changes proposed next may enhance both the quality and security of the game regardless of the captcha system used dynamic difficulty an anti botting system should keep a healthy balance between user friendliness and protection against malicious software to do so the system could reward successes while penalizing failures for example if a user correctly answers many runes the next ones could contain fewer arrows on the other hand the game could propose a harder challenge if someone or some program makes many mistakes in a row in addition to this the delays between attempts could be adjusted currently no matter how many times a user fails to solve a rune the delay between activations is only five seconds a better alternative is to increase the amount of time exponentially after a certain amount of errors this measure impairs the performance of inaccurate models that need several attempts to solve a rune as a side effect it is also harder for an attacker to collect data from the game better robustness currently the game client is responsible for both generating and validating rune challenges which allows hackers to perform code packet injection and forge rune validation because of this botting tools can bypass this protection system without processing the game screen as we have mentioned previously while it is considerably difficult to accomplish such a feat which involves bypassing the protection mechanisms of the game this vulnerability is enough to defeat the purpose of the runes to a significant extent one way to solve this issue is to move both generation and validation processes to the server the game server would send the generated image to the client and validate the response of the client the client on the other hand would simply display the received image and forward the user s input by doing so the only possible way to bypass this mechanism would be through image processing although it was reasonably simple to circumvent the current rune system through image processing it is not nearly as trivial with other captchas alternative systems while the changes above could improve the overall quality of the rune system they alone are not capable of solving its main vulnerabilities for this reason the following alternatives were proposed based on the resources readily available within the game and the capabilities of modern image classifiers finding image matches finding image matches docs find matches png in this system the user is asked to find all the monsters npcs or characters that match the specified labels finding non rotated images finding non rotated images docs find rotated png in this system the user is asked to find all the monsters npcs or characters that are not rotated both systems have their advantages and disadvantages compared to the current one let s examine them more closely advantages maplestory promptly features thousands of different sprites which may be rotated scaled flipped and put in an obfuscated background resulting in hundreds of thousands of different combinations thus when coupled with better robustness improvements better robustness the proposed systems could force botting software to be extremely expensive inaccurate and possibly impractical besides the first system has an extra advantage since attackers would also have to find out all of the labels that may be specified by the rune system increasing the cost of developing an automated tool even more disadvantages although they may provide benefits the proposed systems do not come without caveats both mechanisms are considerably more expensive to implement especially the first one also they are not as universal as the current runes which only require the user to identify arrow directions despite this it is possible to mitigate the latter issue by employing dynamic difficulty that concludes the article i hope you have learned something new feel free to open an issue if you have any questions i will be more than happy to help | maplestory convolutional-neural-network computer-vision runes machine-learning bot auto | ai |
sofe | project status sofe is a plugin for systemjs 0 21 but the latest version of systemjs is 3 the new versions of systemjs https github com systemjs systemjs have the equivalent of sofe built into systemjs itself via import maps https github com wicg import maps because of this we encourage that you use systemjs 3 import maps instead of using systemjs 0 21 sofe see this file https gitlab com themcmurder single spa portal example blob master src index html l10 for an example of what the new alternative looks like for the equivalent of sofe s local storage overrides check out import map overrides https github com joeldenning import map overrides sofe service oriented front end sofe is a systemjs https github com systemjs systemjs plugin that resolves a module name to a fully qualified url at runtime npm version https img shields io npm v sofe svg style flat square https www npmjs org package sofe build status https img shields io travis canopytax sofe svg style flat square https travis ci org canopytax sofe code coverage https img shields io codecov c github canopytax sofe svg style flat square https codecov io github canopytax sofe sofe allows developers to avoid building monolothic front end web applications and instead build software on top of micro services sofe is a javascript library that enables independently deployable javascript services to be retrieved at run time in the browser more on the motivation for soa in the browser docs motivation md getting started 1 setup systemjs and jspm http jspm io docs getting started html 2 configure systemjs to use the sofe plugin bash jspm install npm sofe 3 load sofe services javascript system import sofe hello world sofe then hello hello sofe is properly setup if the browser alerts a hello world you have successfully loaded the hello world sofe service by default any npm package with a umd build can be a sofe service simply load the service by the package name javascript system import backbone sofe then backbone new backbone view create your own service by default all npm packages are loaded through npm unpkg https unpkg com while this is convenient it isn t always what you want to do for your own services you can provide a custom location for where your service distributable is located by adding a sofe property to your package json javascript name my service version 1 0 0 sofe url https cdn some location com my service 1 0 0 js republish a new sofe url to npm whenever you want to update the location of your service distributable services in production create your own private service by telling sofe how to resolve your service name add to your systemjs configuration a manifest option to a sofe property or auto discover services by loading a remote manifest of available services javascript system config sofe manifest someinternalservice http somelocation com service 1 0 0 js manifesturl https somelocation com available services json learn more about production configuration within the api documentation docs sofe api md examples examples are available at sofe surge sh http sofe surge sh or you can run them locally examples examples md tools 1 sofe inspector https github com canopytax sofe inspector developer tool for inspecting and overriding available sofe services 1 sofe babel plugin https github com canopytax sofe babel plugin a babel plugin for production sofe workflows with webpack 1 sofe deplanifester https github com canopytax sofe deplanifester a manifest deployment service for sofe 1 sofe hello world https github com canopytax sofe hello world a simple sofe service to say hello license copyright 2016 canopytax licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | front-end | front_end |
awesome-online-machine-learning | div align center h1 awesome online machine learning h1 a href https github com sindresorhus awesome img src https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg a div online machine learning https www wikiwand com en online machine learning is a subset of machine learning where data arrives sequentially in contrast to the more traditional batch learning online learning methods update themselves incrementally with one data point at a time courses and books courses and books blog posts blog posts software software modelling modelling deployment deployment papers papers linear models linear models support vector machines support vector machines neural networks neural networks decision trees decision trees unsupervised learning unsupervised learning time series time series drift detection drift detection anomaly detection anomaly detection metric learning metric learning graph theory graph theory ensemble models ensemble models expert learning expert learning active learning active learning miscellaneous miscellaneous surveys surveys general purpose algorithms general purpose algorithms hyperparameter tuning hyperparameter tuning evaluation evaluation courses and books machine learning for streaming data with python https github com packtpublishing machine learning for streaming data with python ie 498 online learning and decision making https yuanz web illinois edu teaching ie498fa19 introduction to online learning https parameterfree com lecture notes on online learning machine learning the feature http www hunch net mltf gives some insights into the inner workings of vowpal wabbit especially the slides on online linear learning http www hunch net mltf online linear pdf machine learning for data streams with practical examples in moa https www cms waikato ac nz abifet book contents html online methods in machine learning mit http www mit edu rakhlin 6 883 streaming 101 the world beyond batch https www oreilly com ideas the world beyond batch streaming 101 prediction learning and games http www ii uni wroc pl lukstafi pmwiki uploads agt prediction learning and games pdf introduction to online convex optimization https ocobook cs princeton edu ocobook pdf reinforcement learning and stochastic optimization a unified framework for sequential decisions https castlelab princeton edu rlso the entire book builds upon online learning paradigm in applied learning optimization problems chapter 3 online learning being the reference big data course at the cilvr lab at nyu https cilvr cs nyu edu doku php id courses bigdata slides start focus on linear models and bandits some courses are given by john langford the creator of vowpal wabbit machine learning for personalization http www cs columbia edu jebara 6998 course from columbia by tony jebara covers bandits blog posts fennel ai blog posts about online recsys https fennel ai blog anomaly detection with bytewax redpanda bytewax 2022 https www bytewax io blog anomaly detection bw rpk the online machine learning predict fit switcheroo max halford 2022 https maxhalford github io blog predict fit switcheroo real time machine learning challenges and solutions chip huyen 2022 https huyenchip com 2022 01 02 real time machine learning challenges and solutions html anomalies detection using river matias aravena gamboa 2021 https medium com spikelab anomalies detection using river 398544d3536 introdu o n o extensiva a online machine learning saulo mastelini 2021 https medium com saulomastelini introdu c3 a7 c3 a3o a online machine learning 874bd6b7c3c8 machine learning is going real time chip huyen 2020 https huyenchip com 2020 12 27 real time machine learning html the correct way to evaluate online machine learning models max halford 2020 https maxhalford github io blog online learning evaluation what is online machine learning max pagels 2018 https medium com value stream design online machine learning 515556ff72c5 what is it and who needs it data science central 2015 https www datasciencecentral com profiles blogs stream processing what is it and who needs it software see more here https github com stars maxhalford lists online learning modelling river https github com creme ml creme a python library for general purpose online machine learning dask https ml dask org incremental html jubatus http jubat us en index html flink ml https nightlies apache org flink flink ml docs stable apache flink machine learning library libffm https www csie ntu edu tw cjlin libffm a library for field aware factorization machines liblinear https www csie ntu edu tw cjlin liblinear a library for large linear classification libol https github com libol a collection of online linear models trained with first and second order gradient descent methods not maintained moa https moa cms waikato ac nz documentation scikit learn https scikit learn org stable some https scikit learn org stable computing scaling strategies html incremental learning of scikit learn s estimators can handle incremental updates although this is usually intended for mini batch learning see also the computing with scikit learn https scikit learn org stable computing html page spark streaming https spark apache org docs latest streaming programming guide html doesn t do online learning per say but instead mini batches the data into fixed intervals of time sofiaml https code google com archive p sofia ml streamdm https github com huawei noah streamdm a machine learning library on top of spark streaming tornado https github com alipsgh tornado vfml http www cs washington edu dm vfml vowpal wabbit https github com vowpalwabbit vowpal wabbit deployment kappaml https www kappaml com django river ml https github com vsoch django river ml a django plugin for deploying river models chantilly https github com online ml chantilly a prototype meant to be compatible with river previously creme papers linear models field aware factorization machines for ctr prediction 2016 https www csie ntu edu tw cjlin papers ffm pdf practical lessons from predicting clicks on ads at facebook 2014 https research fb com wp content uploads 2016 11 practical lessons from predicting clicks on ads at facebook pdf ad click prediction a view from the trenches 2013 https static googleusercontent com media research google com en pubs archive 41159 pdf normalized online learning 2013 https arxiv org abs 1305 6646 towards optimal one pass large scale learning with averaged stochastic gradient descent 2011 https arxiv org abs 1107 2490 dual averaging methods for regularized stochastic learning andonline optimization 2010 https www microsoft com en us research wp content uploads 2016 02 xiao10jmlr pdf adaptive regularization of weight vectors 2009 https papers nips cc paper 3848 adaptive regularization of weight vectors pdf stochastic gradient descent training forl1 regularized log linear models with cumulative penalty 2009 https www aclweb org anthology p09 1054 confidence weighted linear classification 2008 https www cs jhu edu mdredze publications icml variance pdf exact convex confidence weighted learning 2008 https www cs jhu edu mdredze publications cw nips 08 pdf online passive aggressive algorithms 2006 http jmlr csail mit edu papers volume7 crammer06a crammer06a pdf logarithmic regret algorithms foronline convex optimization 2007 https www cs princeton edu ehazan papers log journal pdf a second order perceptron algorithm 2005 http www datascienceassn org sites default files second order 20perception 20algorithm pdf online learning with kernels 2004 https alex smola org papers 2004 kivsmowil04 pdf solving large scale linear prediction problems using stochastic gradient descent algorithms 2004 http citeseerx ist psu edu viewdoc summary doi 10 1 1 58 7377 support vector machines pegasos primal estimated sub gradient solver for svm 2007 http citeseerx ist psu edu viewdoc summary doi 10 1 1 74 8513 a new approximate maximal margin classification algorithm 2001 http www jmlr org papers volume2 gentile01a gentile01a pdf the relaxed online maximum margin algorithm 2000 https papers nips cc paper 1727 the relaxed online maximum margin algorithm pdf neural networks three scenarios for continual learning 2019 https arxiv org pdf 1904 07734 pdf decision trees amf aggregated mondrian forests for online learning 2019 https arxiv org abs 1906 10529 mondrian forests efficient online random forests 2014 https arxiv org abs 1406 2673 mining high speed data streams 2000 https homes cs washington edu pedrod papers kdd00 pdf unsupervised learning online clustering algorithms evaluation metrics applications and benchmarking 2022 https dl acm org doi pdf 10 1145 3534678 3542600 online hierarchical clustering approximations 2019 https arxiv org pdf 1909 09667 pdf deepwalk online learning of social representations 2014 https arxiv org pdf 1403 6652 pdf online learning with random representations 2014 http citeseerx ist psu edu viewdoc download doi 10 1 1 127 2742 rep rep1 type pdf online latent dirichlet allocation with infinite vocabulary 2013 http proceedings mlr press v28 zhai13 pdf web scale k means clustering 2010 https www eecs tufts edu dsculley papers fastkmeans pdf online dictionary learning for sparse coding 2009 https www di ens fr sierra pdfs icml09 pdf density based clustering over an evolving data stream with noise 2006 https archive siam org meetings sdm06 proceedings 030caof pdf knowledge acquisition via incremental conceptual clustering 2004 http www inf ufrgs br engel data media file aprendizagem cobweb pdf online and batch learning of pseudo metrics 2004 https ai stanford edu ang papers icml04 onlinemetric pdf birch an efficient data clustering method for very large databases 1996 https www2 cs sfu ca coursecentral 459 han papers zhang96 pdf time series online learning for time series prediction 2013 https arxiv org pdf 1302 6927 pdf drift detection a survey on concept drift adaptation 2014 http eprints bournemouth ac uk 22491 1 acm 20computing 20surveys pdf anomaly detection leveraging the christoffel darboux kernel for online outlier detection 2022 https hal laas fr hal 03562614 document interpretable anomaly detection with mondrian p lya forests on data streams 2020 https arxiv org pdf 2008 01505 pdf fast anomaly detection for streaming data 2011 https www ijcai org proceedings 11 papers 254 pdf metric learning online metric learning and fast similarity search 2009 http people bu edu bkulis pubs nips online pdf information theoretic metric learning 2007 http www cs utexas edu users pjain pubs metriclearning icml pdf online and batch learning of pseudo metrics 2004 https ai stanford edu ang papers icml04 onlinemetric pdf graph theory deepwalk online learning of social representations 2014 http www cs cornell edu courses cs6241 2019sp readings perozzi 2014 deepwalk pdf ensemble models optimal and adaptive algorithms for online boosting 2015 http proceedings mlr press v37 beygelzimer15 pdf an implementation is available here https github com vowpalwabbit vowpal wabbit blob master vowpalwabbit boosting cc online bagging and boosting 2001 https ti arc nasa gov m profile oza files ozru01a pdf a decision theoretic generalization of on line learning and an application to boosting 1997 http www face rec org algorithms boosting ensemble decision theoretic generalization pdf expert learning on the optimality of the hedge algorithm in the stochastic regime https arxiv org pdf 1809 01382 pdf active learning a survey on online active learning 2023 https arxiv org ftp arxiv papers 2302 2302 08893 pdf miscellaneous multi output chain models and their application in data streams 2019 https jmread github io talks 2019 03 08 imperial stats seminar pdf a complete recipe for stochastic gradient mcmc 2015 https arxiv org abs 1506 04696 online em algorithm for latent data models 2007 https arxiv org abs 0712 4273 source code is available here https www di ens fr cappe code onlineem streamai dealing with challenges of continual learning systems for serving ai in production 2023 https ieeexplore ieee org abstract document 10172871 surveys machine learning for streaming data state of the art challenges and opportunities 2019 https www kdd org exploration files 3 cr 7 machine learning for streaming data state of the art final pdf online learning a comprehensive survey 2018 https arxiv org abs 1802 02871 online machine learning in big data streams 2018 https arxiv org abs 1802 05872v1 incremental learning algorithms and applications 2016 https www elen ucl ac be proceedings esann esannpdf es2016 19 pdf batch incremental versus instance incremental learning in dynamic and evolving data http albertbifet com wp content uploads 2013 10 ida2012 pdf incremental gradient subgradient and proximal methods for convex optimization a survey 2011 https arxiv org abs 1507 01030 online learning and stochastic approximations 1998 https leon bottou org publications pdf online 1998 pdf general purpose algorithms maintaining sliding window skylines on data streams 2006 http www cs ust hk dimitris papers tkde06 sky pdf the sliding dft 2003 https pdfs semanticscholar org 525f b581f9afe17b6ec21d6cb58ed42d1100943f pdf an online variant of the fourier transform a concise explanation is available here https www comm utoronto ca dimitris ece431 slidingdft pdf sketching algorithms for big data https www sketchingbigdata org hyperparameter tuning chacha for online automl 2021 https arxiv org pdf 2106 04815 pdf evaluation delayed labelling evaluation for data streams 2019 https link springer com article 10 1007 s10618 019 00654 y efficient online evaluation of big data stream classifiers 2015 https dl acm org doi pdf 10 1145 2783258 2783372 issues in evaluation of stream learning algorithms 2009 https dl acm org doi pdf 10 1145 1557019 1557060 | awesome awesome-list machine-learning online-machine-learning | ai |
mathematics-for-machine-learning-coursera | mathematics for machine learning cousera this repository contains all the quizzes assignments for the specialization mathematics for machine learning by imperial college of london on coursera br proof of my certification can be seen here https www coursera org account accomplishments specialization xsg9yarupcat br note the material provided in this repository is only for helping those who may get stuck at any point of time in the course it is strongly advised that no one should just copy the solutions voilation of coursera honor code presented here updates course 1 linear algebra completed br nbsp nbsp nbsp nbsp week1 completed br nbsp nbsp nbsp nbsp week2 completed br nbsp nbsp nbsp nbsp week3 completed br nbsp nbsp nbsp nbsp week4 completed br nbsp nbsp nbsp nbsp week5 completed br course 2 multivariate calculus completed br nbsp nbsp nbsp nbsp week1 completed br nbsp nbsp nbsp nbsp week2 completed br nbsp nbsp nbsp nbsp week3 completed br nbsp nbsp nbsp nbsp week4 completed br nbsp nbsp nbsp nbsp week5 completed br nbsp nbsp nbsp nbsp week6 completed br course 3 pca completed br nbsp nbsp nbsp nbsp week1 completed br nbsp nbsp nbsp nbsp week2 completed br nbsp nbsp nbsp nbsp week3 completed br nbsp nbsp nbsp nbsp week4 completed br | linear-algebra machine-learning eigenvalues eigenvectors principal-component-analysis multivariable-calculus coursera mathematics-machine-learning | ai |
TTK4155-byggern | electromechanical ping pong game byggern ttk4155 embedded and industrial computer systems design https www ntnu edu studies courses ttk4155 tab omemnet autumn 2018 take note that this was our first experience with coding in c and our group were one person less than the accepted group size of three course content design of embedded computer systems computer architectures and system components for embedded and industrial applications microcontrollers and specialized microprocessors parallel and seriabl bus systems datacommunication in industrial environments analog digital interfaces in this project we a group of two in this case were to create an electromechanical ping pong game the main challenge were to assemble the hardware components and develop software for the microcontrollers making a fully functional embedded computer system that will enable you to play a game of ping pong the tern project was a rather comprehensive labratory exercise which required allocation of a substantial amount of time for the lab throughout the semester we were given one day every week where we had access to the lab and a student assistant available an oral presentation and practical demonstration of this labratory project accounts for 50 of the final grade the project was divided into nine parts that we did more or less on a week to week basis part 1 initial assembly of microcontroller and rs 232 part 2 address decoding and external ram part 3 a d converting and joystick input part 4 oled display and user interface part 5 spi and can controller part 6 can bus and communication between nodes part 7 controlling servo and ir part 8 controlling motor and solenoid part 9 completion of the project and extras node 1 atmega162 and the usb multifunction card https github com evenlwanvik student byggern blob master images node1 jfif a 8 12 voltage supply was connected to v1 and earth plug on the left side a pcb bottom with several useful functions and node 2 top atmega2560 https github com evenlwanvik student byggern blob master images node2 jfif the pcb communicates with node 1 via can and forward it to node 2 using spi relay circuit left for the solenoid used to hit the ping pong ball and a filter right for the ir sensors https github com evenlwanvik student byggern blob master images pcb relay ir jfif example hierarchy of each node folder build a out pingpong o main hex main o pingpong c pingpong h main c makefile | os |
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oasis-core | oasis core build status buildkite badge buildkite link ci lint status github ci lint badge github ci lint link ci reproducibility status github ci repr badge github ci repr link docker status github docker badge github docker link release status github release badge github release link godev godev badge godev link docusaurus docusaurus badge docs link note markdown doesn t support tables without headers so we need to work around that and make the second non header row also bold go go coverage codecov badge codecov link rust rust coverage coveralls badge coveralls link markdownlint disable line length buildkite badge https badge buildkite com 16896a68bd8fba45d7b41fd608f26f87c726da10f7f24694a0 svg branch master buildkite link https buildkite com oasisprotocol oasis core ci github ci lint badge https github com oasisprotocol oasis core workflows ci lint badge svg github ci lint link https github com oasisprotocol oasis core actions query workflow ci lint branch master github ci repr badge https github com oasisprotocol oasis core workflows ci reproducibility badge svg github ci repr link https github com oasisprotocol oasis core actions query workflow ci reproducibility github docker badge https github com oasisprotocol oasis core workflows docker badge svg github docker link https github com oasisprotocol oasis core actions query workflow docker github release badge https github com oasisprotocol oasis core workflows release badge svg github release link https github com oasisprotocol oasis core actions query workflow release codecov badge https codecov io gh oasisprotocol oasis core branch master graph badge svg codecov link https codecov io gh oasisprotocol oasis core coveralls badge https coveralls io repos github oasisprotocol oasis core badge svg coveralls link https coveralls io github oasisprotocol oasis core godev badge https img shields io badge go dev reference 007d9c logo go logocolor white godev link https pkg go dev github com oasisprotocol oasis core go tab subdirectories docusaurus badge https img shields io badge docusaurus docs 007d9c logo read the docs logocolor white docs link https docs oasis io core markdownlint enable line length note oasis core is in active development so all apis protocols and data structures are subject to change the code has not yet been fully audited for security issues and other security related topics see security security contributing see our contributing guidelines contributing md security read our security docs security md document developer documentation see our developer documentation index developer documentation index docs readme md developing and building the system see a list of prerequisites followed by build instructions and an example of setting up a local test network with a simple runtime markdownlint disable line length a list of prerequisites docs development setup prerequisites md build instructions docs development setup building md setting up a local test network with a simple runtime docs development setup oasis net runner md markdownlint enable line length directories client client library for talking with the runtimes docker docker environment definitions go oasis node keymanager api common common keymanager code shared between client and lib keymanager client client crate for the key manager keymanager lib keymanager library crate runtime the runtime library that simplifies writing sgx and non sgx runtimes runtime loader the sgx and non sgx runtime loader process scripts bash scripts for development tests runtimes clients and resources used for e2e tests tools build tools | blockchain smart-contracts privacy | blockchain |
BlockChain | blockchain simple blockchain demo in java i demonstrated the concept of a blockchain that powers almost all of the modern cryptocurrencies is very simple at its core bitcoin ethereum litecoin etc all are based on this blockchain technology many people think that the blockchain is a complicated thing while at it s core its just a clever use case of hashes workspace java blockchain demo src block java eclipse 21 oct 17 5 09 57 pm https user images githubusercontent com 17050077 31851425 0c2c35ba b683 11e7 86ee 288bba262d36 png workspace java blockchain demo src block java eclipse 21 oct 17 5 10 05 pm https user images githubusercontent com 17050077 31851426 0c67d462 b683 11e7 99f5 b6cdefca0606 png | blockchain blockchain-demos blockchain-technology cryptography hashmap hash blockchain-demo java | blockchain |
leetcode-swift | awards ranking dev global top 200 certificate https leetcode com sergeyleschev a href https leetcode com sergeyleschev img src https github com sergeyleschev sergeyleschev blob main leetcode ranking png raw true alt drawing width 410 a a href https leetcode com sergeyleschev img src https github com sergeyleschev sergeyleschev blob main leetcode medals png raw true alt drawing width 410 a languages swift shell database t sql pl sql mysql concurrency python3 algorithms linked lists binary search hash table queue stack dfs bfs sort heap hash two pointers sliding window tree greedy problems etc | tree linked-list stack queue hash sort dfs heap bfs hash-table binary-search two-pointers sliding-window greedy-problems sql swift shell t-sql sergeyleschev oracle | server |
angular-design-system | bellese angular design system all documentation installation instructions and component examples have been moved to https bellese github io angular design system | os |
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SampleEcommerce | sampleecommerce app sampleecommerce is a cloud native microservices demo application the project consists of a n tier microservices application the application is a web based e commerce app where users can browse items add them to the cart and purchase them the project demonstrates how to develop small microservices for larger applications using containers orchestration service discovery gateway and best practices you are always welcome to improve code quality and contribute it if you have any questions or issues don t hesitate to ask what is microservices architecture the microservices architecture style is an approach for developing small services each running in its process it enables the continuous delivery deployment of large complex applications it also allows an organization to evolve its technology stack why microservices architecture microservices came in a picture for building systems that were too big the idea behind microservices is that there are some applications which can easily build and maintain when they are broken down into smaller applications which work together each component is continuously developed and separately managed and the application is then merely the sum of its constituent elements whereas in traditional monolithic application which is all developed all in one piece motivation developing separately deployable and scalable micro services based on best practies using containerization developing cross platform beautiful mobile apps using xamarin forms developing single page applications using react and including best practices configuring fully automated ci cd pipelines using github actions to mono repo and azure pipelines and appcenter for mobile using modern technologies such as graphql grpc rabbitmq service meshes writing clean maintainable and fully testable code unit testing integration testing and mocking practices using solid design principles architecture overview distributed architecture all the services communicate with the api gateway through rest or rpc these services can be deployed as multiple instances and the requests can be distributed to these instances separately deployed components each component is deployed separately if one component needs changes others don t have to deploy again service components services components communicate with each other via service discovery bounded by contexts it encapsulates the details of a single domain and define the integration with other domains it is about implementing a business capability img src art ecommercearchitecture png style max width 100 list of micro services and infrastructure components table thead tr th th th service th th description th th build status th th endpoints th tr thead tbody tr td align center 1 td td cart api ddd cqrs ef core sql server td td manages customer basket in order to keep items on in memory cache using redis td td soon td td td tr tr td align center 2 td td catalog api crud repository sql server td td catalog management td td soon td td td tr tr td align center 3 td td delivery api crud repository mongodb td td deals with delivery book orders and their statuses td td soon td td td tr tr td align center 4 td td search api net core elasticsearch td td searching items td td soon td td td tr tr td align center 5 td td inventory api dapper repository postgresql td td inventory management td td soon td td td tr tbody table mobile xamarin forms mobile part of project written in xamarin forms for more detail you can read the a href https github com ahror sampleecommerce tree master src frontend sampleecommerce moblie documentation a contributing 1 fork it 2 create your feature branch git checkout b my new feature 3 commit your changes git commit m adds some feature 4 push to the branch git push origin my new feature 5 submit a pull request | asp-net-core microservices docker xamarin-forms react mvvm reactiveui design design-patterns clean-architecture typescript kubernetes | front_end |
IoTResearch | iot reading list iot research papers from 2016 to 2019 this page includes a collection of papers we recommend reading for those interested in studying internet of things security and privacy if you have any suggestions please send a pull request z berkay celik https beerkay github io and xiaolei wang mailto xxw170 psu edu first depth study on iot security focusing on smartthings and motivating the following iot research 2016 ieee s p security analysis of emerging smart home applications http iotsecurity eecs umich edu img fernandes smartthingssp16 pdf sensitive information flow tracking 2016 usenix security flowfence practical data protection for emerging iot application frameworks https www usenix org system files conference usenixsecurity16 sec16 paper fernandes pdf 2018 usenix security sensitive information tracking in commodity iot https www usenix org system files conference usenixsecurity18 sec18 celik pdf access control 2017 ndss contexiot towards providing contextual integrity to appified iot platforms https amir rahmati com dl ndss17 contexiot ndss17 pdf 2017 usenix security smartauth user centered authorization for the internet of things https www usenix org system files conference usenixsecurity17 sec17 tian pdf 2017 access control models fact functionality centric access control system for iot programming frameworks http www corelab or kr pubs sacmat17 fact pdf 2018 usenix security rethinking access control and authentication for the home internet of things iot https www usenix org system files conference usenixsecurity18 sec18 he pdf 2018 ieee secdev tyche risk based permissions for smart home platforms https arxiv org pdf 1801 04609 safety and security violation detection 2019 ndss iotguard dynamic enforcement of security and safety policy in commodity iot https github com beerkay iotresearch will be available soon 2018 usenix atc soteria automated iot safety and security analysis https www usenix org system files conference atc18 atc18 celik pdf 2018 conext iotsan fortifying the safety of iot systems https arxiv org pdf 1810 09551 pdf 2018 ccs on the safety of iot device physical interaction control https wding people clemson edu papers ccs 2018iotmon pdf vulnerability mining 2018 ndss iotfuzzer discovering memory corruptions in iot through app based fuzzing http web cse ohio state edu lin 3021 file ndss18b pdf forensics 2018 ndss fear and logging in the internet of things http seclab illinois edu wp content uploads 2017 12 wang2018fear pdf iot malware 2018 ieee s p understanding linux malware https rud is dl ieee sp 2018 435301a870 pdf fault detection and evaluation 2018 dsn detecting and identifying faulty iot devices in smart home with context extraction https ieeexplore ieee org document 8416520 privacy inference via sensors and defenses 2018 arxiv peek a boo i see your smart home activities even encrypted https arxiv org pdf 1808 02741 2018 arxiv closing the blinds four strategies for protecting smart home privacy from network observers https arxiv org pdf 1705 06809 pdf 2018 arxiv a developer friendly library for smart home iot privacy preserving traffic obfuscation https arxiv org pdf 1808 07432 pdf 2017 arxiv spying on the smart home privacy attacks and defenses on encrypted iot traffic https arxiv org pdf 1708 05044 pdf 2017 arxiv detecting spies in iot systems using cyber physical correlation https faculty washington edu lagesse publications hiddensensordetection pdf attacks against voice control 2018 arxiv understanding and mitigating the security risks of voice controlled third party skills on amazon alexa and google home https arxiv org pdf 1805 01525 pdf 2018 arxiv adversarial attacks against automatic speech recognition systems via psychoacoustic hiding https arxiv org pdf 1808 05665 pdf 2018 arxiv commandersong a systematic approach for practical adversarial voice recognition https arxiv org pdf 1801 08535 pdf 2018 arxiv nonsense attacks on google assistant https arxiv org pdf 1808 01947 pdf trigger action platform measurement and security analysis 2016 chi trigger action programming in the wild an analysis of 200 000 ifttt recipes https par nsf gov servlets purl 10026427 2017 www some recipes can do more than spoil your appetite analyzing the security and privacy risks of ifttt recipes http www andrew cmu edu user liminjia research papers ifttt info flows www2017 pdf 2017 arxiv ifttt vs zapier a comparative study of trigger action programming frameworks https arxiv org pdf 1808 02741 2017 imc an empirical characterization of ifttt ecosystem usage and performance https conferences sigcomm org imc 2017 papers imc17 final41 pdf 2018 ndss decentralized action integrity for trigger action iot platforms http earlence com assets papers dtap ndss18 pdf iot test suite 2018 iotbench a micro benchmark suite to assess the effectiveness of tools designed for iot apps https github com iotbench iotbench test suite iot surveys 2019 arxiv program analysis of commodity iot applications for security and privacy challenges and opportunities https arxiv org pdf 1809 06962 pdf 2019 ieee s p sok security evaluation of home based iot deployments https www computer org csdl proceedings sp 2019 6660 00 666001a208 abs html 2018 arxiv iot security an end to end view and case study https arxiv org pdf 1805 05853 pdf 2017 arxiv a survey of machine and deep learning methods for internet of things iot security https arxiv org pdf 1807 11023 pdf 2017 arxiv understanding iot security through the data crystal ball where we are now and where we are going to be https arxiv org pdf 1703 09809 pdf 2017 ieee s p magazine internet of things security research a rehash of old ideas or new intellectual challenges https arxiv org pdf 1705 08522 pdf 2018 blackhat iot malware comprehensive survey analysis framework and case studies https i blackhat com us 18 thu august 9 us 18 costin zaddach iot malware comprehensive survey analysis framework and case studies wp pdf 2018 arxiv a survey on sensor based threats to internet of things iot devices and applications https arxiv org pdf 1802 02041 pdf notes will be read suggested by dr hongxin hu https people cs clemson edu hongxih | server |
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CountryStatisticManager | country statistic manager anastasions liontos konstantinos kitsios panagiotis vardaris contents countystatisticmanager java project scripts etl python scripts for extracting and tranforming data scripts schemas sql scripts for creating table schemas scripts load sql scripts for loading data into the db scrits etl output the final data after etl proccess ready for loading diagrams uml and eer diagrams etl db sql backup of the database demo link https youtu be xn 2vazjz38 | server |
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auto-sklearn | auto sklearn auto sklearn is an automated machine learning toolkit and a drop in replacement for a scikit learn https scikit learn org estimator find the documentation here https automl github io auto sklearn quick links installation guide https automl github io auto sklearn master installation html releases https automl github io auto sklearn master releases html manual https automl github io auto sklearn master manual html examples https automl github io auto sklearn master examples index html api https automl github io auto sklearn master api html auto sklearn in one image image doc images askl pipeline png auto sklearn in four lines of code python import autosklearn classification cls autosklearn classification autosklearnclassifier cls fit x train y train predictions cls predict x test relevant publications if you use auto sklearn in scientific publications we would appreciate citations efficient and robust automated machine learning matthias feurer aaron klein katharina eggensperger jost springenberg manuel blum and frank hutter advances in neural information processing systems 28 2015 link https papers neurips cc paper 5872 efficient and robust automated machine learning pdf to publication inproceedings feurer neurips15a title efficient and robust automated machine learning author feurer matthias and klein aaron and eggensperger katharina and springenberg jost and blum manuel and hutter frank booktitle advances in neural information processing systems 28 2015 pages 2962 2970 year 2015 auto sklearn 2 0 the next generation matthias feurer katharina eggensperger stefan falkner marius lindauer and frank hutter arxiv 2007 04074 cs lg 2020 link https arxiv org abs 2007 04074 to publication article feurer arxiv20a title auto sklearn 2 0 hands free automl via meta learning author feurer matthias and eggensperger katharina and falkner stefan and lindauer marius and hutter frank booktitle arxiv 2007 04074 cs lg year 2020 also have a look at the blog on automl org https automl org where we regularly release blogposts | automl scikit-learn automated-machine-learning hyperparameter-optimization hyperparameter-tuning hyperparameter-search bayesian-optimization metalearning meta-learning smac | ai |
altschool-cloud-exercises | exercise 10 netmask address 193 16 20 35 29 11000001 00010000 00010100 00100011 netmask 255 255 255 248 29 11111111 11111111 11111111 11111000 wild card 0 0 0 7 00000000 00000000 00000000 00000111 network ip 193 16 20 32 29 11000001 00010000 00010100 00100000 number of host 6 host min 193 16 20 33 11000001 00010000 00010100 00100001 host max 193 16 20 38 11000001 00010000 00010100 00100110 broadcast 193 16 20 39 11000001 00010000 00010100 00100111 | bash-scripting linux-commands adding-users-and-groups-on-linux ansible-and-automation crontab-tasks-and-execution php-install-from-repository | cloud |
fabric-ca | fabric ca developer s guide this is the developer s guide for fabric ca which is a certificate authority for hyperledger fabric fabric ca can issue enrollment certificates and tls certificates for hyperledger fabric deployments see the fabric getting started guide https hyperledger fabric readthedocs io en latest getting started html for information on how to install and use fabric ca with hyperledger fabric sample networks see the fabric ca user s guide operations guide and deployment guide https hyperledger fabric ca readthedocs io for detailed information on how to use and deploy fabric ca the remainder of this guide is intended for developers contributing to fabric ca prerequisites go 1 20 installation or later docker version 17 03 or later docker compose version 1 11 or later contribution guidelines you are welcome to contribute to fabric ca the following are guidelines to follow when contributing 1 see the general information about contributing to fabric http hyperledger fabric readthedocs io en latest contributing html 2 to run the unit tests manually cd gopath src github com hyperledger fabric ca make unit tests the test coverage for each package must be 75 or greater if this fails due to insufficient test coverage then you can run gencov to get a coverage report to see what code is not being tested once you have added additional test cases you can run go test cover in the appropriate package to see the current coverage level warning running the unit tests may fail due to too many open file descriptors depending on where the failure occurs the error message may not be obvious and may only say something similar to unable to open database file depending on the settings on your host you may need to increase the maximum number of open file descriptors for example the osx default per process maximum number of open file descriptors is 256 you may issue the following command to display your current setting ulimit n 256 and the following command will increase this setting to 65536 ulimit n 65536 please note that this change is only temporary to make it permanent you will need to consult the documentation for your host operating system package overview 1 cmd fabric ca server contains the main for the fabric ca server command 2 cmd fabric ca client contains the main for the fabric ca client command 3 lib contains most of the code a server go contains the main server object which is configured by serverconfig go b client go contains the main client object which is configured by clientconfig go 4 util csp go contains the crypto service provider implementation 5 lib dbutil contains database utility functions 6 lib ldap contains ldap client code 7 lib spi contains service provider interface code for the user registry 8 lib tls contains tls related code for server and client 9 util contains various utility functions additional info fvt see fvt tests scripts fvt readme md for information on functional verification test cases updating the cfssl vendored package following are the steps to update cfssl package using version 1 0 8 of govendor tool remove cfssl from vendor folder cd gopath src github com hyperledger fabric ca vendor govendor remove github com cloudflare cfssl rm rf github com cloudflare cfssl clone cfssl repo cd gopath src github com mkdir cloudflare cd cloudflare git clone https github com cloudflare cfssl git add cfssl from gopath to the vendor folder cd gopath src github com hyperledger fabric ca vendor govendor add github com cloudflare cfssl you can optionally specify revision or tag to add a particular revision of code to the vendor folder govendor add github com cloudflare cfssl abc12032 remove sqlx package from cfssl vendor folder this is because certsql newaccessor called by fabric ca requires sqlx db object to be passed from the same package if we were to have sqlx package both in fabric ca and cfssl vendor folder go compiler will throw an error rm rf github com cloudflare cfssl vendor github com jmoiron sqlx remove the packages that are added to the fabric ca vendor folder that are not needed by fabric ca license a name license a hyperledger project source code files are made available under the apache license version 2 0 apache 2 0 located in the license license file hyperledger project documentation files are made available under the creative commons attribution 4 0 international license cc by 4 0 available at http creativecommons org licenses by 4 0 | blockchain distributed-ledger hyperledger | blockchain |
DrowsyDriverDetection | drowsydriverdetection this is a project implementing computer vision and deep learning concepts to detect drowsiness of a driver and sound an alarm if drowsy youtube demo https youtu be 3umlnuxfnfc built a model for drowsiness detection of a driver by real time eye tracking in videos using haar cascades and camshift algorithm used the significant features for each video frame extracted by cnn from the final pooling layer to stitch as a sequence of feature vectors for consecutive frames this sequence 2048 d is given as an input to long short term memory lstm recurrent neural networks rnn which predicts the drowsiness of the driver given the video sequence and sounds an alarm in such a case optimized network weights by adam optimization algorithm technologies used python 2 7 opencv 3 3 0 tensorflow keras cnn rnn lstm steps to run this project 1 run the run extract eyes sh program to track the eyes for different videos training data and to store the patches of the eyes in a folder for every video alert and drowsy 2 use this training data to retrain the cnn model inception v3 model 3 run extract features py to extract the features from the second last layer of the cnn model which is a 2048 d vector and to create a sequence of frames as a single vector to be given as an input to the lstm which is a part of recurrent neural networks rnn 4 run data py and models py 5 finally run train py to get the final predictions for the test sequence of data and the alarm will sound if the model predicts the sequence to be in a drowsy state | drowsy-driver-warning-system computer-vision machine-learning deep-learning neural-network lstm lstm-neural-networks lstm-cells rnn keras tensorflow inception-v3 | ai |
CodeFun | machinelp lp9628 01 programming language 01 programming language 01 python 01 programming language 01 python 02 scala 01 programming language 02 scala 03 c 01 programming language 03 c 02 data structure 02 data structure 01 sword offer 02 data structure 01 sword offer 02 leetcode 02 data structure 02 leetcode 03 03 02 data structure 03 03 deep learning 03 deep learning 01 tensorflow examples 03 deep learning 01 tensorflow examples 01 tensorflow1 0 03 deep learning 01 tensorflow examples 01 tensorflow1 0 02 tensorflow2 0 03 deep learning 01 tensorflow examples 02 tensorflow2 0 03 recommendation 03 deep learning 01 tensorflow examples 03 recommendation 04 cv 03 deep learning 01 tensorflow examples 04 cv 05 nlp 03 deep learning 01 tensorflow examples 05 nlp 06 gan 03 deep learning 01 tensorflow examples 06 gan 07 rl 03 deep learning 01 tensorflow examples 07 rl 02 keras examples 03 deep learning 02 keras examples 03 pytorch examples 03 deep learning 03 pytorch examples tensorflow keras pytorch gan rl 04 machine learning 04 machine learning 01 sklearn examples 04 machine learning 01 sklearn examples 02 sparkml examples 04 machine learning 02 sparkml examples 03 feature engineering examples 04 machine learning 03 feature engineering 05 auto ml dl 05 auto ml dl 01 auto ml 05 auto ml dl 01 auto ml 02 auto dl 05 auto ml dl 02 auto dl automl dl paper 06 model deploying 06 model deploying 01 py backend 06 model deploying py backend 02 c backend 06 model deploying c backend 03 java backend 06 model deploying java backend sdk 07 sql 07 sql sql reference reference md | 10-steps-to-become-a-data-scientist data-scientist-roadmap automl autodl deeplearning-machinelearning | ai |
UgExamples | ugexamples this is the main repository containing all the example apps demonstrating features functionality integrations in scade application development this directory also contains the source code of some mobile apps developed using scade the cross platform mobile application platform for android and ios powered by apple swift check the examples directory to see all examples to run an example choose a running option in the scade ide either the scade simulator ios simulators or android emulators before pressing the play button command r see the getting started guide https docs scade io docs installation to install scade issues please use the issues tab to file any issues bugs or feature requests pertaining to any of these examples in this repo also feel free to use any of the issues labels to enhance better communication contributing we welcome code contributions from developers to this repo please send a pull request if you wish to contribute a change or an update to any of the existing examples in this repo additionally we also appreciate the contribution of new app examples when submitting additions always adhere to the project s style and contribution standards in general we follow the fork and pull git workflow fork the repo on github clone the project to your machine commit changes to your branch push your work back up to your fork send a pull request so that we can examine your changes prior to merging them note remember to merge the most recent changes from upstream before making a pull request https github com scadedoc ugexamples pulls integration examples 1 kyle1 https github com scadedoc ugexamples tree master kyle1 2 pinauthapp https github com scadedoc ugexamples tree master pinauthapp 3 pinauthscadeapp https github com scadedoc ugexamples tree master pinauthscadeapp 4 ugasyncexample https github com scadedoc ugexamples tree master ugasyncexample 5 ugaynchttpclientexample https github com scadedoc ugexamples tree master ugasynchttpclientexample 6 uglogging https github com scadedoc ugexamples tree master uglogging 7 ugsqlliteswift https github com scadedoc ugexamples tree master ugsqlliteswift 8 ugswiftfoundationdemo2 https github com scadedoc ugexamples tree master ugswiftfoundationdemo2 9 ugswiftnioexample https github com scadedoc ugexamples tree master ugswiftnioexample 10 uggesturedemo https github com scadedoc ugexamples tree master uggesturedemo 11 ugswissclockdemo https github com scadedoc ugexamples tree master ugswissclockdemo 12 ugappinappdemo https github com scadedoc ugexamples tree master ugappinappdemo ui controls examples 1 ugbitmapdemo2 https github com scadedoc ugexamples tree master ugbitmapdemo2 2 ugcamerademo https github com scadedoc ugexamples tree master ugcamerademo 3 ugmapcontrol https github com scadedoc ugexamples tree master ugmapcontrol 4 ugmasterpagedemo https github com scadedoc ugexamples tree master ugmasterpagedemo 5 ugmodaldialogdemo https github com scadedoc ugexamples tree master ugmodaldialogdemo 6 ugprogrammaticuidev https github com scadedoc ugexamples tree master ugprogrammaticuidev 7 ugslider https github com scadedoc ugexamples tree master ugslider 8 ugslidercontroldemo https github com scadedoc ugexamples tree master ugslidercontroldemo 9 ugtextlabeldemo https github com scadedoc ugexamples tree master ugtextlabeldemo 10 ugtilesmenudemo2 https github com scadedoc ugexamples tree master ugtilesmenudemo2 11 ugwebviewcontrol https github com scadedoc ugexamples tree master ugwebviewcontrol app examples 1 simplye https github com scadedoc ugexamples tree master simplye 2 ugcalculator https github com scadedoc ugexamples tree master ugcalculator 3 ugcryptoswift https github com scadedoc ugexamples tree master ugcryptoswift faq https github com scadedoc ugexamples issues | apple cross-platform android swift apple-swift | front_end |
ahmed-basher-abduSAmad | ahmed basher abdusamad information technology | server |
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ir-esp-uart | ir esp uart reliable ir library for esp open rtos this library provides an implementation of the hack described by analysir to reliably send ir codes using the secondary uart in an esp8266 module ir codes are sent on gpio2 which is also shared by the built in led usage while most ir libraries provide only mark and space methods to use this library you must wrap your mark space combination within begin end calls typical usage would be common initialization can be done only once ir esp uart init ir esp set frequency 38 command begin ir esp uart begin command ir esp uart mark 9000 ir esp uart space 500 ir esp uart mark 9000 ir esp uart space 500 command end ir esp uart end if you are migrating from another ir library it s typically easy to wrap the mark space calls in a method to add the required begin end calls ir esp uart begin initializes gpio2 for uart usage and initializes a timestamp required for better timings in mark space calls ir esp uart end ends a command and returns gpio2 to gpio analysir https www analysir com blog 2017 01 29 updated esp8266 nodemcu backdoor upwm hack for ir signals | os |
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CS224n | understanding recurrent neural networks this repository is simply me working through cs224d deep learning for natural language processing http cs224d stanford edu spring 2016 there is nothing to see here but if you re even vaguely interested in this topic you should probably take this class harish narayanan https harishnarayanan org 2017 course syllabus lecture 1 intro to nlp and deep learning x video https youtu be qy0oekczkbi slides slides lecture1 pdf notes notes lecture1 pdf suggested reading linear algebra review notes cs229 linalg pdf probability review notes cs229 prob pdf convex optimization review notes cs229 cvxopt pdf x more optimization sgd review https github com hnarayanan cs231n raw master notes optimization pdf from frequency to meaning vector space models of semantics papers live 2934 4846 jair pdf x python tutorial https github com hnarayanan cs231n raw master notes python numpy tutorial pdf lecture 2 simple word vector representations word2vec glove video https youtu be arqn8t1hlxs slides slides lecture2 pdf suggested reading distributed representations of words and phrases and their compositionality papers 5021 distributed representations of words and phrases and their compositionality pdf efficient estimation of word representations in vector space papers 1301 3781 pdf lecture 3 advanced word vector representations language models softmax single layer networks video https youtu be cp9bit4ipvo | ai |
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Blockchain-in-a-Box | blockchain in a box a batteries included ethereum blockchain for bootstrapping the metaverse includes contracts for identity inventory currency and trade can be extended for any kind of marketplace application but the goal is fast free interoperable transfer of all user data from one server authoritative platform to another the ideal use case we imagine is a portal between two separate virtual worlds sharing user data over the blockchain docs https blockchain etherealengine org v1 api docs what is this a node js based rest api ethereum node cache service and smart contracts to deploy your own ethereum based sidechain for identity and durable assets users can transfer assets created on your platform to the ethereum or polygon main networks who is it for 1 javascript developers who want to build interoperable applications with durable digital assets but don t have the resources to write everything from scratch and need a starting point which they can mold to their needs 2 developers interested in creating metaverse scale interoperable applications where users may want to move their assets and identity to other platforms 3 developers of existing applications who cannot reasonably remove their database from their application and want to mirror this data onto blockchain or vice versa 4 organizations who wish to build blockchains with other organizations and need a standard and conventional way to do so full gui management of a custodial sidechain what are sidechains they are either 1 custodial assets are moved to a parallel chain with its own consensus mechanism security 2 non custodial assets are held state is secured by smart contracts on ethereum can survive an attack of the sidechain any resources to help me learn what s going on how to code a crypto collectible erc 721 asset tutorial ethereum https www youtube com watch v ypbgjppc1d0 build your first blockchain app using ethereum smart contracts and solidity https www youtube com watch v coq5dg8wm2o node js and express js full course https www youtube com watch v oe421epjebe redis caching with node js https www youtube com watch v oajq1mq3dfi building an ipfs app with node js https www youtube com watch v rmlo9 wfkyu pinata cloud the broken token how pinata can make nfts more durable https www youtube com watch v 0iuave a0fi where can i find the code everything is in the packages folder you shouldn t need to change much beyond setup packages api the api is what you ll interact with generate wallets mint tokens etc additional documentation here packages api readme md developer reference api server https blockchain etherealengine org v1 api docs packages cache the cache server listens to the chain for events and posts them to redis elasticache additional documentation here packages cache readme md packages ethereum ethereum ethstats remix ide and all related services as docker kubernetes yaml files additional documentation here packages ethereum readme md packages market a marketplace for your blockchain additional documentation here packages market readme md packages market service backend services for the marketplace additional documentation here packages market readme md packages terraform eks simple deployment to amazon eks using terraform additional documentation here packages ethereum readme md setup and installation configure env file in the root folder create a clone of env default file and name it env update the content of env file with your resources setup pinata account pinata allows you to upload and manage files on ipfs it provides ipfs api through which you can easily perform ipfs pinning service 1 open https pinata cloud and signup login 2 navigate to https pinata cloud keys and create a new key make sure to check admin key switch button in the modal 3 update env file with following entries from generated key pinata api key pinata secret api key deployment local deployment local deployment can be done using kubectl and minikube full instructions are here packages ethereum readme md deployment to aws aws setup and deployment are done via terraform but there are also instructions on deploying directly to eks here packages ethereum readme md and here packages terraform eks readme md contributing pull requests are gladly accepted for bug fixes please submit an issue if you can fix the bug submit a pr and link the issue for features please submit an issue with your intention to add the feature and make a draft pr as soon as possible please try to minimize changes to number of files or make multiple pull requests to minimize confusion or possible introduction of bugs | ethereum nft token api blockchain dapp | blockchain |
SWTU_FDD | fault detection and diagnosis in a sour water treatment unit this project gathers the databases developed during my masters in chemical engineering in epqb ufrj the chemical process of interest is a two column sour water treatment unit swtu as shown in the figure below to remove h sub 2 sub s and nh sub 3 sub from industrial sour water dynamicsimulation https user images githubusercontent com 87589223 126053770 b4d75a7d 074f 41a7 951b 693a10780030 png the units static and dynamic behavior was simulated using aspen plus dynamics the controllers added in the simulation are described in the table below p align center img src https user images githubusercontent com 87589223 126053792 c8bf3720 c55c 4f23 9a64 a2fe4ec3df11 png p fault free and faulty scenarios were tested based on industrial experts advice a brief description of the classes including fault free and simulated faults is exposed in the following table along with its limit values p align center img src https user images githubusercontent com 87589223 126053863 df325d37 d305 49f6 90ff 5dd1665479c5 png p fault free simulations are based on small disturbances to stimulate controllers reactions and create a more diverse database those same disturbances were also applied before some fault applications making a more reality adherent process behavior there are seven different kinds of disturbances in this work and they can be defined as small increases and decreases in certain variables listed as follows sw1 mass flow h sub 2 sub s in sw1 nh sub 3 sub in sw1 sw1 temperature sw5 mass flow cw5 mass flow and amg1 temperature the first three displayed faults are related to the process feed stream sw1 considering significant variances in its mass flow or composition to check how the system reacts if changes happen in the inlet stream simulating typical flooding phenomena that occur in real columns faults 4 and 5 were performed by the insertion of a bias to reduce the pressure on the top of both columns between the columns exit and the controllers entrance those faults are associated with sensor problems as the controllers are receiving deceiving pressure values in a real industrial process that offset could be caused by clogging although the last tested fault is also related to sw1 the objective is to evaluate how the process reacts to the overheat phenomena as c1 s heat duty operational range is typically narrow small changes could increase gas acid s temperature reducing separation efficiency increasing the process s inlet temperature is a way to simulate that once it would stimulate changes in the column s heat duty all the simulations run to generate the database were set with programmable tasks based on scripts to alter the variables automatically always in an s ramp shape using the function sramp available in the software each of the six faults and fault free also had several runs including a fouling effect as if a heat exchanger had already a certain service period to represent that h3 s overall heat transfer coefocient or u value was reduced in a single step between 3 and 3 5 at the very beginning of the simulation the dynamic data were processed incluiding noise and delay addition both intending to make the data more realistc and dynamic as each sample is composed of two sampling timestamps the data were transformed into two databases train and test by concatenating the data from multiple simulation runs the databases were composed by independent simulation runs so that all the data from each run go to the same database avoiding leaking information between datasets also all the classes must be present in both databases including simulations with and without the fouling effect for each class so that training is eficient to evaluate the test dataset another concern was class distribution throughout the databases as the classification performed was based on faults in a chemical process most of the data should be related to normal operation the data were standardized to make the ai techniques training process more manageable and less biased the train and test databases available in this project are composed of 37519 and 22525 samples respectively with about 60 to 65 of fault free data the table below displays the distribuition of the datasets p align center img src https user images githubusercontent com 87589223 126053624 034ee077 09e1 4770 83c5 c44d40b0b90e png p the standardized datasets are composed of 27 columns each of the 13 controlled process variables with delay that is in two different sampling times t and t 1 and also the class of the sample this last entry is an integer that varies from 0 to 6 meaning fault free and each of the 6 faults studied the standardization was performed in python 3 using the functions fit and transform from standardscaler available in sklearn preprocessing scikit learn 0 23 2 | server |
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mupen64plus-ae | mupen64plus ae mupen64plus android edition ae is an android user interface for mupen64plus please visit the official forum http www paulscode com forum index php for support and discussion img src https f droid org badge get it on png alt get it on f droid height 80 https f droid org packages org mupen64plusae v3 alpha nightly builds download nightly builds from continuous integration build status build actions actions https github com mupen64plus ae mupen64plus ae actions workflows build yml build https github com mupen64plus ae mupen64plus ae actions workflows build yml badge svg build instructions 1 download and install the prerequisites android studio https developer android com studio index html during the installation make sure the latest sdk and ndk if running windows make sure you install git python awk and required microsoft visual c redistributable i e cmake 3 18 1 requires microsoft visual c redistributable 2015 and that the binaries are in your path environment variable 2 clone the mupen64plus ae repository and initialize the working copy git clone https github com mupen64plus ae mupen64plus ae git 3 open the project using android studio 4 build and run the app from android studio select build make project to build select run run app to run | front_end |
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MDEN | mden mobile development engineer notes | front_end |
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ikago-web | ikago web ikago web is a front end interface for ikago https github com zhxie ikago license ikago web is licensed under the mit license license | front_end |
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PipePipe | the project is hosted on both codeberg https codeberg org nullpointerexception pipepipe and github https github com infinityloop1308 pipepipe it is preferred to open issue on codeberg but it is ok if you open it on github please star this repo if you like it hr p align center img src https i imgur com q7r0xtu png width 150 p h2 align center b pipepipe b h2 h4 align center alternative android streaming front end of bilibili niconico youtube and more h4 p align center a href https f droid org packages infinityloop1309 newpipeenhanced img src https fdroid gitlab io artwork badge get it on png alt get it on f droid height 80 a a href https apt izzysoft de fdroid index apk infinityloop1309 newpipeenhanced img src assets izzyondroid png alt get it on izzyondroid height 80 a p hr services list bilibili niconico youtube soundcloud not maintained media ccc not maintained bandcamp not maintained peertube not maintained screenshots img src fastlane metadata android en us images phonescreenshots 00 v2 png width 640 fastlane metadata android en us images phonescreenshots 00 v1 png img src fastlane metadata android en us images phonescreenshots 01 v2 png width 160 fastlane metadata android en us images phonescreenshots 01 v1 png img src fastlane metadata android en us images phonescreenshots 02 v2 png width 160 fastlane metadata android en us images phonescreenshots 02 v2 png img src fastlane metadata android en us images phonescreenshots 03 v2 png width 160 fastlane metadata android en us images phonescreenshots 03 v2 png img src fastlane metadata android en us images phonescreenshots 04 v1 png width 160 fastlane metadata android en us images phonescreenshots 04 v1 png br img src fastlane metadata android en us images phonescreenshots 05 v1 png width 160 fastlane metadata android en us images phonescreenshots 05 v1 png img src fastlane metadata android en us images phonescreenshots 06 v1 png width 160 fastlane metadata android en us images phonescreenshots 06 v1 png img src fastlane metadata android en us images phonescreenshots 07 v1 png width 160 fastlane metadata android en us images phonescreenshots 07 v1 png img src fastlane metadata android en us images phonescreenshots 08 v1 png width 160 fastlane metadata android en us images phonescreenshots 08 v1 png br img src fastlane metadata android en us images phonescreenshots 09 v1 png width 160 fastlane metadata android en us images phonescreenshots 09 v1 png img src fastlane metadata android en us images phonescreenshots 10 v1 png width 160 fastlane metadata android en us images phonescreenshots 10 v1 png img src fastlane metadata android en us images phonescreenshots 11 v2 png width 160 fastlane metadata android en us images phonescreenshots 11 v2 png img src fastlane metadata android en us images phonescreenshots 12 v2 png width 160 fastlane metadata android en us images phonescreenshots 12 v1 png new client features bullet comments live chats show comments of replies search filters filter items in local playlists android 13 support music player mode sort playlists remove duplicate items of local playlists open timestamp in the main player long click to append all related items to playlist fetch all the items in the online playlist contribute issues and prs are welcomed please note that i will not accept service requests due to my limited time anyone interested in creating their own service is encouraged to fork this repository warning this fork is very far from the newpipe repo if you want to contribute your translation see translation md translation md note please do not open any issue about merging niconico bilibili to newpipe in this repo teamnewpipe newpipe https github com teamnewpipe newpipe or teamnewpipe newpipeextractor https github com teamnewpipe newpipeextractor this project is for my personal use if you open an issue i won t encounter most likely i will not make any fixes you may also want to check newpipe https github com teamnewpipe newpipe or newpipe x sponsorblock https github com polymorphicshade newpipe special thanks socialsisteryi bilibili api collect https github com socialsisteryi bilibili api collect for providing some bilibili api lists aioilight https github com aioilight for providing some code of niconico service | android bilibili mediaplayer niconico youtube | front_end |
AltSchool-Cloud-Exercises | altschool cloud exercices altschool cloud exercices cloud3 jpg this is the beginning of my journey in altschool cloud engineering track second semester i am excited and pumped up to begin an exciting journey into the cloud introduction readme md overview overview my process my process task task exercise 1 exercise 1 readme md exercise 2 exercise 2 exercise2 md exercise 3 exercise 3 exercise3 md exercise 4 exercise 4 exercise4 md exercise 5 exercise 5 exercise5 md exercise 6 exercise 6 exercise6 md exercise 7 exercise 7 exercise7 md exercise 8 exercise 8 exercise8 md exercise 9 exercise 9 exercise9 md exercise 10 exercise 10 exercise10 md semester project semester project semester project md author author overview learning cloud engineering with altschool this is the first week of learning cloud engineering with altschool and it has been the most exciting and tiring week for me this project is a documentation of my progress in my chosen track task exercise 1 create an account on github and a private repo for documenting your exercises you can name it altschool cloud exercises invite your instructors to the project my process i will be documenting my progress on a weekly basis this will include all weekly tasks and exercises i have included links to each exercise for easy navigation kindly click on the link above to navigate to the exrcise of interest thanks for following along happy viewing author website bukola testimony https bukola testimony github io my portfolio website twitter bukolatestimony https twitter com bukolatestimony | cloud |
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TODO-_JSON_server | todo json server | server |
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Modern-Computer-Vision-with-PyTorch | modern computer vision with pytorch a href https www packtpub com product modern computer vision with pytorch 9781839213472 utm source github utm medium repository utm campaign 9781839213472 img src https static packt cdn com products 9781839213472 cover smaller alt modern computer vision with pytorch height 256px align right a this is the code repository for modern computer vision with pytorch https www packtpub com product modern computer vision with pytorch 9781839213472 utm source github utm medium repository utm campaign 9781839213472 published by packt explore deep learning concepts and implement over 50 real world image applications what is this book about deep learning is the driving force behind many recent advances in various computer vision cv applications this book takes a hands on approach to help you to solve over 50 cv problems using pytorch1 x on real world datasets by the end of this book you ll be able to leverage modern nn architectures to solve over 50 real world computer vision problems confidently this book covers the following exciting features train a nn from scratch in numpy and then in pytorch implement 2d and 3d multi object detection and segmentation generate digits and deepfakes with autoencoders and advanced gans manipulate images using cyclegan pix2pixgan stylegan2 and srgan combine cv with natural language processing to perform ocr image captioning and object detection combine cv with reinforcement learning to build agents that play pong and self drive a car deploy a deep learning model on the aws server using fastapi and docker implement over 35 nn architectures and common opencv utilities if you feel this book is for you get your copy https www amazon com dp 1839213477 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a errata chapter01 chapter01 readme md chapter03 chapter03 readme md chapter06 chapter06 readme md instructions and navigations all of the code is organized into folders the code will look like the following def accuracy x y model model eval let s wait till we get to dropout section get the prediction matrix for a tensor of x images prediction model x compute if the location of maximum in each row coincides with ground truth max values argmaxes prediction max 1 is correct argmaxes y return is correct cpu numpy tolist following is what you need for this book this book is for beginners to pytorch and intermediate level machine learning practitioners who are looking to get well versed with computer vision techniques using deep learning and pytorch if you are just getting started with neural networks you ll find the use cases accompanied by notebooks in github present in this book useful basic knowledge of the python programming language and machine learning is all you need to get started with this book with the following software and hardware list you can run all code files present in the book chapter 1 18 software and hardware list chapter software required os required 1 18 minimum 8 gb ram intel i5 processor or better windows mac os x and linux any nvidia 8 gb graphics card gtx1070 or better minimum 50 mbps internet speed python 3 6 and above pytorch 1 7 google colab can run in any browser all the notebooks can be run directly on colab https colab google com using the open in collab https colab research google com assets colab badge svg https github com packtpublishing modern computer vision with pytorch button that can be found at the start of every notebook if you wish to run the notebooks locally ensure you have a cuda compatible gpu with drivers installed instructions are given here https github com packtpublishing modern computer vision with pytorch blob master install cuda drivers md we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https static packt cdn com downloads 9781839213472 colorimages pdf related products other books you may enjoy hands on natural language processing with pytorch 1 x packt https www packtpub com product hands on natural language processing with pytorch 1 x 9781789802740 amazon https www amazon com dp 1789802741 pytorch computer vision cookbook packt https www packtpub com product pytorch computer vision cookbook 9781838644833 amazon https www amazon com dp b0862cx2zl get to know the author v kishore ayyadevara leads a team focused on using ai to solve problems in the healthcare space he has more than 10 years experience in the field of data science with prominent technology companies in his current role he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams kishore has filed 8 patents at the intersection of machine learning healthcare and operations prior to this book he authored four books in the fields of machine learning and deep learning kishore got his mba from iim calcutta and his engineering degree from osmania university yeshwanth reddy is a senior data scientist with a strong focus on the research and implementation of cutting edge technologies to solve problems in the health and computer vision domains he has filed four patents in the field of ocr he also has 2 years of teaching experience where he delivered sessions to thousands of students in the fields of statistics machine learning ai and natural language processing he has completed his mtech and btech at iit madras download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781839213472 https packt link free ebook 9781839213472 a p | ai |
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altschool-cloud-eng-assignment-tracker | altschool cloud eng assignment tracker this is a repo meant for documenting all my altschool https altschoolafrica com cloud engineering assignments the goal is to update this repo each time a new assignment is given in order not to miss anyone click here https github com uncleobinna altschool cloud exercises to access the exercises | cloud |
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ZephyrNative | zephyr native rapid mobile development android for now aim of the project create tool for fast mobile development simple not too complex applications with the power of web technology and native compilation to platforms only native code on mobile no cordova no javascript vm for android only java small apk from 70 kb fast as lightning install work in progress | mobile android rapid webapp | front_end |
nymea | nymea open source iot edge server p align center a href https nymea io img src https nymea io downloads img nymea logo svg width 300 a p nymea n ai mea is an open source iot edge server the plug in based architecture allows to integrate protocols and apis with the build in rule engine you are able to interconnect devices or services available in the system and create individual scenes and behaviours for your environment quick start install nymea on a raspberry pi p align center img src https nymea io downloads img nymea pi svg width 300 p we have created an image for your raspberry pi all models that comes with an array of plugins for different smart devices use the raspberry pi imager https www raspberrypi com software and select nymea as operating system you ll have the choice between a headless nymea core setup or a kiosk image that contains nymea core and nymea app for raspberry pis with touch screen alternatively the image can be downloaded and flashed manually from here https downloads nymea io images raspberrypi we recommend the latest raspberry pi os bullseye core https downloads nymea io images raspberrypi nymea core image raspios bullseye latest zip or kiosk https downloads nymea io images raspberrypi nymea kiosk image raspios bullseye latest zip image get nymea app here table align middle tr td p a href https apps apple com us app nymea app id1400810250 img border 0 align middle alt ios badge src https nymea io downloads img app store appstore png width 200 p td td p a href https play google com store apps details id io guh nymeaapp hl en pcampaignid mkt other global all co prtnr py partbadge mar2515 1 img border 0 align middle alt android badge src https play google com intl en us badges static images badges en badge web generic png width 250 p td td p a href https open store io app io guh nymeaapp img border 0 align middle alt openstore badge src https open store io badges en us png width 200 p td td p a href https apps apple com us app nymea app id1488785734 img border 0 align middle alt macos badge src https nymea io downloads img app store macos png width 200 p td td p a href https snapcraft io nymea app img border 0 align middle alt snap badge src https nymea io downloads img app store snap store png width 200 p td td p a href https downloads nymea io nymea app windows latest img border 0 align middle alt windows badge src https nymea io downloads img app store windows svg width 200 p td tr table manual download files of nymea app can be found here https downloads nymea io nymea app a detailed description how to install and getting started with nymea can be found in the nymea user documentation https nymea io documentation users installation getting started getting help if you want to present your project or want to share your newest developments you can share it in our forum https forum nymea io if you are facing any troubles don t hesitate to reach out for us or the community members we will be pleased to help you chat with us on telegram http t me nymeacommunity or discord https discord gg tx9ycpd developing with nymea a detailed documentation on how to develop with nymea is available on the nymea developer documentation https nymea io documentation developers network discovery when starting nymead as user without root privileges the network device discovery will not available due to missing raw socket permission if you still want to make use of this feature the binary capabilities need to be adjusted sudo setcap cap net admin cap net raw eip usr bin nymead this will allow nymead to create raw sockets for arp and icmp network discovery tools even when nymead gets started as user without root privileges license nymea is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation version 3 of the license | iot qt5 server iot-platform c-plus-plus automation nymea hacktoberfest | server |
wanda | p align center img alt wonderflow src https repository images githubusercontent com 446459287 4c4d001e 4566 4976 8d01 87540f6d3719 p h3 align center a href https design wonderflow ai b documentation s b a a href contributing md b contributing b a h3 | react components design-systems ui hacktoberfest design | os |
wendigo | wendigo img src https user images githubusercontent com 5960567 54274028 6641ca80 4587 11e9 9e92 f27daa2e3910 png align right width 150px by angrykoala npm https img shields io npm v wendigo svg https www npmjs com package wendigo gitlab pipeline status https img shields io gitlab pipeline angrykoala wendigo master svg label gitlab ci https gitlab com angrykoala wendigo pipelines github workflow status https img shields io github workflow status angrykoala wendigo test label github actions https github com angrykoala wendigo actions a proper monster for front end automated testing wendigo w nd o simplify your front end and end to end automated testing using puppeteer https github com googlechrome puppeteer install it with npm install save dev wendigo warning this documentation refers to wendigo 2 if you are using previous versions of wendigo go here https github com angrykoala wendigo blob 1 13 1 readme md consider the following example using just puppeteer javascript await page click my btn await page waitforselector my modal const modaltext await page evaluate const modalelement document queryselector my modal return modalelement textcontent assert strictequal modaltext button clicked the same test can be written like this with wendigo javascript await browser click my btn await browser waitfor my modal await browser assert text my modal button clicked features assertion library built in cookies localstorage and webworkers handling requests mocker plugins full access to puppeteer methods easy and flexible query system with support for css selectors and xpath docker and ci friendly easy to setup just node required authorization helpers contents getting started getting started requirements requirements installing wendigo installing wendigo usage usage api api wendigo wendigo class browser browser assert assert cookies cookies console console localstorage localstorage requests requests auth auth webworkers webworkers dialog dialog errors errors selectors selectors injected scripts injected scripts dom element dom element plugins plugins writing a plugin writing a plugin examples examples development development troubleshooting troubleshooting acknowledgements acknowledgements license license getting started requirements to start using wendigo for testing or browser automation make sure you ve got nodejs https nodejs org en 8 16 0 or higher and npm installed in your system you can check if they are installed and their versions with the following commands bash node v bash npm v installing wendigo you should install wendigo in your npm project usually as a dev dependency bash npm install save dev wendigo usage you can use it with your favorite test suite or standalone in a javascript file js const wendigo require wendigo async function getmypageheader const browser await wendigo createbrowser await browser open http my page const text await browser text h1 await browser close return text getmypageheader then text console log text queries with wendigo most methods in wendigo will receive a selector parameter to query for dom elements on which execute actions or get data unless specified these queries can be performed by passing a css selector e g div container an xpath selector text my text or passing a dom element dom element for example js const myelement await browser query div await browser text div my element text await browser text myelement my element text using wendigo with typesscript wendigo 2 0 and higher contains typings so importing it in a tyescript project is enough for types support typescript import as wendigo from wendigo if you find any problem please check our troubleshooting troubleshooting for solutions or fill an issue https github com angrykoala wendigo issues new if it appears to be a bug or lacking feature with wendigo more information and guides on how to use wendigo available at the wiki https github com angrykoala wendigo wiki using puppeteer directly wendigo is intended to be used as a full wrapper of puppeteer so usually accessing puppeteer directly is not needed however the browser class provide direct access to puppeteer classes browser page browser context browser corebrowser check the api for those properties for more info api wendigo class wendigo is the only class exported by the package it provides the methods necessary to create browsers and disconnect from chrome can be imported with require wendigo createbrowser settings will create and return a promise to a browser browser instance it will automatically launch and connect puppeteer and chrome settings an optional object with the configuration for the browser log false if true it will log all the console events of the browser logrequests false if true all requests made by the browser will be logged in the console incognito false if true the browser will open as an incognito browser useragent if defined the default user agent will be overridden nosandbox sets the option no sandbox when opening puppeteer this option will also be set if the env variable no sandbox is set check troubleshooting troubleshooting timezone sets the browser s timezone e g utc asia tokyo bypasscsp true if set to false puppeteer may fail if content security policy is set in the page proxyserver null if defined chromium will run with the option proxy server set to the given address defaulttimeout 500 sets the default timeout for wait methods except browser wait cache true if true requests cache will be enabled any settings that can be passed to puppeteer can be passed to createbrowser for example headless true if true the browser will run on headless mode slowmo 0 slows the execution of commands by given number of milliseconds examples js const browser await wendigo createbrowser using default options js const browser await wendigo createbrowser headless false slowmo 500 using options to see what s happening stop will stop and disconnect all the browsers it should be called after finishing all the tests registerplugin name plugin assertions registers a new plugin for more information check plugins plugins this must be called before createbrowser for the plugins to work optionally an object can be passed with the following options name plugin assertions clearplugins removes all plugins from wendigo this will affect all newly created browsers browser the browser instance is and interface with the page class of puppeteer attributes page puppeteer page class https github com googlechrome puppeteer blob master docs api md class page allows access to puppeteer api if needed js await browser page evaluate document queryselector h1 loaded true if the page has already opened and loaded incognito true if the browser is configured as incognito page cache if the requests cache is active context returns puppeteer s browsercontext https github com googlechrome puppeteer blob master docs api md class browsercontext corebrowser returns puppeteers s browser https github com googlechrome puppeteer blob master docs api md class browsercontext class for direct requests methods all the methods in browser return a promise than can easily be handled by using async await open url options opens the given url in the browser js await browser open http localhost 8000 the following options can be passed viewport viewport config to set when opening the browser uses the same syntax as setviewport querystring querystring to be appended to the url can be a string or object avoid using this parameter if a query string is already present in the url geolocation options to override geolocation same as using setgeolocation headers sets extra http headers before opening the page same as using requests setheaders dismissalldialogs this will automatically dismiss any native dialog alert prompt when appearing if no protocol is defined e g https http will be used openfile path options opens the given file same options as open can be passed the file will be passed by appending file to the absolute path js await browser open static index html close close the browser it should be called after finishing using the browser instance avoid creating a new browser before closing the previous one if possible having multiple open browsers will cause performance degradation in your tests js await browser close evaluate cb args evaluates given callback in the browser passing n arguments returns the puppeteer s result of the evaluation js const selector h1 const elementtext await browser evaluate s return document queryselector s textcontent selector my title this method unlike browser page evaluate will automatically parse any argument if possible query selector childselector queries the given selector css xpath or domelement and returns a domelement dom element if multiple elements match only the first will be returned returns null if no element found js const element await browser query h1 optionally 2 parameters can be passed a query will be performed only on the nested elements of the first selector js const element await browser query div const children await browser query element p first paragraph under element div note that any result that is not an element node will be filtered for xpath requests queryall selector childselector returns an array with all the domelement dom element matching the given selector js const elements await browser queryall h1 elements length 2 optionally queryall supports 2 parameters the query will then be performed only on the elements under the first selector note that any result that is not an element node will be filtered for xpath requests elementfrompoint x y given the coordinates in pixels as two numbers returns the topmost domelement in that position or null if no element is present js const element await browser elementfrompoint 500 150 await browser text element my title selectpage index selects the given page a k a tab to be used by wendigo keep in mind that tabs are never changed automatically unless explicitly selected or closed with closepage js await browser click btn new tab await browser wait 100 waits for new tab to be loaded await browser pages length is 2 await browser selectpage 1 goes to newly opened tab csp bypass will not be enabled in the newly opened tabs if you rely on it it may be necessary to reload the tab after it has been opened with browser refresh closepage index closes the page with given index if the closed page is the current active page it will change to the new page with index 0 reloading it in the process if no more pages exists the browser will close with browser close automatically pages returns all puppeteer s pages https github com googlechrome puppeteer blob master docs api md class page one per tab or popup warning all pages related methods are still under revision and its behavior may heavily change in future releases setcontent html sets the page content from a string can be used instead of browser open js await browser setcontent h1 title h1 addscript scriptpath executes the given script in the browser context useful to set helper methods and functions this method must be called after the page is already loaded if another page is loaded the scripts won t be re executed if these scripts are required for a plugin to work remember to execute this method on the afteropen hook it is heavily recommended to only use this to load helper functions and not execute anything that might cause side effects anything loaded as a script may interfere with the behavior of the page or wendigo it is recommended to always check if the object of function you are loading already exists before loading remember that wendigoutils wendigoquery and wendigopathfinder objects are required for wendigo to work and overriding them will cause problems class selector returns and array with the classes of the first element found throws if no element found js const classes await browser class div container main returns container main another class using a domelement js const node await browser query div container main const classes await browser class node returns container main another class value selector returns the value of the first element with given selector throws if no element or value found js const value await browser value input my input attribute selector attributename return the attribute value of the first element found with given selector throws if no element is found returns if the attribute is set but no value is given and null if the attribute doesn t exists js const classattribute await browser attribute my element class returns my element another class const hiddentattr await browser attribute my hidden element hidden returns const hiddentattr2 await browser attribute not hidden element hidden returns null styles selector returns an object with all the computed css styles of the first element matching the given selector throws if no element is found js const styles await browser styles h1 my title styles color rgb 255 0 0 style selector style returns the value of the given style of the first element matching the given selector returns undefined if the style doesn t exists throws if no element found js const style await browser style h1 my title color rgb 255 0 0 checked selector returns true if the first element matching the given selector checkbox is checked if the value is not a checkbox and doesn t have checked property set it will return undefined throws if no element is found text selector returns an array with the texts of the elements matching the given selector returns the same value as element textcontent property js const texts await browser text p my first paragraph my second paragraph innerhtml selector returns an array with the innerhtml strings of all the elements matching the given selector js await browser innerhtml p my b first b paragraph elementhtml selector returns an array with the html strings of all the elements matching the given selector js await browser elementhtml p p my b first b paragraph p title returns the page title html returns the page html as string it will return the html as it was before performing any actions pdf options generates a pdf if options is a string or contains the value path a pdf file will be generated on given path buffer https nodejs org api buffer html will be returned otherwise js await browser pdf my page pdf const mybuffer await browser pdf await browser pdf using puppeteer pdf options path page pdf width 10cm this methods is just a wrapper on puppeteer s pdf method the full list of possible options can be found here https github com googlechrome puppeteer blob master docs api md pagepdfoptions frames returns all the frames https github com googlechrome puppeteer blob v1 8 0 docs api md class frame attached to the page url returns the current url of the page click selector index clicks all the elements with the matching selector if the index parameter is set only the nth element will be clicked returns the number of elements clicked js await browser click button btn an array of domelements is also supported as selector optionally if two numbers are passed position x y in pixels will be clicked in this case null with be returned instead of the clicked elements warning elements are clicked sequentially if one of them is a link to an external page subsequent clicks will fail clicktext selector text index clicks all the elements matching given text returns the number of elements clicked js await browser clicktext click me optionally a selector can be passed as first argument to only click elements under the given selector if an index is passed only the nth element found will be clicked be aware of the type passed down to index if selector is not passed js await browser clicktext click me 2 clicks the second element await browser clicktext click me 2 will search for an element with selector click me and text 2 await browser clicktext container click me 2 clicks the second element with given text under the element container clicktextcontaining selector text index same as clicktext but matches with any text containing the given string clickandwaitfornavigation selector timeout 500 clicks an element and waits until a navigation event is triggered recommended for links to different pages keep in mind that not all the clicks will trigger a navigation event js await browser url my page account await browser clickandwaitfornavigation home button await browser url my page home clickandwaitfornavigation may delay up to 100ms after the given timeout while waiting for the page to load waitandclick selector timeout 500 waits for an element to exists and be visible before clicking it useful for clicking elements that may have a delay before appearing domelements selectors not supported waitandtap selector timeout 500 waits for an element to exists and be visible before tapping it domelements selectors not supported waitandtype selector text timeout 500 waits for an element to exists and be visible before typing text in it domelements selectors not supported waitandcheck selector timeout 500 waits for an element checkbox to exists and be visible before checking it domelements selectors not supported tap selector index performs a touchscreen tap action on all the elements matching the given selector if the index parameter is set only the nth element will be tapped returns the number of elements tapped the interface is compatible with browser click if two numbers are passed the given coordinates are clicked check selector checks the first element matching given selector setting its checked property to true throws if no element is found uncheck selector unchecks the first element matching given selector setting its checked property to false throws if no element is found focus selector focus the first element matching the given selector blur selector unfocus the first element matching the given selector hover selector hovers over the first element matching the given selector draganddrop source target drags the source selector and drops it on target js await browser draganddrop my draggable target note that this method emulates expected events of this behavior instead of emulating mouse interaction scroll value xvalue vertically scrolls the page to the given value on pixels an optional xvalue can be passed for horizontal scrolling if value is a selector or domelement the page will scroll until that element is at view screenshot options takes a screenshot of the page this is just a direct interface to puppeteer s screenshot method https github com googlechrome puppeteer blob master docs api md pagescreenshotoptions and will use the same options please check puppeteer docs for the updated usage it will return the image as a string base64 or buffer depending on the encoding if an option path is defined the image will be created on given path the possible options from puppeteer s docs are path the file path to save the image to the screenshot type will be inferred from file extension if path is a relative path then it is resolved relative to current working directory if no path is provided the image won t be saved to the disk type specify screenshot type can be either jpeg or png defaults to png fullpage when true takes a screenshot of the full scrollable page defaults to false clip an object which specifies clipping region of the page should have the fields x y width and height omitbackground hides default white background and allows capturing screenshots with transparency defaults to false encoding the encoding of the image can be either base64 or binary defaults to binary screenshotofelement selector options takes a screenshot of the first element matching the given selector will fail if the element is not found the supported options are the same as screenshot js const base64image await browser screenshotofelement my dashboard encoding base64 type selector text options types given text in the first element matching the selector if a value is already present writes the new value at the beginning throws if no elements is found the following options passed as an object are supported delay if a delay is given it will delay the given ms for each key press js await browser type input my input my input keypress key count press a keyboard key the key can be the name of any key supporter by puppeteer https github com googlechrome puppeteer blob master lib uskeyboardlayout js if an array is passed all the keys will be pressed consecutively if count parameter is passed all the keys will be pressed that given count times js await browser keypress enter press enter once await browser keypress escape 2 press enter twice await browser keypress enter escape enter uploadfile selector path sets the value of an input file element matching given selector path can be absolute or relative to the current working directory js await browser uploadfile input my file input my file path txt select selector value will select the given value in the select tag of the first element matching the given selector removing all previous selections returns an array with the values that could be selected correctly value can be a string or an array if the select is multiple all elements in value will be selected if not only the first element in the select options will will throw if no elements were found js await browser select select language select spanish english returns spanish english if the option doesn t have a value the text should be provided clearvalue selector clears any value that exists in any of the elements matched by the given selector setting the value to js await browser clearvalue input my input setattribute selector attributename value sets the attribute of given name to a string value if null is passed the attribute will be removed from the element addclass selector classname adds the given css class to the element removeclass selector classname removes the given css class from the element if exists wait ms 250 waits the given milliseconds waitfor selector timeout 500 args waits for given selector to exists and be visible with the given timeout in milliseconds js await browser waitfor popup if a function is passed instead of a selector it will wait for that function to resolve in the browser context to true the optional arguments are passed to the function js await browser waitfor s waits for 2 or more elements to be in the page const docs document queryselectorall s return docs length 2 600 my elements domelements selectors not supported waituntilnotvisible selector timeout 500 waits until the given selector is no longer visible or doesn t exists with the given timeout in milliseconds js await browser waituntilnotvisible toast waitforurl url timeout 500 waits for the page to have the given url the url can be a string or a regexp js await browser click a await browser waitforurl my url waitfortext text timeout 500 waits for the given text to exists js await browser waitfortext click me await browser clicktext click me waitfornavigation timeout 500 waits until next page is loaded recommended after following a link to a different page keep in mind that a navigation within a spa won t necessarily trigger a navigation event waitfornavigation may delay up to 100ms after the given timeout while waiting for the page to load waituntilenabled selector timeout 500 waits until the first element matching the given selector has the attribute disabled set to null waitforpageload waits until a dom ready event is fired this method will also wait until wendigo is ready to perform assertions on the given page findbytext selector text returns an array with the elements with text content matching the given text js const elements await browser findbytext my first paragraph elements length 1 optionally a selector can be passed as first argument to perform a text search on children of that element only findbytextcontaining selector text returns an array with all the elements with a text that contains the given text js const elements await browser findbytextcontaining paragraph elements length 2 optionally a selector can be passed as first argument to perform a text search on children of that element only findbyattribute attributename attributevalue returns an array with all elements having an attribute matching the given name and value if no value is assigned it will match all elements with that attribute regardless of the value use empty string as value to match all the elements with an attribute without value e g div hidden js const hiddenelements await browser findbyattribute hidden returns all the elements with the hidden attribute const paswordelements await browser findbyattribute name password find all elements with a name attribute and value password findbylabeltext labeltext given a label text returns all the inputs associated to the label through the for attribute html label class label1 for input1 my label label input id input1 js const input await browser findbylabeltext my label returns an array containing iput input1 findcsspath element will return the css path string e g body div button of a domelement js const elem await browser query my element const path await browser findcsspath elem body div p my element findxpath element will return the xpath string e g html body div button of a domelement tag selector returns the tag name of the first element matching the given selector keep in mind that the tag will always be returned lowercase returns null if no element was found js await browser tag my header h1 setvalue selector value sets the given value on all the elements matching the given selector returns the number of elements changed throws if no element found js await browser setvalue input new val returns 1 await browser assert value input new val this method won t trigger certain events use type and select when possible options selector returns the selector options values of the first element matching the given selector throws if no element is found if the element doesn t have options i e is not a selector an empty array is returned js const options await browser options selector my selector value1 value2 selectedoptions selector returns all the selected options of the first element matching the given selector if no value is set the text of the option will be returned throws if no element is found back navigates to previous page in history forward navigates to next page in history refresh reloads current page setviewport viewportconfig sets the configuration of the page viewport using the same config as puppeteer method https github com googlechrome puppeteer blob master docs api md pagesetviewportviewport js await browser setviewport width 300 unlike puppeteer setviewport the current values will be used for the new viewport for any option not set settimezone timezone sets browser timezone valid timezones can be found here https cs chromium org chromium src third party icu source data misc metazones txt rcl faee8bc70570192d82d2978a71e2a615788597d1 setmedia mediaoptions sets css media options options can be a string to define a type or an object with the following properties type defines media type emulation can be print or string passing null disables media emulation features receives an array of objects containing name and value of the css media feature to override supported names are prefers colors scheme and prefers reduced motion this method is a wrapper over puppeteer s emulatemediafeatures https github com puppeteer puppeteer blob master docs api md pageemulatemediafeaturesfeatures and emulatemediatype https github com puppeteer puppeteer blob master docs api md pageemulatemediatypetype triggerevent selector eventname options creates and dispatch a dom event in the elements matching the given selector the event dispatched will have the name given and all the options will be passed down to the native event constructor with the options bubbles cancelable and composed supported js await browser triggerevent button click triggers a click event on all buttons this won t emulate mouse movement like browser click await browser triggerevent listener my custom event triggers a custom event to an element that may have a listener atached mockdate date options mocks the browser s date object so it returns the expected date instead of current date when using new date without parameters or date now the first parameter must be a javascript date object the following options are supported freeze true if set to true the new date objects will always return the given date as current date if false the expected date will increase normally as time passes js await browser mockdate new date 2010 10 6 mocks to 6 sept 2010 await browser evaluate const d new date 6 sept 2010 const d2 new date 2011 10 10 10 sept 2011 js await browser mockdate new date 2010 10 6 freeze false await browser evaluate const d new date 6 sept 2010 plus some milliseconds await browser wait 1000 await browser evaluate const d new date 6 sept 2010 plus one second and some milliseconds keep in mind that there may be different timezones between the browser and node using timestamps is recommended cleardatemock clears the date mock if any returning to the native date object setcache enabled enables or disables the requests cache keep in mind that this method returns a promise that resolves to when the change is effective setgeolocation option overrides browser s location options can contain latitude longitude and accuracy as numbers geolocation returns the current location position https developer mozilla org en us docs web api geolocation getcurrentposition as an object the object contains the following attributes if available accuracy altitude altitudeaccuracy heading latitude longitude speed js const location await browser geolocation location latitude 60 location longitude 20 to access geolocation you may need to first override the geolocation permission overridepermissions url permissions grants given permissions to given web permissions can be a string or array of strings with the options listed here https github com googlechrome puppeteer blob master docs api md browsercontextoverridepermissionsorigin permissions js await browser overridepermissions http myweb comm geolocation accelerometer assert browser assert provide some out of the box assertions to easily write tests that are readable without having to specifically perform evaluations all the assertions have a last optional parameter to define a custom assertion message all assertions will return a promise that will fail if the assertion fails unless specified any selector will support css xpath and domelements assert exists selector msg asserts that at least one element with given selector exists js await browser assert exists h1 main title assert visible selector msg asserts that the at least one element matching the selector is visible an element will considered visible if exists the computed style doesn t contain display none or visibility hidden opacity is not 0 all the parents are visible assert tag selector expected msg asserts that at least one element matching the given selector has the given tag name js await browser assert tag my header h1 assert text selector expected msg asserts that at least one element matching the given selector has the expected string or regex if expected is an array all texts in it should match it matches against the value of element textcontent property js await browser assert text p my first paragraph assert textcontains selector expected msg asserts that at least one element matching the given selector contains the expected text expected text can be an array of strings in this case all expected texts should match at least one element js await browser assert textcontains p my first assert title expected msg asserts that the page title matches the expected string or regex assert class selector expected msg asserts that the first element matching the selector contains the expected class js await browser assert class div container main div container assert url expected msg asserts that the current url matches the given string or regexp assert value selector expected msg asserts that the first element matching the selector has the expected value js await browser type input my input dont panic await browser assert value input my input dont panic assert element selector msg asserts that exactly one element matches given selector same as elements selector 1 assert elements selector count msg asserts the number of element that matches given selector the count parameter can be a number of the exact number of elements expected or an object with the following properties atleast expects at least the given number of elements atmost expects up to the given number of elements equal expects the exact number of elements html p paragraph 1 p p class second paragraph 2 p js await browser assert elements p 2 ok await browser assert elements p equal 2 ok await browser assert elements p atleast 1 atmost 3 ok await browser assert elements p first 0 ok await browser assert elements p second 2 fails await browser assert elements p second atleast 1 ok assert attribute selector attribute expected msg asserts that at least one element element matching the given selector contains an attribute matching the expected value if no expected value is given any not null value for the attribute will pass the expected value can be a string or regex js await browser assert attribute hidden class class hidden class await browser assert attribute hidden class hidden to pass a custom message without specifying an expected value you can pass undefined js await browser assert attribute hidden class hidden undefined hidden class doesn t have attribute hidden you can check an attribute doesn t exists passing null as expected argument or using assert not attribute if the element doesn t exists the assertion will fail assert style selector style expected msg asserts that the first element matching the given selector has an style with the expected value the assertion will throw an error if no element is found js await browser assert style h1 color rgb 0 0 0 assert href selector expected msg asserts that the any matching the given selector contains an attribute href with expected value js browser assert href a foo html browser assert href link styles css same as browser assert attribute selector href expected msg assert innerhtml selector expected msg asserts that at least one element matching the given selector has the expected innerhtml the expected html can be either a string or a regex value the assertion will throw if no element is found js await browser assert innerhtml p my b first b paragraph assert elementhtml selector expected msg asserts that at least one element matching the given selector has the expected html the expected html can be either a string or a regex value the assertion will throw if no element is found js await browser assert elementhtml p p my b first b paragraph p assert options selector expected msg assets that the first element with given selector has the expected options value expected can be a string if only one option is given or an array if multiple options are given all expected options must match in the same order js await browser assert options select my select value1 value2 assert selectedoptions selector expected msg assert that the first element with given selector has the expected options selected expected can be a string if only one option is given or an array all the selected options must match the expected options in the same order assert global key value msg asserts that the global object window has the given key with the expected value if not value or undefined value is provided it will assert that the key exists with a not undefined value js browser assert global localstorage browser assert global my val dontpanic assert checked selector msg asserts that the first element matching the given selector has a checked value set to true assert disabled selector msg asserts that the first element matching the given selector is disabled has attribute disabled assert enabled selector msg asserts that the first element matching the given selector is enabled doesn t have attribute disabled assert focus selector msg asserts that an element matching the given selector is focused js browser click btn browser assert focus btn assert redirect msg asserts that the opened url is a redirection negative assertions most of assertions have a negative counterpart that can be used with browser assert not most of the not assertions are simply the inverse of the positive version assert not exists selector msg asserts that no element matching given selector exists js await browser not exists h1 foo bar assert not visible selector msg asserts that no elements with given selector is visible if no element matches it will be considered as not visible as well and will pass assert not tag selector expected msg asserts that no element matching the given selector has the given tag name js await browser assert not tag my main header h4 assert not text selector expected msg asserts that no element matching the given selector matches the expected text if expected is an array no text in it should match any element with given selector js await browser assert not text p this text doesn t exists await browser assert not text p this text doesn t exists neither do this assert not textcontains selector expected msg asserts that no elements matching the given selector contain the expected text if expected is an array no text in it should be contained any element with given selector js await browser assert not textcontains p doesn t exist assert not title expected msg asserts that the title of the page is not the given string assert not class selector expected msg asserts that the first element matching the selector doesn t contain the expected class it will throw if no element is found assert not url expected msgs asserts that the url of the page doesn t match the expected string assert not value selector expected msg asserts that the first element with the given selector doesn t have the expected value assert not element selector msg asserts that the number of elements matching the given selector is 0 assert not attribute selector attribute expected msg asserts that no element matching the given selector doesn t contain an attribute with the expected value if no expected value is given any not null value on the attribute will fail js await browser assert not attribute not hidden class class hidden class await browser assert not attribute not hidden class hidden to pass a custom message without specifying an expected value you can pass undefined js await browser assert not attribute hidden class href undefined hidden class has attribute href the assertion will throw if no element is found keep in mind that passing null as expected value will assert that the attribute exists and it is not recommended assert not style selector style expected msg asserts the first element matching the selector doesn t has a style with given value assert not href selector expected msg asserts that no element matching the given selector doesn t contain an attribute href with the expected value same as browser assert not attribute selector href expected msg assert not innerhtml selector expected msg asserts that at no element matching the given selector has the expected innerhtml the expected html can be either a string or a regex value the assertion will throw if no element is found js await browser assert not innerhtml p not b a b paragraph assert not elementhtml selector expected msg asserts that at no element matching the given selector has the expected html the expected html can be either a string or a regex value the assertion will throw if no element is found js await browser assert not elementhtml p div not b a b paragraph div assert not selectedoptions selector expected msg assert that the first element with given selector doesn t have the expected options selected expected can be a string if only one option is given or an array the assertion will only fail if all the expected options match the selected options in the same order assert not global key value msg asserts that the global object window doesn t have the given key with the expected value if not value or undefined value is provided it will assert that the key doesn t exist or it is undefined assert not checked selector msg asserts that the first element matching the given selector has a checked value set to false note that if the element doesn t have a checked value i e is not a checkbox this assertion will throw assert not disabled selector msg asserts that the first element matching the given selector is not disabled same as assert enabled assert not enabled selector msg asserts that the first element matching the given selector is not enabled same as assert disabled assert not focus selector msg asserts that none of the elements matching the given selector is focused assert not redirect msg asserts that the current opened page is not a redirection cookies the module browser cookies provides a way to easily handle cookies through puppeteer s api all methods return promises most methods in the cookies module accept or return a puppeteer s cookie object https github com googlechrome puppeteer blob master docs api md pagecookiesurls cookies all returns all the cookies in the current page as an object with key being the name and value a string with the cookie value js const cookies await browser cookies all username arthur dent email arthur dent com cookies get name url returns the cookie object with given name returns undefined if the cookie doesn t exists cookies from current page will be returned by default js const cookie await browser cookies get username cookie name username cookie value arthur dent if parameter url is set cookies from the given url domain will be returned cookies set name value sets the value of the cookie with given name if it already exists it will be replaced the value can be a string it will only set the cookie value or a cookie object https github com googlechrome puppeteer blob master docs api md pagecookiesurls js await browser cookies set username marvin await browser cookies set another cookie value foo secure false the possible parameters of the object are name required value required url domain path expires unix time in seconds httponly secure samesite can be strict or lax cookies delete name deletes the cookie with given name if exists optionally an array can be passed and all the cookies will be removed won t do anything if the cookie doesn t exists js await browser cookies delete username await browser cookies delete username email optionally an object with same interface as puppeteer https github com googlechrome puppeteer blob master docs api md pagedeletecookiecookies can be passed to delete cookies from different pages this object can provide the following arguments name required domain path url cookies clear deletes all the cookies of the current page js await browser cookies clear cookies assertions it is possible to assert the existence or not of a cookie in the current url with the following assertions assert cookies name expected msg asserts that the cookie with the given name exists if the expected parameter is passed it will check that the cookie has that value js browser assert cookies username browser assert cookies username arthur dent assert not cookies name expected msg asserts that the cookie with given name doesn t have the expected value if no expected value is passed it will check that the cookie doesn t exists is undefined js browser assert not cookies not a cookie browser assert not cookies username not user console browser console provides a list of all logs generated by the current page a k a console methods the logs are returned as an instance of log log note that none of these methods requires the use of async await console all returns an array with all the logs generated by the page js const logs browser console all logs 0 text hello world console clear clear all the current logs note that logs are cleared when browser close is called but not when a new page is opened console filter options returns an array with all the logs matching the given parameters options can be type the log type log info error it must be a string matching puppeteer s console types https pptr dev product puppeteer version v1 5 0 show api consolemessagetype some of the most common types are accessible through browser console logtypes text a string or regex matching the log output if no options are passed all the logs will be returned the options can be used together js const errorlogs browser console filter type browser console logtypes error errorlogs 0 text oh no an error js const logs browser console filter text hello logs 0 text hello world console assertions the following assertions can be used to check the existence of a console log assert console options count msg assets that at least one console event with given options exists if count is set asserts that the exact number of events exists the options can be text asserts for the console event to have a text matching the given string or regex type asserts that the event is of the given type log info error js await browser assert console text hello world type browser console logtype log objects logged will be converted to string using json stringify if the object fails to be stringified circular structure object object will be returned log the class log provides an abstraction over puppeteer s consolemessage class https pptr dev product puppeteer version v1 5 0 show api class consolemessage log has the following attributes text the log text if multiple parameters were passed to console method these will be concatenated with spaces type the log type string message the native puppeteer log instance logtypes all the log types are strings but some of the most common types are accessible in browser console logtypes for more possible types check puppeteer docs https pptr dev product puppeteer version v1 5 0 show api consolemessagetype log debug info error warning trace keep in mind that this list does not contain all possible log types localstorage the module browser localstorage provides a wrapper around the browser localstorage all the methods return promises localstorage getitem key returns the item with the given key returns null if no element exists js const value await browser localstorage getitem my key returns my value localstorage setitem key value sets a localstorage entry js await browser localstorage setitem my key my value localstorage removeitem key removes the item with given key localstorage clear removes all the items on the store localstorage length returns the number of items in the store js const itemslength await browser localstorage length 3 localstorage all returns all the items in localstorage as an object localstorage assertions assertions related local storage can be accessed through browser assert localstorage assert localstorage exist key msg asserts that the item with given key exists in the localstorage i e not null js browser assert localstorage exist my key alternatively if an array is passed as key all the elements will be checked assert localstorage value key expected msg asserts that the item with given key has the expected value js browser assert localstorage value arthur dontpanic alternatively an object can be passed instead of key expected with the elements that should be checked js browser assert localstorage value arthur dontpanic marvin the paranoid assert localstorage length expected msg asserts that the localstorage has the expected length assert localstorage empty msg asserts that the localstorage is empty i e length 0 all these assertions have the negative browser assert localstorage not requests the requests module allows to get filter and mock the requests made by the browser since the page was opened returned requests objects are puppeteer s requests https github com googlechrome puppeteer blob master docs api md class request requests filter returns a filter over the requests check filtering requests filtering requests for examples requests all returns all requests ordered by when it was dispatched js await browser requests all requests mock url options mocks all the requests to an url sending a fake response instead if a method get post is specified only requests to given method will be mocked the url can be a full url string http or a regex if multiple mocks match a requests the more specific will be used the following options are supported status response status code defaults to 200 headers optional response headers contenttype if set equals to setting content type response header body optional response body it can be a string or a json serializable object delay optional delay to wait for the response to be fullfilled in ms auto if set to false the request won t be fullfilled automatically and a manual trigger must be defined default to true continue if set to true the request will be continue to the server and the real response will be returned method defines the method get post to mock empty to mock any method querystring if set only requests with the exact query string will be mocked accepts string or object by default all requests with the given url regardless of the query string will be mocked unless a querystring is set in the url or in the options redirectto if set the mock will return the response of the given url instead of the original call maintaining the query string keep in mind that the redirected request won t trigger any mocks e g requests mock http localhost 8010 redirectto http localhost 9010 will change the port where all request in the page are sent this object properties will be used with the interface of puppeteer s respond method https github com googlechrome puppeteer blob master docs api md requestrespondresponse js all requests made to api will return 200 with the given body browser requests mock http localhost 8000 api body result ok mock will return a requestmock object with the following read only properties called if the mock has been called timescalled the times the mock has been called response the response the mock is returning read only url mocked url immediate if the mock will return immediately delay 0 auto if the request will be completed automatically method mock expected method querystring mock expected querystring parsed as an object and the following methods waituntilcalled timeout 500 waits until the mock is called it will also add a slight delay to give the browser time to process the response js const mock browser requests mock http localhost 8000 api body result ok mock called false mock timescalled 0 callapi result ok mock called true mock timescalled true the mock will also provide an assertion interface in mock assert with the following assertions called times msg asserts that the mock has been called the given number of times if times parameter is not given the assertion will throw if no calls were made postbody expected msg asserts that the mock has been called with the given body in the request expected can be an string object or regexp js const mock browser requests mock http localhost 8000 api body result ok await mock assert called 0 callapi my request post requests with given body await mock assert called 0 await mock assert postbody my request all mocks are removed on browser close if the mock is not auto it can be manually triggered with the method trigger this method cannot be called with auto mocks js const mock browser requests mock http localhost 8000 api body result ok auto false callapi mock trigger trigger supports an optional response that will be used instead of the mock default response it uses the same syntax body status requests removemock url options removes the mock with the given url if the original mock has a method or querystring these must be provided in options requests clearrequests clears the list of requests requests clearmocks remove all the request mocks requests getallmocks returns an array with all the current request mocks set in the browser requests waitforrequest url timeout 500 waits until a request with given url is done this will resolve immediately if the requests was already made to wait without taking in account past requests use waitfornextrequest url can be a string or regexp js await browser requests waitforrequest my url requests waitforresponse url timeout 500 waits until a response to the given url is done this will resolve immediately if the response was already received to wait without taking in account past requests use waitfornextresponse url can be a string or regexp requests waitfornextrequest url timeout 500 waits until next request with given url is done if the request was already made this method will wait until next one url can be a string or regexp requests waitfornextresponse url timeout 500 waits until next response with given url is received if the response was already received this method will wait until next one url can be a string or regexp requests setheaders headers sends the given headers on every http requests on top of default headers js await browser requests setheaders custom dontpanic await browser open my url note that authorization header should be set with auth module where possible filtering requests to filter the requests made by the browser you can use browser requests filter for example to filter requests with status code of 200 js const filteredrequests await browser requests filter status 200 requests the available filters are requests filter url value filters by the given url the url can be a string or a regex js await browser requests filter url http localhost 8002 api requests requests filter method value filters by request method get post requests filter status value filters by response status 200 400 requests filter fromcache value true filters whether the response comes from the browser cache or not requests filter responseheaders headers filters requests where the response has all the given headers with the given values the expected value can be a string or regex js await browser requests filter responseheaders content type html requests filter ok isok true filters request which response are considered successfull status is between 200 and 299 filters can be joined to perform a filter of several fields js filters all the post requests made to any url with api that are not cached and returned a success code await browser filter url api method post ok fromcache false requests requests filter postbody expected filter requests by post body the body can be a string object or regex js filters all delete requests made to with json body await browser filter url api method delete postbody id 5 requests requests filter resourcetype resource filter requests by given resource type xhr fetch possible resource types are defined here https github com googlechrome puppeteer blob master docs api md requestresourcetype requests filter resourcetype contenttype filter responses by content type header text css charset utf 8 accepts both string or regexp requests filter pending filter requests by pending response not retrieved requests filter responsebody expected filters requests by response body the body can be a string object or regex js const byresponsefilter await browser requests filter url api responsebody response ok requests keep in mind that some filters like status require the requests to be finished use await browser waitforresponse before filtering to make sure the requests was completed requests assertions assertions related requests can be accessed through browser assert requests like filters request assertion don t need await and can be concatenated all the assertions will check that at least one request with the given constraints was made assert requests url expected msg asserts that at least one request is made to the given url the url can be a string or regex js await browser assert requests url api assert requests method expected msg asserts that at least one request was made with the given method get post js await browser assert requests method get assert requests status expected msg asserts that a response was received with the given status js await browser assert requests status 200 note that this method requires the request to be finished assert requests responseheaders expected msg asserts that a response was received with the given headers the expected variable is an object with one or more key values representing the expected headers the value can be either a string or regex js await browser assert requests responseheaders content type html assert requests ok expected true msg asserts that an successful response was received status is between 200 and 299 or false if false is given assert requests postbody expected msg asserts that a request contains the given post body regardless of method the expected value can be a string regex or object js await browser assert requests postbody status ok assert requests responsebody expected msg asserts that a request response contains the given body the expected value can be a string regex or object js await browser assert requests responsebody response ok assert requests pending msg asserts that at least one request is still pending no response received assert requests resourcetype resourcetype msg asserts that at least one request has the given resource type fetch xhr assert requests contenttype expected msg asserts that at least one response has given content type header text css charset utf 8 accepts both string or regexp assert requests exactly expected msg asserts that the exact given number of requests match the assertions expected can be any positive number or 0 js await browser assert requests url localhost 800 api asserts that at least one request is made to given url await browser assert requests url localhost 800 api exactly 2 asserts that 2 requests are made to given url await browser assert requests url localhost 800 api exactly 0 asserts that no requests are made to given url concatenating multiple assertions is possible js asserts that a post method was done to the api endpoint await browser assert requests method post url localhost 8000 api negative assertions are not supported for requests auth the auth module provides some utilities to simplify the process of some standard authorization methods in webpages by setting up an extra http header authorization on every request the following methods will setup said header accordingly to the auth method auth http credentials it will setup a basic http authentication https developer mozilla org en us docs web http authentication with the credentials given user and password if no credentials given it will remove the current auth value js await browser auth http user arthur dent password dontpanic42 auth bearer token sets the bearer token used in jwt https jwt io and oauth2 among others auth clear will clear any authorization token currently active including those not set up by the auth module webworkers the webworkers module allows to retrieve all the webworkers in the current page webworkers all returns all the webworkers currently executing in the page each webworker will have the following properties url returns the webworker file url worker returns the puppeteer s worker instance https pptr dev product puppeteer version v1 5 0 show api class worker note that this method does not require await js const webworkerslist browser webworkers all webworkerslist length 3 webworkers assertions assert webworker options msg assert that at least one webworker is running the following options can be passes url matches only the webworkers with given url count matches exactly the given number of webworkers running js await browser assert webworkers url foo js at least one webworker with given url running await browser assert webworkers at least one webworker running await browser assert webworkers count 0 no webworkers running dialog the dialog module allows handling of native dialog such as those created by alert and prompt dialog all returns the full ordered list of all dialogs triggered since the page was opened dialog clear clears the list of dialogs it will invalid dismisslast and may cause waitfordialog to misbehave dialog waitfordialog timeout 500 waits until the next dialog is triggered returns a promise with the dialog object if the dialog was already triggered it will resolve the promise immediately js browser dialog all length 0 const p browser click dialog trigger note that await is not used yet const dialog await browser dialog waitfordialog dialog text my dialog message await dialog dismiss await p after the dialog is dissmissed we can safely await until click event is completed browser dialog all length 1 keep in mind that until the dialog is dismissed or confirmed events such as click will be blocked so await shouldn t be used on events that may trigger an alert unless dismissalldialogs option is set to true dialog dismisslast dismiss the last dialog triggered if any and if it is still active this can safely be used whether a dialog appeared or not or it has already been dismissed problems may happen if the alert is dismissed manually for example by using the not headless mode dialog objects dialog objects are returned by the all and waitfordialog methods these provide a wrapper around puppeteer s dialog https pptr dev product puppeteer show api class dialog with the following attributes text the dialog message type the dialog type alert beforeunload confirm promp same as those provided by puppeteer handled whether the dialog has already been handled or not dismiss dismisses the dialog if the dialog is a prompt null will be returned accept text accepts the dialog if the dialog is a prompt text will be returned if not it will behave just like dismiss remember that the option dismissalldialogs on browser open will automatically dismiss any dialog errors wendigo errors can be accessed through wendigo errors these are errors that will be thrown by wendigo browser assertionerror extends from node js assertion error it will be throw for any assertion queryerror error defining a problem with a dom query generally thrown as an unexpected result of a query made in an action or assertion timeouterror timeout error it will be thrown in waitfor methods keep in mind that this error is not the same as puppeteer s timeouterror https pptr dev product puppeteer show api class timeouterror fatalerror defines a fatal error with puppeteer e g a connection error injectscripterror defines an error injecting scripts on the page this may be caused on open if the option bypasscsp is set to false this error extends from fatalerror selectors most wendigo methods and assertions will require a selector to localize one or more elements in the dom unless specified any method will accept 3 different kinds of selectors css such as my id or container any selector supported by the standard document queryselector xpath the standard xml path language https en wikipedia org wiki xpath allowing more complex queries domelement the result of browser query can be directly used as a selector check dom element dom element injected scripts for wendigo to work properly it must inject some scripts into the web page within the browser s context at runtime usually these scripts will only be used by wendigo but you can still access them when using evaluate in your code or when writing a plugin the following objects are declared as global variables wendigoutils wendigo utils contain several methods and utilities for wendigo it can be accessed in the browser s context in an evaluate callback through the global variable wendigoutils or window wendigoutils the following methods are exposed isvisible element returns true if an element is visible queryelement selector returns the first element matching the given selector the selector can be css xpath or an element queryall selector returns all the elements matching the given selector css xpath or dom element xpathquery xpath returns all the elements matching the given xpath selector getstyles element returns all the styles of the given element mockdate timestamp freeze mocks the browsers date and time used by browser mockdate cleardatemock clears the datetime mock if any user by browser cleardatemock findcsspath node returns the full css path of a dom node as a string findxpath node returns the full xpath of a dom node as a string wendigoquery the variable wendigoquery or window wendigoquery exposes several utilities regarding wendigo querying system these shouldn t be used by user s code or plugins as wendigoutils already exposes the methods to perform these queries wendigopathfinder exposes utilities to find the full css path or xpath of an element this should not be directly accessed as the methods findcsspath and findxpath in wendigoutils provide the interface to access these utilities dom element any query that returns a dom node will return an instance of domelement this class provides an abstraction over puppeteer s elementhandle https github com googlechrome puppeteer blob master docs api md class elementhandle and can be used as a selector for other methods and assertions a domelement contains the following attributes element puppeteer s elementhandle object name name to be used when displaying errors a domelement also provides the following methods query selector performs a css query within the children of the element returns the first element matching the selector queryxpath selector performs an xpath query within the children of the element queryall selector similar to query returns all the elements matching the given css selector getattribute attributekey returns the value of the attribute with given name or null if not defined click clicks the given element tap taps the given element tostring returns a readable string of the domelement used for displaying errors focus focus the element hover hovers the mouse over the element type text options types the given text on the element input element a delay between keystrokes can be set in the options parameters plugins wendigo supports plugins to extends its capabilities with custom features and assertions these plugins can be added with registerplugin wendigo vue plugin https github com angrykoala wendigo vue plugin this plugin support several methods and assertions to use along with pages using vue https vuejs org writing a plugin to write a plugin you must write classes defining the new methods and then registering them in wendigo with registerplugin before creating a browser js class myplugin constructor browser the plugin will receive the browser instance in the constructor this browser browser getheadertitle custom method to find our title return this browser text h1 header title 0 findkoalas return this browser findbytextcontaining koala beforeopen options this hook will be called anytime browser open is executed you can perform actions required for your plugin to run whenever a new page is opened such as setting up cache keep in mind that the page won t be accesible yet beforeclose this hook will be called anytime browser close is executed you can perform actions required for your plugin when the page is close keep in mind that this will only be called on browser close and not on any page loading afteropen options this hook will be called after the page is opened and loaded you can use this hook to start performing actions with evaluate or adding custom scripts with this browser page addscripttag class mypluginassertions the assertions will be under browser assertions mypluginname constructor browser myplugin plugin assertions receive browser and plugin in the constructor this myplugin myplugin therearekoalas count const koalas this myplugin findkoalas length if count koalas 0 throw new assertionerror no koalas node s assertionerror else if count koalas count throw new assertionerror no enough koalas headertitle title if this myplugin getheadertitle title throw new assertionerror invalid title wendigo registerplugin koalafied myplugin mypluginassertions const browser wendigo createbrowser more code browser koalafied getheadertitle koalas are great browser assert koalafied headertitle koalas are great browser assert koalafied therearekoalas wendigo registerplugin receives 3 parameters name name to be used to access the plugin it must be different than other plugins and should not collide with wendigo core modules plugin class to be used as plugin accessed under browser name this class will receive browser as constructor parameter and 2 methods can be implemented as hooks beforeopen called when browser open is called before opening the page beforeclose called when browser close is called before closing the page assertion class to be used as plugin s assertions it can be accessed on browser assertion name the constructor will receive both the browser and the core plugin as parameters registerplugin also accepts a single object containing the data in the following structure name plugin assertion keep in mind that both the plugin and the assertions are optional but at least one must exists to register the plugin instead of classes if a plain function is provided as a plugin or assertion it will be attached directly to browser or browser assertion without calling new the function will receive the same arguments as the constructor of the plugin as well as any extra parameter passed to the function js function mypluginassertionfunc browser myplugin count const koalas myplugin findkoalas length if count koalas 0 throw new assertionerror no koalas node s assertionerror else if count koalas count throw new assertionerror no enough koalas wendigo registerplugin koalafied myplugin mypluginassertions browser assert koalafied note the assertion is called directly publishing a plugin if you want to create a new plugin and publish it in the npm store please follow these steps 1 make sure your package exports a single object compatible with the interface described above to make it easier to import do not export the classes individually 2 make sure your code is tested using node js 8 and above 3 set wendigo as a peer dependency https docs npmjs com files package json peerdependencies in you package json if you are writing tests also set wendigo as a dev dependency never as a normal dependency 4 wendigo usually follows semantic versioning https semver org so your plugin should be compatible with any minor version above the version you wrote it but a lot of things may break so it is good to make sure your plugin still works properly in the latest version after a release 5 make a pr to update this document withyour plugin 6 let people know about it examples these are some examples on how to use wendigo more examples can be found here https github com angrykoala wendigo wiki testing a simple page with mocha and wendigo javascript const assert require assert const wendigo require wendigo describe my tests function this timeout 5000 recommended for ci let browser beforeeach async browser await wendigo createbrowser aftereach async for more speed this method could be only executed after all tests pass if your tests do not rely on state changes await browser close after async await wendigo stop after all tests finished it page title async await browser open http localhost await browser assert text h1 main title my webpage await browser assert title my webpage it open menu async await browser open http localhost await browser assert not visible menu await browser click btn open menu await browser assert visible menu development these instructions assume node 8 0 0 and npm installed 1 clone the git repository dev branch 2 npm install 3 npm test to execute the tests npm run lint to execute the linting tests npm run dummy server to start the testing server on port 8002 before doing a commit or pr to the dev branch make sure both the tests and all lint tests pass this can be checked by running npm run prepublishonly architecture wendigo the main class exported by the module provides the base interface to instantiate the browser class browserfactory class takes care of the creation of the browser instance browser provides all the api to connect retrieve information and perform assertions on a webpage the browserfactory will compose this class from multiple mixins and modules mixins a browser is divided in several mixins found on the folder with the same name these will be composed in the final browser class assertions all core assertions in the browser class module related assertions exists within each module folder modules a module represents an object that will be attached to the browser with a given name e g localstorage modules are different from mixins in that the modules are attached as a separate class instance whereas mixins are composed into the same class note that each module can be composed by 2 parts one attached to the main browser class and other to the assertion module each module and all related code is stored in a separate folder injection scripts these scripts will be injected at runtime into the browser and are required to perform most of wendigo actions within the browser context these scripts cannot access other parts of the code and the rest of the code should not use these scripts out of the browser s context these scripts can be used in the context of browser evaluate by the user and other plugins troubleshooting error failed to launch chrome no usable sandbox this error may appear when running wendigo on certain systems and in most ci services the sandbox setup can be bypassed by setting the environment variable no sandbox true for example no sandbox true npm test maxlistenersexceededwarning possible eventemitter memory leak detected warning this can be caused by executing wendigo createbrowser multiple times without calling browser close on previous browsers causing all of them to be kept open this may cause performance issues in your tests and it is recommended to close every browser after using it running tests with travis ci running tests using puppeteer s require disabling the sandbox running mode this can easily be achieved by passing the environment variable no sandbox true this can be done either as part of the test execution command as a travis secret env variable or in the travis yml file itself it is recommended to add travis retry to allow travis to execute the tests multiple times as browser based setup may fail frequently on travis workers yml language node js os linux node js stable sudo false env no sandbox true script travis retry npm test cache directories node modules example of travis yml file running tests with gitlab ci it is recommended to add retry 2 to allow the ci to execute the tests multiple times as browser based setup may fail frequently on ci workers due to hardware resources needs yml image angrykoala wendigo before script npm install test stage test retry 2 script npm test example of gitlab ci yml running tests with github actions to run tests with wendigo on gh actions the default node workflow can be used using npm test npm test to add a single retry in case tests fail once yml name test on push jobs test runs on ubuntu latest steps uses actions checkout v1 uses actions setup node v1 with node version 12 run npm install name test run sudo apt get update sudo apt get install y libgbm dev npm test npm test env ci true example of github workflows test yml using wendigo with docker wendigo con be running on docker just as any other application using puppeteer but an official image is provided to ease the setup the following is an example of a dockerfile using node 10 and wendigo dockerfile from angrykoala wendigo copy package json installs your up it must have wendigo as a dependency run npm ci copies the rest of your app copy app runs your tests cmd npm test warning this image is updated and maintained but it is still an early feature and may not be as stable as using wendigo directly assertion failed messages without error if you are using node 10 and puppeteer 1 4 0 or less you may experience messages such as assertion failed no node found for selector this is due to a change in how console assertion works in node 10 and how puppeteer uses it these messages won t affect the tests if the messages are a big problem for you consider downgrading your node js version upgrading puppeteer if possible or overriding console assert console assert remember to check puppeteer troubleshooting https github com googlechrome puppeteer blob master docs troubleshooting md page doesn t work the same way on headless mode wendigo runs on headless mode by default it can be disabled with the options headless false making it run on a graphical mode this can cause some inconsistencies as headless and graphical mode won t behave exactly the same way on all scenarios these differences can be caused by multiple reasons most commonly graphical mode requires more resources and execute actions slower than headless mode making it less stable and causing some timeouts and race conditions setting the slowmo option may help on making headless mode behave more similar to a real browser the user agent provided by puppeteer is also different when executing in headless browser some webpages may rely on this to detect bots or to change the display this can easily be avoided by explicitly setting the user agent on createbrowser js this setup will make headless mode run work more like a real browser const browser await wendigo createbrowser slowmo 50 useragent mozilla 5 0 x11 linux x86 64 applewebkit 537 36 khtml like gecko chrome 73 0 3683 75 safari 537 36 acknowledgements puppeteer https github com googlechrome puppeteer and chrome headless as base headless browser zombiejs https github com assaf zombie as inspiration of the assertion library nightmarejs http www nightmarejs org as inspiration for part of the browser interface some code based on the following https github com chromedevtools devtools frontend blob master front end elements dompath js https github com capaj proxy date license wendigo is maintained by angrykoala under gpl 3 0 license wendigo old logo made by jbeguna04 is licensed under creative commons attribution 4 0 international license http creativecommons org licenses by 4 0 wendigo logo made by belenpicazo is licensed under creative commons attribution 4 0 international license http creativecommons org licenses by 4 0 | headless testing node javascript browser puppeteer chromium typescript browser-automation wendigo hacktoberfest | front_end |
udacity-dend-project3-data-warehouse | sparkify analytics database etl objective provide a functional database on cloud with songs users are listening to so the analytics team can work with datasets the data used in the etl pipeline is from the original files in json generated by the music streaming app there are two datasets described bellow songs dataset contains metadata about a song and the artist of that song json num songs 1 artist id arjie2y1187b994ab7 artist latitude null artist longitude null artist location artist name line renaud song id soupiru12a6d4fa1e1 title der kleine dompfaff duration 152 92036 year 0 log dataset activity from the music app running to run the project you are going to need pipenv pipenv is used so you don t need to install the dependencies locally commandline pipenv install pipenv run python file py starting there are two main scripts to run in order to create the database and importing the data the first one create tables py will drop the tables if they exists and re creating them commandline pipenv run python create tables py the second one etl py will load song and log data from the sparkify s3 bucket and insert this date properly first in the staging tables an then in the final star schema tables commandline pipenv run python etl py both scripts use sql queries py in order to achieve the result setting up the redshift cluster use iac py script to creates a redshift cluster according to the dwh cfg file remember to teardown the cluster after usage | cloud |
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SSEA-Linear-Algebra-22 | ssea linear algebra activities database of activities used in the stanford summer engineering academy mathematics course 2022 created by shintaro fushida hardy pranav nuti and megan selbach allen please feel free to modify and incorporate any of these activities into your own courses materials licensed under creative commons attribution sharealike 4 0 international https creativecommons org licenses by sa 4 0 the activities are arranged by topic introduction to vectors perpendicularity planes subspaces dimension spans projections non linear algebra meta skills general skills and other activities homework within each topic each activity has a corresponding readme file this file describes the goals of the activity both mathematical and meta mathematical the necessary materials rough time lines for the activity detailed instructions and additional tips and advice from our experiences using the activity some activities in the database require no supplementary materials some require digital non digital materials that we don t provide and most require digital materials that we do provide activities can be browsed manually in the main git branch directory or if you re more comfortable you can browse them through this page which walks the reader through the overall syllabus and click any links syllabus syllabus and lesson plans are also provided the syllabus outlines expectations for students and instructors and overall administrative details about the course the lesson plans describe in detail each lesson carried out throughout the course in particular how the activities are spaced through the four weeks syllabus https github com sfushidahardy ssea linear algebra activities blob main syllabus syllabus lesson plans https github com sfushidahardy ssea linear algebra activities blob main syllabus lessonplans introduction linear algebra more specifically vectors and their properties is introduced through the iola https iola math vt edu index php carpet ride problem together with our maps and distances activity in this activity students use the language of vectors to estimate distances between classmates hometowns with physical maps magic carpet https github com sfushidahardy ssea linear algebra activities blob main introduction magiccarpet maps and distances https github com sfushidahardy ssea linear algebra activities blob main introduction mapsanddistances perpendicularity the first big topic of the class is perpendicularity which is introduced through perpendicularity and approximations this is a quick activity to motivate why perpendicularity is a useful property next the dot product is discovered in patterns in perpendicular vectors finally as a class we formally prove that the dot product detects perpendicularity in cosine law and dot product students should work on cosine law directly before dot product as the latter builds on the former perpendicularity and approximations https github com sfushidahardy ssea linear algebra activities blob main perpendicularity perpendicularityandapproximations patterns in perpendicular vectors https github com sfushidahardy ssea linear algebra activities blob main perpendicularity patternsinperpendicularvectors cosine law https github com sfushidahardy ssea linear algebra activities blob main perpendicularity cosinelaw dot product https github com sfushidahardy ssea linear algebra activities blob main perpendicularity dotproduct planes the second big topic is planes living in 3 dimensions introduced through various sneaky mathematicians this is a spiritual successor to the carpet ride problem next students practice converting between different types of descriptions of planes in converting between descriptions of planes and planes telephone specifically students convert between parametric equational three point and point normal forms of planes various sneaky mathematicians https github com sfushidahardy ssea linear algebra activities blob main planes varioussneakymathematicians converting between descriptions of planes https github com sfushidahardy ssea linear algebra activities blob main planes convertingbetweendescriptionsofplanes planes telephone https github com sfushidahardy ssea linear algebra activities blob main planes planestelephone subspaces dimension spans the third big topic is bases subspaces dimension and spans the idea of bases and spanning sets is introduced in spanning sets next students practice changing between bases in basis ladder which is really an activity about checking whether or not a set has a redundancy finally students work on practice with bases which consists of various different problems relating bases to earlier topics spanning sets https github com sfushidahardy ssea linear algebra activities blob main subspacedimensionspan spanningsets basis ladder https github com sfushidahardy ssea linear algebra activities blob main subspacedimensionspan basisladder practice with bases https github com sfushidahardy ssea linear algebra activities blob main subspacedimensionspan practicewithbases projections the final big topic is projections mostly onto lines and planes in 3 dimensions projections are first introduced through projections using a toy train next projections are formalised in the nearest vector interpretation and the projection formula finally students practice projecting vectors in projection onto planes telephone projections using a toy train https github com sfushidahardy ssea linear algebra activities blob main projections projectionsusingatoytrain the nearest vector interpretation and the projection formula https github com sfushidahardy ssea linear algebra activities blob main projections thenearestvectorinterpretationandtheprojectionformula projection onto planes telephone https github com sfushidahardy ssea linear algebra activities blob main projections projectionontoplanestelephone non linear algebra throughout the course non linear algebra activities were scattered between linear algebra activities these were of several types one was a discrete version of a linear algebra concept three were algebra activities and two were playful activities designed to encourage collaboration and communication and build problem solving skills early in the course students worked on no more pennies in which they determine which values of currency can be attained by using only coins of specified values this can be considered a discrete version of linear combinations and spans no more pennies https github com sfushidahardy ssea linear algebra activities blob main non linearalgebra nomorepennies students also work on algebra activies throughout the course with an emphasis on fractions exponents and general algebra rules respectively each activity is surprisingly deep with egyptian fractions and root 2 and beyond emphasising problem solving egyptian fractions https github com sfushidahardy ssea linear algebra activities blob main non linearalgebra egyptianfractions root 2 and beyond https github com sfushidahardy ssea linear algebra activities blob main non linearalgebra root2andbeyond true or false https github com sfushidahardy ssea linear algebra activities blob main non linearalgebra trueorfalse there were also two activities designed to be playful and encourage collaboration and communication string shapes https github com sfushidahardy ssea linear algebra activities blob main non linearalgebra stringshapes paper folding https github com sfushidahardy ssea linear algebra activities blob main non linearalgebra paperfolding meta skills general skills and other activities each of good groupwork math resources preparing for lecture and problem solving are discussions around certain aspects of learning maths generally at university similarly honor code is a discussion based activity but specific to stanford university in which students must adhere to the honor code https communitystandards stanford edu policies guidance honor code good groupwork https github com sfushidahardy ssea linear algebra activities blob main metaskills goodgroupwork math resources https github com sfushidahardy ssea linear algebra activities blob main metaskills mathresources perparing for lecture https github com sfushidahardy ssea linear algebra activities blob main metaskills preparingforlecture problem solving https github com sfushidahardy ssea linear algebra activities blob main metaskills problemsolving honor code https github com sfushidahardy ssea linear algebra activities blob main metaskills honorcode next attending section cheat sheet and reading practice are all opportunities for students to directly attempt tasks they ll perform in university math classes the section worksheet for attending section consists of problems from the math 51 textbook since the textbook is licensed under copyright the section worksheet cannot be redistributed here attending section https github com sfushidahardy ssea linear algebra activities blob main metaskills attendingsection cheat sheet https github com sfushidahardy ssea linear algebra activities blob main metaskills cheatsheet reading practice https github com sfushidahardy ssea linear algebra activities blob main metaskills readingpractice in math words matching game students work together to learn the meanings of words like lemma conjecture and theorem in secret santa students write linear algebra problems for one another and in skills recap we discuss and celebrate all of the learning that has been done throughout the course math words matching game https github com sfushidahardy ssea linear algebra activities blob main metaskills mathwordsmatchinggame secret santa https github com sfushidahardy ssea linear algebra activities blob main metaskills secretsanta skills recap https github com sfushidahardy ssea linear algebra activities blob main metaskills skillsrecap homework homework largely consisted of three problem sets with exercises from the stanford university math 51 textbook the textbook is licensed under copyright these problem sets cannot be redistributed here homework also incorporated short writing assignments typically reflections on certain meta mathematical topics writing prompts https github com sfushidahardy ssea linear algebra activities blob main homework writing | server |
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blockchains-auditing | blockchain auditing br this repository is an on going catalog from my own research and development if you are passionate about self sovereignty and love solving complex problems don t be intimidated do your homework and join us we need you if you are interested in mev related resources check out our mev toolkit https github com go outside labs mev toolkit as everything in life the most valuable commodity is information br p align center img src https github com go outside labs blockchain auditing assets 138340846 d8aa9abb 1aba 43d1 8ed8 6e191e3229ff width 55 align center style padding 1px border 1px solid black p br i learn the basics readings basic knowledge oracles basic knowledge oracles bridges basic knowledge bridges honeypots basic knowledge honeypots cryptography basic knowledge cryptography l2s rollups basic knowledge l2 and rollups the evm opcodes basic knowledge evm and opcodes decentralized storage basic knowledge decentralized storage br ii learn the tools hacking tools hacking tools decompilers hacking tools decompilers environments hacking tools environments visual explorers hacking tools visual explorers identity hacking hacking tools identity tools static analysis hacking tools static analysis dynamic analysis hacking tools dynamic analysis hacking by chains hacking tools hacking by chains br iii learn from the past bug hunting advanced expert bug hunting attack reviews advanced expert attack reviews vulnerabilities advanced expert vulnerabilities foundry exploits advanced expert foundry exploits practice your skills advanced expert practice your skills contracts of interest advanced expert contracts of interest br | blockchain defi ethereum evm solidity security aurora blockchain-security near rust smart-contracts | blockchain |
cs193p-Winter-2017 | stanford engineering cs193p developing ios 10 apps with swift art itunesu png raw true this repo contains my lecture notes and projects from paul hegarty s classic cs 193p iphone application development course http web stanford edu class cs193p cgi bin drupal offered by the school of engineering at stanford img src https raw githubusercontent com duliodenis cs193p winter 2017 master art cs193p jpg width 200px height 200px this course is described as being updated for ios 10 and swift 3 tools and apis required to build applications for the iphone and ipad platforms using the ios sdk the course covers user interface design for mobile devices and unique user interactions using multi touch technologies object oriented design using model view controller paradigm memory management the swift programming language other topics include animation mobile device power management multi threading networking and performance considerations lectures lecture slides source video date 1 course overview intro to ios xcode and swift slides lecture 1 slides pdf art play png raw true https youtu be ilq tq772vi list plpa aybrweuz32nsgnzdl0 qisw f12ai january 9 2017 2 applying mvc calculator demo continued slides lecture 2 slides pdf art play png raw true https youtu be aug myu02q list plpa aybrweuz32nsgnzdl0 qisw f12ai january 11 2017 3 more swift foundation framework slides lecture 3 slides pdf art play png raw true https youtu be 4voseyy6krc list plpa aybrweuz32nsgnzdl0 qisw f12ai january 18 2017 4 custom views slides lecture 4 slides pdf art play png raw true https youtu be lx4ohhsc3ho list plpa aybrweuz32nsgnzdl0 qisw f12ai january 23 2017 5 gestures and multiple mvcs slides lecture 5 slides pdf art xcode png raw true democode lecture 5 demo code faceit pdf art play png raw true https youtu be fxinju nkwu list plpa aybrweuz32nsgnzdl0 qisw f12ai january 25 2017 6 multiple mvcs view controller lifecycle and memory management slides lecture 6 slides pdf art xcode png raw true democode lecture 6 demo code faceit pdf art play png raw true https youtu be hqrxm2zupvy list plpa aybrweuz32nsgnzdl0 qisw f12ai january 30 2017 7 error handling in swift extensions protocols delegation and scroll views slides lecture 7 slides pdf art xcode png raw true democode lecture 7 demo code cassini pdf art play png raw true https youtu be gilsl 6tqmm list plpa aybrweuz32nsgnzdl0 qisw f12ai february 1 2017 8 multithreading text field table view intro slides lecture 8 slides pdf art xcode png raw true democode lecture 8 demo code cassini pdf art play png raw true https youtu be h9kbzg3rk8 list plpa aybrweuz32nsgnzdl0 qisw f12ai february 6 2017 9 table view slides lecture 9 slides pdf art xcode png raw true democode lecture 9 demo code smashtag pdf art play png raw true https youtu be 78lwmmdxr4k list plpa aybrweuz32nsgnzdl0 qisw f12ai february 8 2017 10 core data slides lecture 10 slides pdf art play png raw true https youtu be ssipdu73p7a list plpa aybrweuz32nsgnzdl0 qisw f12ai february 13 2017 11 core data demo art xcode png raw true democode lecture 11 demo code smashtag pdf art play png raw true https youtu be whf63gtaw1w list plpa aybrweuz32nsgnzdl0 qisw f12ai february 15 2017 12 autolayout slides lecture 12 slides pdf art play png raw true https youtu be uppl3lv5l8w list plpa aybrweuz32nsgnzdl0 qisw f12ai february 22 2017 13 timer animation slides lecture 13 slides pdf art xcode png raw true democode lecture 13 demo code faceit pdf art play png raw true https youtu be 6tdnjwdwfys list plpa aybrweuz32nsgnzdl0 qisw f12ai february 27 2017 14 dynamic animation demos art xcode png raw true democode lecture 14 demo code asteroids pdf art play png raw true https youtu be 8ryq1a zdmw list plpa aybrweuz32nsgnzdl0 qisw f12ai march 1 2017 15 segues slides lecture 15 slides pdf art xcode png raw true democode lecture 15 demo code faceit segues pdf art play png raw true https youtu be mjklubbkggc list plpa aybrweuz32nsgnzdl0 qisw f12ai march 6 2017 16 alerts notifications lifecycles persistence slides lecture 16 slides pdf art play png raw true https youtu be hkuedmw7qx0 list plpa aybrweuz32nsgnzdl0 qisw f12ai march 8 2017 17 accessibility art play png raw true https youtu be nozxrbom7bw list plpa aybrweuz32nsgnzdl0 qisw f12ai march 13 2017 reading assignments reading name 1 reading 1 intro to swift reading reading assignment 1 intro to swift pdf 2 reading 2 more swift reading reading assignment 2 more swift pdf 3 reading 3 the rest of swift reading reading assignment 3 the rest of swift pdf problem sets ps name 1 assignment 1 calculator problemsets programming project 1 calculator pdf 2 assignment 2 calculator brain problemsets programming project 2 calculator brain pdf 3 assignment 3 graphing calculator problemsets programming project 3 graphing calc pdf 4 assignment 4 smashtag mentions problemsets programming project 4 smashtag mentions pdf 5 assignment 5 smashtag mention popularity problemsets programming project 5 smashtag mention popularity pdf licensing my cs193p projects are licensed under the mit license license support or contact visit ddapps co http ddapps co to see more | swift stanford engineering iphone school | os |
Web-Development-with-Blazor-Second-Edition | web development with blazor second edition code repository for web development with blazor second edition https www packtpub com product web development with blazor second edition 9781803241494 published by packt an in depth practical guide for net developers to build interactive uis with c about the book blazor is an essential tool if you want to build interactive web apps without javascript but it has a learning curve updated with the latest code in net 7 and c 11 and written by someone who adopted blazor early this book will help you overcome the challenges associated with being a beginner with blazor and teach you the best coding practices you ll start by learning how to leverage the power of blazor and exploring the full capabilities of both blazor server and blazor webassembly then you ll move on to the practical part centered around a sample project a blog engine you ll apply all your newfound knowledge about creating blazor projects the inner workings of razor syntax validating forms and creating your own components this new edition also looks at source generators dives deeper into blazor webassembly with ahead of time and includes a dedicated new chapter demonstrating how to move components of an existing javascript angular react or mvc based website to blazor or combine the two you ll also see how to use blazor hybrid together with net maui to create cross platform desktop and mobile applications when you reach the end of this book you ll have the confidence you need to create and deploy production ready blazor applications and you ll have a big picture view of the blazor landscape what you will learn understand the different technologies that can be used with blazor such as blazor server blazor webassembly and blazor hybrid find out how to build simple and advanced blazor components explore the differences between blazor server and blazor webassembly projects discover how minimal apis work and build your own api explore existing javascript libraries in blazor and javascript interoperability learn techniques to debug your blazor server and blazor webassembly applications test blazor components using bunit table of contents chapters 1 hello blazor 2 creating your first blazor app 3 managing state part 1 4 understanding basic blazor components 5 creating advanced blazor components 6 building forms with validation 7 creating an api 8 authentication and authorization 9 sharing code and resources 10 javascript interop 11 managing state part 2 12 debugging the code 13 testing 14 deploy to production 15 moving from or combining an existing site 16 going deeper into webassembly 17 examining source generators 18 visiting net maui 19 where to go from here if you feel this book is for you get your copy https www amazon com web development blazor depth interactive dp 1803241497 today img alt coding height 15 width 35 src https media tenor com ex hdd k5p8aaaai habbo habbohotel gif following is what you need for this book this book is for net web developers and software developers who want to use their existing c skills to build interactive spa applications running either inside the web browser using blazor webassembly or on the server using blazor server you ll need intermediate level web development skills basic knowledge of c and prior exposure to net web development before you get started the book will guide you through the rest with the following software and hardware list you can run all code files present in the book software and hardware list software covered in this book os requirements visual studio 2022 net7 windows 10 or later macos linux know more on the discord server img alt coding height 25 width 32 src https cliply co wp content uploads 2021 08 372108630 discord logo 400 gif you can get more engaged on the discord server for more latest updates and discussions in the community at https packt link webdevblazor2e https packt link webdevblazor2e download a free pdf img alt coding height 25 width 40 src https emergency com au wp content uploads 2021 03 free gif if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost simply click on the link to claim your free pdf https packt link free ebook 9781803241494 https packt link free ebook 9781803241494 img alt coding height 15 width 35 src https media tenor com ex hdd k5p8aaaai habbo habbohotel gif we also provide a pdf file that has color images of the screenshots diagrams used in this book at https packt link g0hsv img alt coding height 15 width 35 src https media tenor com ex hdd k5p8aaaai habbo habbohotel gif get to know the author jimmy engstr m has been developing ever since he was 7 years old and got his first computer he loves to be on the cutting edge of technology trying new things when he got wind of blazor he immediately realized the potential and adopted it already when it was in beta he has been running blazor in production since it was launched by microsoft his passion for the net industry and community has taken him around the world speaking about development microsoft has recognized this passion by awarding him the microsoft most valuable professional award 8 years in a row | api blazor csharp dotnet webassembly | front_end |
elasticsearch-learning-to-rank | build status https travis ci com o19s elasticsearch learning to rank svg branch master https travis ci com o19s elasticsearch learning to rank the elasticsearch learning to rank plugin uses machine learning to improve search relevance ranking it s powering search at places like wikimedia foundation and snagajob what this plugin does this plugin allows you to store features elasticsearch query templates in elasticsearch logs features scores relevance scores to create a training set for offline model development stores linear xgboost or ranklib ranking models in elasticsearch that use features you ve stored ranks search results using a stored model where s the docs we recommend taking time to read the docs http elasticsearch learning to rank readthedocs io there s quite a bit of detailed information about learning to rank basics and how this plugin can ease learning to rank development you can also participate in regular trainings http opensourceconnections com events training on elasticsearch learning to rank which support the free work done on this plugin i want to jump in the demo lives in another repo now hello ltr https github com o19s hello ltr and it has both es and solr example follow the directions for elasticsearch in the readme to set up the environment and start with the notebooks elasticsearch tmdb hello ltr ipynb https github com o19s hello ltr blob master notebooks elasticsearch tmdb hello ltr 20 es ipynb have fun installing see the full list of prebuilt versions https github com o19s elasticsearch learning to rank releases and select the version that matches your elasticsearch version if you don t see a version available see the link below for building or file a request via issues https github com o19s elasticsearch learning to rank issues to install you d run a command like this but replacing with the appropriate prebuilt version zip bin elasticsearch plugin install https github com o19s elasticsearch learning to rank releases download v1 5 4 es7 11 2 ltr plugin v1 5 4 es7 11 2 zip it s expected you ll confirm some security exceptions you can pass b to elasticsearch plugin to automatically install if you already are running elasticsearch don t forget to restart known issues as any other piece of software this plugin is not exempt from issues please read the known issues known issues md to learn about the current issues that we are aware of this file might include workarounds to mitigate them when possible build and deploy locally notes if you want to dig into the code or build for a version there s no build for please feel free to run the build and installation process yourself gradlew clean check bin elasticsearch plugin install file path to elasticsearch learning to rank build distributions ltr ltr ver es es ver zip how to contribute for more information on helping us out we need your help developing with the plugin creating docs etc please read contributing md contributing md elastic release support we do our best to officially support 1 releases of elasticsearch if you have a need for dot oh compatibility or a version we don t support please consider submitting a pr who built this initially developed http opensourceconnections com blog 2017 02 14 elasticsearch learning to rank at opensource connections http opensourceconnections com significant contributions by wikimedia foundation https wikimediafoundation org wiki home snagajob engineering https engineering snagajob com bonsai https bonsai io and yelp engineering https engineeringblog yelp com thanks to jettro coenradie https amsterdam luminis eu author jettro for porting to es 6 1 other acknowledgments stuff to read bloomberg s learning to rank work for solr https issues apache org jira browse solr 8542 our berlin buzzwords talk we built an elasticsearch learning to rank plugin then came the hard part https berlinbuzzwords de 17 session we built elasticsearch learning rank plugin then came hard part blog article on how is search different from other machine learning problems http opensourceconnections com blog 2017 08 03 search as machine learning prob also check out our other relevance search thingies book relevant search http manning com books relevant search projects elyzer http github com o19s elyzer splainer http splainer io and quepid http quepid com | elasticsearch relevant-search machine-learning search-relevance elasticsearch-plugin elasticsearch-plugins | ai |
webdev-lectures-ru | fullstack 1 https github com profitware webdev coaching blob master plan lectures ru md 2 https github com profitware webdev coaching blob master plan practice ru md 3 html css etc https github com profitware webdev coaching tree master reference 4 1 https github com profitwarewebdev webdev practice 2 https github com profitwarewebdev webdev hosting issues 9 10 python flask celery bootstrap mdl angularjs reactjs jquery ajax 6 8 python flask bootstrap jquery angularjs reactjs 5 html5 css3 4 html4 css2 1 1 python pep 8 0 7 deadline 10 10 https hh ru vacancy 19629686 https hh ru vacancy 19546445 wombat sergey https raw githubusercontent com profitwarewebdev webdev lectures ru master misc mascot png wombat sergey special thanks for mascot https vk com wall 111548911 2129 | front_end |
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youtube-challenge-nestjs-graphql | h1 align center code challenge nestjs typeorm graphql h1 p align center simple graphql api with nestjs in back end react and apollo client for front end p p align center a href https github com rocketseat youtube challenge nestjs graphql graphs contributors img src https img shields io github contributors rocketseat youtube challenge nestjs graphql color 237159c1 logocolor 237159c1 style flat alt contributors a a href https opensource org licenses mit img src https img shields io github license rocketseat youtube challenge nestjs graphql color 237159c1 logo mit alt license a p hr participants img src https avatars3 githubusercontent com u 10366880 s 460 v 4 width 75px https github com guilhermerodz guilherme rodz https github com guilhermerodz functional requirements x user register with e mail only x user login with e mail only x user need to be able to post messages on the board back end user need to be able to post messages on the board front end x optional user need to be able to delete messages back end optional user need to be able to delete messages front end x new messages can be listed at real time back end new messages can be listed at real time front end add swagger support dataloader integration business rules x message can only be deleted by its author non functional requirements x nest js x graphql x typeorm x react apollo client or another library what can be better user id could be stored at context api in front end dependencies node https nodejs org en 10 getting started 1 clone this repository br 2 run npm or yarn install at each project in order to install dependencies br 3 run yarn start dev for back end and yarn start for front end folder br 4 access localhost 3000 in your browser graphql playground localhost 3333 graphql br contributing please read contributing md contributing md for details on our code of conduct and the process for submitting pull requests | front_end |
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Einstein | einstein machine learning deep learning predictive analytics natural language processing and smart data discovery official einstein analytics for partners https partners salesforce com ui core chatter groups groupprofilepage g 0f9300000009ogm einstein analytics discovery cert fp https partners salesforce com ui core chatter groups groupprofilepage g 0f93a00000024uo einstein platform for partners https trailhead salesforce com users nmoscaritolo trailmixes einstein platform for partners fy20 amer einstein ai partner summit seat request form https docs google com forms d e 1faipqlsdkzr3veq4d ikrsplaqoq4gtpq vpfxhdxsj6xctjpnvaaw viewform https metamind readme io v1 docs https github com metamind apex utils https github com salesforceidentity jwt learn einstein lead scoring on trailhead https developer salesforce com promotions orgs einsteinleadscoring einstein platform how to webinar get started with einstein discovery replay http salesforce vidyard com watch ozcijf3m5yajzk9qj9a5pg einstein platform how to webinar get started with einstein discovery slides https success salesforce com 0693a000007saduqas workbook deep learning and natural language processing https colab research google com drive 1dttxahcnxf1idendtoncbpyy42rnequq scrollto gvh mlh0fttj salesforce event log file browser https salesforce elf herokuapp com apex trigger event type https developer salesforce com docs atlas en us api meta api sforce api objects eventlogfile apextrigger htm einstein ai learning path https partnernavigator salesforce com s einsteinai step 1 einstein analytics learning path https partnernavigator salesforce com s einsteinanalytics step 1 certified einstein analytics and discovery consultant salesforce certified einstein analytics and discovery consultant https trailhead salesforce com help article salesforce certified einstein analytics and discovery consultant exam guide learn einstein analytics plus trailmix https trailhead salesforce com users ea trails trailmixes learn einstein analytics plus db total time is the time in nanoseconds for a database round trip cpu time is the cpu time in milliseconds used to complete the request this measure indicates the amount of activity taking place in the app server layer compare the db total time to cpu time to determine whether performance issues are occurring in the database layer or in your own code services data v46 0 query q select id eventtype logdate logfilelength logfile from eventlogfile where eventtype reportexport https instance salesforce com services data v34 0 query q select id eventtype logdate from eventlogfile where logdate day einstein platform a model is a machine learning construct used to solve a classification problem the model learns from data instead of from explicit rules sales cloud einstein einstein activity capture sales reps just connect their email and calendar to salesforce then their activities are automatically added to related salesforce records plus emails sent from salesforce go through their regular email account einstein lead scoring gives each lead a score based on how well it matches your company s particular lead conversion patterns einstein lead scoring shows you exactly which details about each lead have the greatest effect on its score predictive lead scoring constantly adjusts its analysis in order to discover any new patterns that emerge opportunity insights uses machine learning and sentiment analysis to help sales reps close more deals the same data collection that s used to log customer data and identify the best leads can also move the sales needle insights are tailored to the patterns and data specific to your organization three types of insights deal predictions your reps see predictions based on recent activity and existing opportunity data for example whether a deal is more or less likely to close or if a deal seems unlikely to close in time follow up reminders reps get reminders to follow up when a contact hasn t responded in a while they also get reminders if there hasn t been any communication related to an important opportunity for a significant period of time key moments reps are notified at key moments related to a deal such as when a contact mentions a competitor or is leaving their company einstein account insights helps your sales team maintain their relationships with customers by keeping the team informed about key business developments that affect customers einstein language language contains two nlp services einstein intent and einstein sentiment the einstein language apis support data in these file formats csv comma separated values tsv tab separated values json einstein intent the einstein intent api categorizes unstructured text into user defined labels to better understand what users are trying to accomplish use this api to analyze text from emails chats or web forms to determine which products prospects are interested in and send customer inquiries to the appropriate sales person route service cases to the correct agents or departments or provide self service options understand customer posts to provide personalized self service in your communities einstein sentiment the einstein sentiment api classifies text into positive negative and neutral classes to understand what the words people use can tell us about how they re feeling use this api to analyze emails social media and text from chat to identify the sentiment or emotion in a prospect s emails to trend a lead or opportunity up or down provide proactive service by helping dissatisfied customers first or extending promotional offers to satisfied customers monitor how people perceive your brand across social media channels identify brand evangelists and note customer satisfaction resources einstein api signup https api einstein ai signup einstein platform developer guide https metamind readme io docs intro to einstein language | ai |
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ESD_MINI_PROJECTS | esd mini projects embedded system design | os |
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projsemb | projsemb rtos for atmega falta fazer remover passos vari veis e argumentos desnecess rios das fun es do kernel testar sem foros medir tempos implementar teste por uart | os |
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Web-Development-with-Julia-and-Genie | web development with julia and genie a href https www packtpub com product web development with julia and genie 9781801811132 img src https static packt cdn com products 9781801811132 cover smaller alt web development with julia and genie height 256px align right a this is the code repository for web development with julia and genie https www packtpub com product web development with julia and genie 9781801811132 published by packt a hands on guide to high performance server side web development with the julia programming language what is this book about julia s high performance and scalability characteristics and its extensive number of packages for visualizing data make it an excellent fit for developing web apps web services and web dashboards the two parts of this book provide complete coverage to build your skills in web development this book covers the following exciting features understand how to make a web server with http jl and work with json data over the web discover how to build a static website with the franklin framework explore julia web development frameworks and work with them uncover the julia infrastructure for development testing package management and deployment develop an mvc web app with the genie framework if you feel this book is for you get your copy https www amazon com dp 180181113x today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following if todo2 priority todo1 priority println better do todo2 first else println better do todo1 first end following is what you need for this book this book is for beginner to intermediate level julia programmers who want to enhance their skills in designing and developing large scale web applications the book helps you adopt genie without any prior experience with the framework julia programming experience and a beginner level understanding of web development concepts are required with the following software and hardware list you can run all code files present in the book chapter 1 8 software and hardware list chapter software required os required 1 8 julia 1 8 windows mac os x and linux any 1 8 visual studio code windows mac os x and linux any 1 8 genie 5 windows mac os x and linux any we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https packt link pymya related products other books you may enjoy interactive visualization and plotting with julia packt https www packtpub com product interactive visualization and plotting with julia 9781801810517 amazon https www amazon com dp 1801810516 rust web programming packt https www packtpub com product rust web programming 9781800560819 amazon https www amazon com dp 1800560818 get to know the authors ivo balbaert is a software engineer and lecturer in web programming and databases he worked for 30 years in the software industry as a developer and consultant in several companies from 2000 onwards he switched to teaching part time authoring books and developing software adrian salceanu is the founder ceo at geniecloud io he is a computer scientist with over 20 years of experience as a full stack web developer working on data intensive applications adrian is the creator and lead maintainer of genie framework the most popular julia web framework counting over 100k downloads and all time top 10 most starred julia package on github he is a julia early adopter and open source contributor to the julialang ecosystem since 2015 download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781801811132 https packt link free ebook 9781801811132 a p | front_end |
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biomedical | bigbio biomedical dataset library bigbio bigscience biomedical is an open library of biomedical dataloaders built using huggingface s datasets library https huggingface co docs datasets for data centric machine learning our goals include lightweight programmatic access to biomedical datasets at scale promoting reproducibility in data processing better documentation for dataset provenance licensing and other key attributes easier generation of meta datasets for natural language prompting multi task learning currently bigbio provides support for 126 biomedical datasets 10 languages 12 task categories harmonized dataset schemas by task type metadata on licensing coarse fine grained task types domain and more how to use bigbio the preferred way to use these datasets is to access them from the official bigbio hub https huggingface co bigbio minimally ensure you have the datasets library installed preferably install the requirements as follows pip install r requirements txt br you can access bigbio datasets as follows python from datasets import load dataset data load dataset bigbio biosses in most cases scripts load the original schema of the dataset by default you can also access the bigbio split that streamlines access to key information in datasets given a particular task br for example the biosses dataset follows a pairs based schema where text based inputs sentences paragraphs are assigned a translated pair python from datasets import load dataset data load dataset bigbio biosses name biosses bigbio pairs generally you can load your datasets as follows python load original schema data load dataset bigbio your dataset load bigbio schema data load dataset bigbio your dataset here name your dataset bigbio schema name check the datacards on the hub to see what splits are available to you you can find more information about schemas task schemas md in documentation documentation below benchmark support bigbio includes support for almost all datasets included in other popular english biomedical benchmarks task type dataset bigbio ours https arxiv org abs 2206 15076 blue https arxiv org abs 1906 05474 blurb https microsoft github io blurb box https arxiv org abs 2204 07600 dua needed ner bc2gm ner bc5 chem ner bc5 disease ner ebm pico ner jnlpba ner ncbi disease re chemprot re ddi re gad qa pubmedqa qa bioasq dc hoc sts biosses sts medsts ner n2c2 2010 ner share clef 2013 nli mednli ner n2c2 deid 2006 dc n2c2 rfhd 2014 ner anatem ner bc4chemd ner bionlp09 ner bionlp11epi ner bionlp11id ner bionlp13cg ner bionlp13ge ner bionlp13pc ner craft ner ex ptm ner linnaeus pos genia sa medical drugs sr covid private sr cooking private sr hrt private sr accelerometer private sr acromegaly private denotes dataset implementation in progress documentation task schema overview task schemas md is an indepth explanation of bigbio schemas implemented streamlit visualization demo https github com bigscience workshop biomedical tree master streamlit demo bigbio data cards https github com bigscience workshop biomedical tree master figures data card report on statistics around each dataset in the library tutorials tba links may not be applicable yet tutorials materializing meta datasets https github com bigscience workshop biomedical blob master notebooks materializing meta datasets materializing meta datasets ipynb prompt engineering and evaluation https github com bigscience workshop biomedical tree master notebooks promptengineering prompt engineering with bloom notebooks bloomprompting bloompipeline md contributing bigbio is an open source project your involvement is warmly welcome if you re excited to join us we recommend the following steps looking for ideas see our volunteer project board https github com orgs bigscience workshop projects 6 to see what we may need help with have your own idea contact an admin in the form of an issue https github com bigscience workshop biomedical issues new assignees labels template add dataset md title implement your idea following guidelines set by the official contributing guide contributing md wait for admin approval approval is iterative but if accepted will belong to the main repository currently only admins will be merging all accepted changes to the hub feel free to join our discord https discord com invite cwf3nt3ajp citing if you use bigbio in your work please cite article fries2022bigbio title bigbio a framework for data centric biomedical natural language processing author fries jason alan and weber leon and seelam natasha and altay gabriel and datta debajyoti and garda samuele and kang myungsun and su ruisi and kusa wojciech and cahyawijaya samuel and others journal arxiv preprint arxiv 2206 15076 year 2022 acknowledgements bigbio is a open source community effort made possible through the efforts of many volunteers as part of bigscience and the biomedical hackathon hackathon md | ai |
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