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stacks-blockchain-docker | stacks blockchain with docker license gpl v3 https img shields io badge license gplv3 blue svg https www gnu org licenses gpl 3 0 pull requests welcome https img shields io badge prs welcome brightgreen svg style flat http makeapullrequest com run your own stacks blockchain node easily with just few commands docker compose version 2 2 2 or greater is required docs docker md quickstart bash git clone https github com stacks network stacks blockchain docker cd stacks blockchain docker cp sample env env sync from genesis bash manage sh n mainnet a start seed chainstate from hiro archiver using data from the hiro archiver https docs hiro so references hiro archive what is the hiro archive service this script will download the latest files extract them and restore the postgres data note it can take a long time to process the data and you ll need at a minimum roughly 150gb of free space bash sudo scripts seed chainstate sh manage sh n mainnet a start requirements docs requirements md docker setup docs docker md configuration docs config md upgrading docs upgrade md usage docs usage md common issues docs issues md accessing the services for networks other than mocknet downloading the initial headers can take several minutes until the headers are downloaded the v2 info endpoints won t return any data use the command manage sh n network a logs to check the sync progress stacks blockchain bash curl sl localhost 20443 v2 info jq stacks blockchain api bash curl sl localhost 3999 v2 info jq proxy optional argument bash curl sl localhost v2 info jq curl sl localhost jq | blockstack blockchain hiro dns decentralized bitcoin docker api stacks | blockchain |
Data_Engineering_Practice_DataModeling | data engineering data modeling practice simple data modeling practice with postgresql and python in jupyter notebook for this project i practiced creating an er diagram then creating and populating a database in postgresql using python first i created an entity relationship diagram for fictional employees data that i created myself this consisted of creating tables and finding primary and foreign keys for each table as well as the joining relationships this is shown in the er diagram i then used python to create a postgresql database and manually created the tables then populated with data next i did this again with another dataset however this dataset was already in an excel csv file i used python to create a database for this and load the data from this dataset into the database in postgresql the data for each dataset is now ready for cleaning and analysis er diagram a fictional er diagram i ve created of an employees database what is a data model a data model organizes elements of data and standardizes how they relate to one another and properties of real world entities why is data modeling important organizes data across multiple tables to more easily maintain tables organizes data and determines later data use begin prior to build out apps business logic and analytical model it is an iterative process relational data models organizes data into tables with a unique key identifying each row structure of how data looks relational database digital database based on relational model of data a software system used to maintain relational database is a relational database management system rdbms mysql postgresql oracle mssql etc basics of relational database database schema is a collection of tables tables relations are a group of rows sharing the same labeled elements advantages of using a relational database ease of use sql ability to join tables ability to do aggregations and analytics smaller data volumes flexibility of queries acid transactions data integrity when not to use relational database large amounts of data need to be able to store different data types formats need a flexible schema need high availablity need horizontal scalibility | postgresql python database jupyter-notebook data-engineering data-modeling er-diagram | server |
qmRTOS | rtos rtos project source cm3 2017 6 25 rtos 1 2 3 4 5 6 7 8 9 10 hooks project inspect qconfig h define qmrtos enable inspect 1 qconfig h define qmrtos kernel debug out 1 define qmrtos test keil swsimu 1 qconfig h define qmrtos enable sem 1 define qmrtos enable mutex 1 define qmrtos enable flaggroup 1 define qmrtos enable mbox 1 define qmrtos enable memblock 1 define qmrtos enable timer 1 define qmrtos enable cpuusage stat 1 define qmrtos enable hooks 1 | rtos | os |
system-design-master-plan | system design master plan img system design header png system design particularly in the realm of large scale design is a complex and demanding field that often presents challenges for engineers who face difficulties in acquiring the requisite knowledge honing their design skills and tackling real world system design problems as a solution to this i have developed a comprehensive roadmap in the form of a chart which serves as a guide for engineers seeking to accelerate their learning process in large scale design ultimately leading them to enhance their expertise and become more proficient engineers system design roadmap img roadmap png img src img roadmap svg | roadmap system-design software-architecture study-plan system-design-interview software-engineering software-development project-planning system-design-template system-design-roadmap | os |
estimator | web development project estimator web development project estimator the web development project estimator is a simple tool that allows web designers and site developers to quickly and thoroughly estimate the time and materials required for a proposed web project to use simply enter the title of the project and your default hourly rate then adjust your anticipated hours accordingly to generate your total project estimate when finished you can view your finalized estimate in a print ready format in case you d like to save a pdf or print a copy for your records | front_end |
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feflow | english readme cn md h1 align center feflow h1 p align center a tool aims to improve front end engineer workflow and standard powered by typescript p br npm npm npm url build status build status build status url install size size size url downloads downloads downloads url lerna lerna lerna url github contributors contributors contributors url issue resolution issue resolution issue resolution url pr s welcome pr welcome pr welcome url introduction feflow is an engineering solution of tencent s open source front end field which is committed to improving development efficiency and specification getting started let s start by installing feflow with npm npm install feflow cli g there are three kinds of commands in feflow native commands fef config fef help fef info fef install fef uninstall fef list you can write a feflow devkit or plugin to extends commands more detail document can be found github wiki https github com tencent feflow wiki website https feflowjs com change log this project adheres to semantic versioning http semver org every release along with the migration instructions is documented on the github releases https github com tencent feflow releases page license mit license txt build status https travis ci org tencent feflow svg build status url https travis ci org tencent feflow contributors https img shields io github contributors tencent feflow svg contributors url https github com tencent feflow graphs contributors downloads https img shields io npm dw feflow cli svg downloads url https www npmjs com package feflow cli issue resolution https isitmaintained com badge resolution tencent feflow svg issue resolution url https github com tencent feflow issues lerna https img shields io badge maintained 20with lerna cc00ff svg lerna url http www lernajs io npm https img shields io npm v feflow cli svg npm url https www npmjs com package feflow cli pr welcome https img shields io badge prs 20 welcome brightgreen svg pr welcome url https github com tencent feflow blob next github contributing md size https packagephobia now sh badge p feflow cli size url https packagephobia now sh result p feflow cli | command-line-tool workflow front-end yeoman lint feflow builder webpack code-quality plugin cli | front_end |
fuel-specs | fuel specifications markdownlint disable next line md036 fuel a secure decentralized generalized massively scalable transaction ledger this repository specifies the fuel protocol including the fuel virtual machine short fuelvm a blazingly fast verifiable blockchain virtual machine mdbook the fuel specifications book is built with mdbook install mdbook and then open a new terminal session in order to run the subsequent commands sh cargo install mdbook to build book sh mdbook build to serve locally sh mdbook serve contributing markdown files must conform to github flavored markdown https github github com gfm markdown must be formatted with markdownlint https github com davidanson markdownlint markdown table prettifier https github com darkriszty markdowntableprettify vscodeext | fuel blockchain fuelvm | blockchain |
data-engineering-gcp | google professional data engineer a professional data engineer enables data driven decision making by collecting transforming and visualizing data the data engineer designs builds maintains and troubleshoots data processing systems with a particular emphasis on the security reliability fault tolerance scalability fidelity and efficiency of such systems the data engineer also analyzes data to gain insight into business outcomes builds statistical models to support decision making and creates machine learning models to automate and simplify key business processes the google cloud certified professional data engineer exam assesses your ability to build and maintain data structures and databases design data processing systems analyze data and enable machine learning model business processes for analysis and optimization design for reliability visualize data and advocate policy design for security and compliance this repository contains a collection of resources that will help you prepare review of tips tip 1 create your own custom preparation plan using the resources in this course tip 2 use the exam guide outline to help identify what to study read through the exam guide outline https cloud google com certification guides data engineer https cloud google com certification guides data engineer the outline communicates how an authority thinks about and organizes the skills required of a professional data engineer training tends to be organized in a ramp basic concepts first building into more complex and difficult concepts later the exam guide is not training so it is not organized for learning or by importance or by process it is organized according to how a group of experts thinks about the job there is no additional information about what any particular line means no explanation tip use the exam guide outline to help identify what to study the recommendation is for you to read through each line and think about what it actually means what do you think it is saying about the job then ask yourself if you understand that aspect of the job and if you feel you have the skills required to be successful in that part of the job if yes great it is an indicator that you are prepared if no take note you may want to study additionally in that part or develop specific skills for that item in the exam guide tip 3 product and technology knowledge you need to know the basic information about each product that might be covered on the exam you need to know what it does why it exists what is special about its design for what purpose or purposes was it optimized when do you use it and what are the limits or bounds when it is time to consider an alternative what are the key features of this product or technology is there an open source alternative if so what are the key benefits of the cloud based service over the open source software which products and technologies training and certification meet at the jta the job task analysis the skills required of the job the scope of the exam matches the learning track and specialization in training so a great place to derive a list of the technologies and products that might be on the exam is to look at all the products and technologies that are covered in the related training the training might not cover everything but it is a good place to start study methods training is great digging into the online documentation can be very instructive and covers more detail than can be covered in a class so documentation tends to have more equal coverage of features whereas training has to prioritize its time getting hands on experience can help you understand a product or technology much better than reading and is the kind of experience a professional in the job would have so labs can be a great way to prepare tip 4 this course has touchstone concepts for self evaluation not technical training seek training if needed https www coursera org learn preparing cloud professional data engineer exam supplement zikft exam tips 4 tip 5 problem solving is the key skill tip 6 practice evaluating your confidence in your answers tip 7 practice case evaluation and creating proposed solutions tip 8 use what you know and what you don t know to identify correct and incorrect answers tip 9 review or rehearse labs to refresh your experience tip 10 prepare good luck certification exam guide section 1 designing data processing systems 1 1 designing flexible data representations considerations include data representations pipelines and processing infrastructure https www coursera org learn preparing cloud professional data engineer exam lecture 8qmyy data representations pipelines and processing infrastructure exam outline tips future advances in data technology tradeoffs between common formats and efficient formats changes to business requirements notice business changes that imply possible solution changes awareness of current state and how to migrate the design to a future state what state format is the data in and for what purpose and where does it need to go for what new purpose and in what new state format data modeling how do data items relate to one another trade offs json xml avro csv databases sql nosql distributed systems main points can the application deal with out of order data eventual consistency can the application handle the overhead and delay of ordering or synchronizing schema design if the data is structured organized for a purpose 1 2 designing data pipelines considerations include exam outline tips future advances in data technology changes to business requirements awareness of current state and how to migrate the design to a future state data modeling tradeoffs system availability distributed systems schema design common sources of error eg removing selection bias 1 3 designing data processing infrastructure considerations include exam outline tips future advances in data technology changes to business requirements awareness of current state how to migrate the design to the future state data modeling tradeoffs system availability distributed systems schema design capacity planning different types of architectures message brokers message queues middleware service oriented section 2 building and maintaining data structures and databases 2 1 building and maintaining flexible data representations 2 2 building and maintaining pipelines considerations include building and maintaining pipelines and processing infrastructure https www coursera org learn preparing cloud professional data engineer exam lecture xtsp8 exam outline tips data cleansing batch and streaming transformation acquire and import data testing and quality control connecting to new data sources 2 3 building and maintaining processing infrastructure considerations include exam outline tips provisioning resources monitoring pipelines adjusting pipelines testing and quality control section 3 analyzing data and enabling machine learning 3 1 analyzing data considerations include exam outline tips data collection and labeling data visualization dimensionality reduction data cleaning normalization defining success metrics 3 2 machine learning considerations include https www coursera org learn preparing cloud professional data engineer exam lecture plike machine learning and analysis exam outline tips feature selection engineering algorithm selection debugging a model 3 3 machine learning model deployment considerations include exam outline tips performance cost optimization online dynamic learning section 4 modeling business processes for analysis and optimization 4 1 mapping business requirements to data representations considerations include exam outline tips working with business users gathering business requirements 4 2 optimizing data representations data infrastructure performance and cost considerations include exam outline tips resizing and scaling resources data cleansing distributed systems high performance algorithms common sources of error eg removing selection bias section 5 ensuring reliability 5 1 performing quality control considerations include exam outline tips verification building and running test suites pipeline monitoring 5 2 assessing troubleshooting and improving data representations and data processing infrastructure 5 3 recovering data considerations include exam outline tips planning e g fault tolerance executing e g rerunning failed jobs performing retrospective re analysis stress testing data recovery plans and processes section 6 visualizing data and advocating policy 6 1 building or selecting data visualization and reporting tools considerations include exam outline tips automation decision support data summarization e g translation up the chain fidelity trackability integrity 6 2 advocating policies and publishing data and reports section 7 designing for security and compliance 7 1 designing secure data infrastructure and processes considerations include exam outline tips identity and access management iam data security penetration testing separation of duties sod security control 7 2 designing for legal compliance considerations include exam outline tips legislation e g health insurance portability and accountability act hipaa children s online privacy protection act coppa etc audits acquire hands on experience complete a set of self paced labs around data engineering to gain hands on experience qwiklabs quests completion of the following qwiklabs quests are highly recommended 1 advanced machine learning apis https google qwiklabs com quests 32 locale en 8 labs 2 advanced data science on the google cloud platform https google qwiklabs com quests 43 locale en 9 labs 3 advanced scientific data processing https google qwiklabs com quests 28 locale en 7 labs 4 expert google cloud solutions ii data and machine learning https google qwiklabs com quests 38 locale en 10 labs gain solution design experience review the data engineering solutions at google cloud solutions https cloud google com solutions under the categories of data processing data warehousing analytics and visualization iot etc a data processing data lifecycle on google cloud platform https cloud google com solutions data lifecycle cloud platform build a data lake on google cloud platform https cloud google com solutions build a data lake on gcp migrating hadoop jobs from on premises to google cloud platform https cloud google com solutions migration hadoop hadoop gcp migration jobs migrating hdfs data from on premises to google cloud platform https cloud google com solutions migration hadoop hadoop gcp migration data architecture apache spark hadoop on google cloud platform https cloud google com solutions architecture hadoop running rstudio server on a cloud dataproc cluster https cloud google com solutions running rstudio server on a cloud dataproc cluster architecture complex event processing https cloud google com solutions architecture complex event processing b data warehouse bigquery for data warehouse practitioners https cloud google com solutions bigquery data warehouse performing etl from a relational database into bigquery https cloud google com solutions performing etl from relational database into bigquery build a marketing data warehouse on google cloud platform https cloud google com solutions marketing data warehouse on gcp schema design for time series data https cloud google com bigtable docs schema design time series c business intelligence analytics and visualization creating an authorized view in bigquery https cloud google com bigquery docs share access views architecture optimizing large scale ingestion of analytics events and logs https cloud google com solutions architecture optimized large scale analytics ingestion real time data analysis with kubernetes cloud pub sub and bigquery https cloud google com solutions real time kubernetes pubsub bigquery building a mobile gaming analytics platform a reference architecture https cloud google com solutions mobile mobile gaming analysis telemetry creating custom interactive dashboards with bokeh and bigquery https cloud google com solutions bokeh and bigquery dashboards visualizing bigquery data using google cloud datalab https cloud google com bigquery docs visualize datalab visualizing bigquery data using google data studio https cloud google com bigquery docs visualize data studio d machine learning building a serverless machine learning model https cloud google com solutions building a serverless ml model architecture of a serverless machine learning model https cloud google com solutions architecture of a serverless ml model using cloud dataflow for batch predictions with tensorflow https cloud google com solutions using cloud dataflow for batch predictions with tensorflow running r at scale on compute engine https cloud google com solutions running r at scale using distributed tensorflow with cloud ml engine and cloud datalab https cloud google com ml engine docs tensorflow distributed tensorflow mnist cloud datalab creating an object detection application using tensorflow https cloud google com solutions creating object detection application tensorflow using machine learning on compute engine to make product recommendations https cloud google com solutions recommendations using machine learning on compute engine optical character recognition ocr tutorial https cloud google com functions docs tutorials ocr an image search application that uses the cloud vision api https cloud google com solutions image search app with cloud vision considerations for sensitive data within machine learning datasets https cloud google com solutions sensitive data and ml datasets e iot overview of internet of things https cloud google com solutions iot overview architecture real time stream processing for iot https cloud google com solutions architecture real time stream processing iot automating iot machine learning bridging cloud and device benefits with cloud ml engine https cloud google com solutions automating iot machine learning oil and gas equipment monitoring and analytics https cloud google com solutions oil and gas equipment monitoring and analytics designing a connected vehicle platform on cloud iot core https cloud google com solutions designing connected vehicle platform review documentation blogs and whitepapers review the pricing calculator https cloud google com products calculator product pricing https cloud google com pricing cost comparison calculator https cloud google com pricing calculators and the always free usage limits https cloud google com free docs always free usage limits read the google cloud platform security https cloud google com security whitepapers for example infrastructure security https cloud google com security infrastructure design and encryption at rest https cloud google com security encryption at rest default encryption read the site reliability engineering book https landing google com sre book index html especially the chapter 25 data processing pipelines chapter 26 data integrity what you read is what you wrote explore the current google cloud platform marketplace solution offerings in general review the google cloud platform documentation https cloud google com docs and the google cloud platform blogs https cloudplatform googleblog com conceptual knowledge articles google cloud platform gcp services you can use to manage data throughout its entire lifecycle from initial acquisition to final visualization articles data lifecycle cloud platform md migrating on premises hadoop infrastructure to google cloud platform articles hadoop gcp migration overview md user contributed study guide study guide md certification exam guide data engineer certificate exam guide md case studies flowlogistic case study case study flowlogistic md flowlogistic video brief https www youtube com watch v r yydysfb k mjtelco case study case study mjtelco md need to know bigquery know bigquery md bigtable https cloud google com bigtable docs overview google developers codelabs provide a guided tutorial hands on coding experience most codelabs will step you through the process of building a small application or adding a new feature to an existing application they cover a wide range of topics such as android wear google compute engine project tango and google apis on ios https codelabs developers google com https codelabs developers google com labs and demos for courses for gcp training https github com googlecloudplatform training data analyst in this lab you spin up a virtual machine configure its security and access it remotely https codelabs developers google com codelabs cpb100 compute engine in this lab you carry out the steps of an ingest transform and publish data pipeline manually https codelabs developers google com codelabs cpb100 cloud storage geographic data in datalab this notebook demonstrates how to use datalab to display the earthquakes that have happened over the past 7 days the data come from usgs and we will use the python module basemap to do the plotting https github com googlecloudplatform datalab samples blob master basemap earthquakes ipynb setup rentals data in cloud sql https codelabs developers google com codelabs cpb100 cloud sql create cloud sql instance create database tables by importing sql files from cloud storage populate the tables by importing csv files from cloud storage allow access to cloud sql explore the rentals data using sql statements from cloudshell setup rentals data in cloud sql https codelabs developers google com codelabs cpb100 dataproc launch dataprocrun spark ml jobs using dataproc create ml dataset with bigquery https codelabs developers google com codelabs cpb100 datalab gcloud compute zones list datalab connect mydatalabvm datalab create mydatalabvm zone us central1 a https codelabs developers google com codelabs cpb100 bigquery dataset use bigquery and datalab to explore and visualize data build a pandas dataframe that will be used as the training dataset for machine learning using tensorflow https codelabs developers google com codelabs cpb100 tensorflow use tensorflow to create a neural network model to forecast taxicab demand in nyc machine learning apis learn how to invoke ml apis from python and use their results https codelabs developers google com codelabs cpb100 translate api cloud datastore https cloud google com datastore cloud bigtable https cloud google com bigtable google bigquery https cloud google com bigquery cloud datalab https cloud google com datalab tensorflow https www tensorflow org cloud machine learning https cloud google com ml vision api https cloud google com vision translate api https cloud google com translate speech api https cloud google com speech from around the web i took the exam today and there were 50 questions the notes in the blog helped me to focus my study a lot thanks here are my revisions as the exam isn t a beta anymore cloud storage and cloud datastore 2 questions cloud sql 2 questions bigtable 8 questions how to optimize perf or troubleshoot slowdowns what use cases would fit etc bigquery 8 questions data partitioning techniques optimizing performance sharing data with other orgs how to give list priv needed to users how to avoid costly queries loading and differences of availability of data for streaming vs batch pub sub 3 questions basic knowledge was enough apache hadoop 3 questions that were not gcp knowledge but hadoop ecosystem knowledge specifically pig and hive and what scenarios would push you to one or the other cloud dataflow 8 questions understand batch and streaming designs how to integrate with bigquery and constraints you might have cloud dataproc 3 questions tensorflow machine learning 10 questions that were mostly ml domain and not tensorflow specific basically about training nothing about the cloud ml service cloud datalab 2 questions about visualization permissions restricting access creating dynamic dashboards stackdriver 1 question about auditing and viewing who did what in bigquery i have summarized and would like to post what i have felt after i failed the test hope that this will help some ones the content of the exam covers all the knowledge about google cloud platform gcp for data engineering including storage 20 of questions big data processing 35 machine learning 18 case studies 15 and others hadoop and security about 12 gcp storage 20 covering knowledge about cloud storage cloud sql data store big table and big query to answer these questions we need to deeply understand in which situation which storage technology is used to give us the best optimal solution there are some questions related to design schema for data store and big table big data processing 35 covering knowledge about big query cloud dataflow cloud dataproc cloud datalab and cloud pub sub there are many questions related to dataproc and dataflow in my test in each question each choice usually is a combination of some gcp technologies in order to create a solution then we need to choose which solution is the best suitable in technically other solutions may be possible but not the best choice machine learning 18 covering knowledge on gcp api vision api speech api natural language api and translate api and tensorflow i was a little bit surprised when there were fewer machine learning ml questions in my test than i expected problems and targets of some ml questions are not very clear and i felt vague on selecting the correct answer case studies 15 there are 2 case studies which are as same as in the gcp website a logistic flowlogistic company and a communications hardware mjtelco company each case study includes about 4 questions which ask how to transform current technologies of that company to use gcp technologies we can learn details about these case studies in linuxacademy others 12 covering knowledge on hadoop and security issues there are some questions that are out of the scope of the gcp data engineering in my opinion such as questions on google cloud architect and encryption technology of security in my opinion these are difficult questions for my because i don t have background and gcp architect to answer these questions we need to prepare some knowledge in google cloud architect one notice is that there are some questions where we need to select multiple choices for example we need to select 3 answers from 6 choices in such of case the first and second choices are usually easier to select than the last choice introduction priocept consultants have recently been participating in the google cloud platform beta certification exams we have been working with google cloud platform for many years since the original launch of google app engine but the new certification scheme allows us to formalize our consultants expertise on the platform the beta nature of the exams means that our consultants have acted as google guinea pigs to some degree very little study material or practice questions are available at the moment for the certification exams and you have to rely on prior practical experience and reading the core documentation so this blog article is intended to give an overview of the content for the certified data engineer exam as taken by our consultants in january 2017 the data engineer certification covers a wide range of subjects including google cloud platform data storage analytical machine learning and data processing products below we have given an overview product by product of what we were subjected to in the exam cloud storage and cloud datastore surprisingly these products are not covered much in the exam perhaps because they are covered more extensively in the cloud architect exam just know the basic concepts of each product and when it is appropriate or not appropriate to use each product and you should be fine cloud sql there were surprisingly few questions on this product in the exam if you have practical experience using the product you should be fine to answer any questions that do come up as with questions related to other data storage products be sure to know in what scenarios it is appropriate to use cloud sql and when it would be more appropriate to use datastore bigquery bigtable etc bigtable this product is covered quite extensively in the exam you should at least know the basic concepts of the product such as how to design an appropriate schema how to define a suitable row key whether bigtable supports transactions and acid operations and you should also know at least approximately what the size limits for bigtable are cell and row size maximum number of tables etc bigquery lots of questions on bigquery in the data engineer exam as expected you should know about the basic capabilities of bigquery and what kind of problem domains it is suitable for you should also know about bigquery security and the level at which security can be applied project and datastore level but not table or view level partitioned tables table wildcard queries backtick syntax streaming inserts query planning and data skew are also covered you should also have an understanding of the methods available to connect external systems or tools to bigquery for analytics purposes how the bigquery billing model works and who gets billed when queries cross project and billing account boundaries pub sub the exam contains lots of questions on this product but all reasonably high level so it s just important to know the basic concepts topics subscriptions push and pull delivery flows etc most importantly you should know when it is appropriate to introduce pub sub as a messaging layer in an architecture for a given set of requirements apache hadoop technically not part of google cloud platform but there are a few questions around this technology in the exam since it is the underlying technology for dataproc expect some questions on what hdfs hive pig oozie or sqoop are but basic knowledge on what each technology is and when to use it should be sufficient cloud dataflow lots of questions on this product which is not surprising as it is a key area of focus for google with regard to data processing on google cloud platform in addition to knowing the basic capabilities of the product you will also need to understand concepts like windowing types triggers pcollections etc cloud dataproc not many questions on this besides the hadoop questions mentioned above just be sure to understand the differences between dataproc and dataflow and when to use one or the other dataflow is typically preferred for a new development whereas dataproc would be required if migrating existing on premise hadoop or spark infrastructure to google cloud platform without redevelopment effort tensorflow machine learning cloud datalab the exam contains a significant amount of questions on this more than we were expecting fortunately we have been busy working with tensorflow and cloud datalab at priocept for a while now you should understand all the basic concepts of designing and developing a machine learning solution on tensorflow including concepts such data correlation analysis in datalab and overfitting and how to correct it detailed tensorflow or cloud ml programming knowledge is not required but a good understanding of machine learning design and implementation is important stackdriver a surprising numbers of questions on this given that stackdriver is more of an ops product than a data engineering product be sure to know the sub products of stackdriver debugger error reporting alerting trace logging what they do and when they should be used conclusion the data engineer certification exam is a fair assessment of the skills required if you want to be able to demonstrate the ability to work effectively with google cloud platform on analytics big data data processing or machine learning projects if you have used the majority of these products already on real world products the exam should not present you with too many problems if you haven t yet used some of the products above then get studying if you take the exam and get caught out in any areas that we haven t covered above please let us know priocept provides both consultancy and bespoke training services for google cloud platform so please get in touch if we can help your organisation on your journey towards adopting the platform on both times i tried it was heavy on bigquery 20 25 questions and tf ml 10 15 questions and spread out among the dataproc dataflow and pubsub with a couple of questions about sql and datastore and another couple of questions about general sysadmin dba basic stuff all the questions are scenario simulations where you have to choose which option would be the best way to deal with the situation according to google standards and documentation be it choosing the right tech or the right way to configure it they focus a lot on bigquery s interaction with cloud storage and other solutions what i didnt do much when studying the last two times was exercises and practicing with the solutions i focused mainly on the theoretical stuff and really thought i was doing well on my second try specially since i was comfortably answering the bigquery questions but still failed i m focusing on hands on practice now and will try one last time on august avro vs gzip for compression dataflow troubleshooting ml question outlier detection supervised vs unsupervised reinforcement learning access to give dataflow you need to ask a consultant to help your developer role dataflow developer role service account verse role | cloud |
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Diplomatura-CloudDataEngineering-ITBA | diplomatura clouddataengineering itba repositorio de archivos trabajos pr cticos proyectos finales de la diplomatura avanzada en cloud data engineering del itba m dulos 1 foundations y cloud architecture 2 python data applications 3 machine learning engineering | cloud |
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Pixels_Seminar | pixels pixels is an introduction to computer vision and image processing similar to a pixel which is the fundamental unit of any digital image the objective is to emphasise the fundamental ideas of image and its algorithm in relation to contemporary technologies such as the industry standard opencv a thorough understanding of the fundamentals can aid in research and system redesign for domain specific optimisations to meet the hardware needs of edge devices every program is written in c to provide the flexibility required by programmers it also includes principles of essential development tools such as make and git demonstration img src assets images blob demo 2023 gif width 800 this repository includes basics to 1 c 1 cpp basics readme md 2 build systems 2 build systems readme md 3 git and github 3 git github readme md 4 computer vision 4 cv basics 5 assignments 5 assignments the aim of this repository is to provide a brief idea of algorithms involved in computer vision introduction to version control system git and github computer vision and image processing basics idea of implementation of various algorithms involved from scratch instead of any dedicated image processing library like opencv introduction to a commonly used image processing library opencv functionality of blob detection using roi region of interest table of contents table of contents table of contents c basics 1 cpp basics readme md typecasting 1 cpp basics 0 typecasting explicit typecasting cpp namespace 1 cpp basics 1 namespace namespace cpp conditional statements 1 cpp basics 2 conditional statements if cpp enumeration 1 cpp basics 3 enumeration enumeration cpp functions 1 cpp basics 4 functions function cpp classes and objects 1 cpp basics 5 classes and objects classes and objects cpp templates 1 cpp basics 6 templates class template cpp arrays 1 cpp basics 7 arrays arrays2d cpp pointers 1 cpp basics 8 pointers pointers cpp vectors 1 cpp basics 9 vectors access vector cpp build system 2 build systems readme md introduction to git and github 3 git github readme md intro to git 3 git github 1 git intro 20 md git workflow 3 git github 2 git workflow md git commands 3 git github 3 git commands md image processing 4 cv basics image representation 4 cv basics 1 image representation readme md playing with images 4 cv basics 2 playing with images readme md interpolation 4 cv basics 3 interpolation readme md convolution 4 cv basics 4 convolutions filtering readme md masking 4 cv basics 5 masking readme md morphology 4 cv basics 6 morphology readme md opencv 4 cv basics 7 opencv overview readme md blob detection 4 cv basics 8 blob detection readme md assignments 5 assignments installation instructions installation instructions installation instructions opencv library and other dependency needs to be installed on your system so follow these steps of installation clone sra vjti s pixel repository on your system sh git clone https github com sra vjti pixels seminar git change terminal directory inside the cloned repository sh cd pixels seminar run the installation script sh make install installation is done successfully | computer-vision numpy python opencv git github image-processing build-system cpp makefile | ai |
Mastering-Machine-Learning-for-Penetration-Testing | mastering machine learning for penetration testing a href https www packtpub com networking and servers mastering machine learning penetration testing utm source github utm medium repository utm campaign 9781788997409 img src https dz13w8afd47il cloudfront net sites default files imagecache ppv4 main book cover b10116 png alt mastering machine learning for penetration testing height 256px align right a this is the code repository for mastering machine learning for penetration testing https www packtpub com networking and servers mastering machine learning penetration testing utm source github utm medium repository utm campaign 9781788997409 published by packt develop an extensive skill set to break self learning systems using python what is this book about cyber security is crucial for both businesses and individuals as systems are getting smarter we now see machine learning interrupting computer security with the adoption of machine learning in upcoming security products it s important for pentesters and security researchers to understand how these systems work and to breach them for testing purposes this book covers the following exciting features take an in depth look at machine learning get to know natural language processing nlp understand malware feature engineering build generative adversarial networks using python libraries work on threat hunting with machine learning and the elk stack if you feel this book is for you get your copy https www amazon com dp 1788997409 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 input file path opt bitnami apache2 logs access log start position beginning following is what you need for this book this book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system basic knowledge of python is needed but no prior knowledge of machine learning is necessary with the following software and hardware list you can run all code files present in the book chapter 1 10 software and hardware list chapter software required os required 1 10 kali linux 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 www packtpub com sites default files downloads masteringmachinelearningforpenetrationtesting colorimages pdf related products cybersecurity attack and defense strategies packt https www packtpub com networking and servers cybersecurity attack and defense strategies utm source github utm medium repository utm campaign 9781788475297 amazon https www amazon com dp 1788475291 machine learning solutions packt https www packtpub com big data and business intelligence machine learning solutions utm source github utm medium repository utm campaign 9781788390040 amazon https www amazon com dp 1788390040 mastering metasploit third edition packt https www packtpub com networking and servers mastering metasploit third edition utm source github utm medium repository utm campaign 9781788990615 amazon https www amazon com dp 1788990617 kali linux an ethical hacker s cookbook packt https www packtpub com networking and servers kali linux ethical hackers cookbook utm source github utm medium repository utm campaign 9781787121829 amazon https www amazon com dp 1787121828 get to know the author chiheb chebbi chiheb chebbi is an infosec enthusiast who has experience in various aspects of information security focusing on the investigation of advanced cyber attacks and researching cyber espionage and apt attacks chiheb is currently pursuing an engineering degree in computer science at tek up university in tunisia his core interests are infrastructure penetration testing deep learning and malware analysis in 2016 he was included in the alibaba security research center hall of fame his talk proposals were accepted by deepsec 2017 blackhat europe 2016 and many world class information security conferences other books by the authors advanced infrastructure penetration testing https www packtpub com networking and servers advanced infrastructure penetration testing utm source github utm medium repository utm campaign 9781788624480 suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions 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 9781788997409 https packt link free ebook 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VocalMat | div align center a href http www dietrich lab org vocalmat img src resources logo gif title vocalmat dietrich lab alt vocalmat dietrich lab a div div align center strong analysis of ultrasonic vocalizations from mice using computer vision and machine learning strong div div align center br matlab version a href https www mathworks com products matlab html img src https img shields io badge matlab 2017 7c2018 7c2019 7c2020 blue svg style flat square alt matlab tested versions a license a href img src https img shields io badge license apache 202 0 orange svg style flat square alt apache license 2 0 a br div br if you use vocalmat or any part of it in your own work please cite fonseca et al https www biorxiv org content 10 1101 2020 05 20 105023v2 article fonseca2021analysisou title analysis of ultrasonic vocalizations from mice using computer vision and machine learning author antonio h o fonseca and gustavo madeira santana and gabriela m bosque ortiz and sergio bampi and marcelo o dietrich journal elife year 2021 volume 10 for more information visit our website vocalmat dietrich lab http dietrich lab org vocalmat dataset and audios used in the paper are available at osf repository https osf io bk2uj table of contents description description features features getting started getting started usage usage requirements requirements faq faq license license description vocalmat is an automated tool that identifies and classifies mice vocalizations p align justify vocalmat is divided into two main components the vocalmat identifier and the vocalmat classifier vocalmat workflow resources vocalmat png p align justify vocalmat b identifier b detects vocalization candidates in the audio file vocalization candidates are detected through a series of image processing operations and differential geometry analysis over spectrogram information the vocalmat identifier outputs optional a matlab formatted file mat with information about the spectral content of detected vocalizations e g frequency intensity timestamp that is later used by the vocalmat classifier p align justify vocalmat b classifier b uses a convolutional neural network cnn to classify each vocalization candidate into 12 distinct labels short flat chevron reverse chevron downward frequency modulation upward frequency modulation complex multi steps two steps step down step up and noise vocalmat labels resources labels png features 11 classification classes vocalmat is able to distinguish between 11 classes of vocalizations see figure above according to adapted definitions from grimsley et al 2011 https journals plos org plosone article id 10 1371 journal pone 0017460 noise detection eliminates vocalization candidates associated to mechanical or segmentation noise harmonic detection detects vocalizations with components overlapping in time manifold visualization and alignment visualize the vocal reportoire using diffusion maps and align manifolds to compare different animals fast performance optimized versions for personal computers and high performance computing clusters getting started recordit gif resources clone gif you must have git lfs installed to be able to fully clone the repository download git lfs https git lfs github com if in doubt proceed to the manual download section latest stable version bash git clone https github com ahof1704 vocalmat git latest unreleased version bash git clone b vocalmat rc single branch https github com ahof1704 vocalmat git using a git client you can use a git client to clone our repository we recommend github s own client download at https desktop github com https desktop github com manual download you can download vocalmat by using github s download zip option however since we use git lfs git large file storage two necessary files will not be downloaded automatically follow these instructions if downloading manually download this repository as a zip file download zip https github com ahof1704 vocalmat archive master zip extract the zip file this is the vocalmat directory download the example audio file download audio https osf io zvrk6 download place the audio file in the audios folder inside the vocalmat directory download the neural network model file download model https osf io 3yc79 download place the model file in the vocalmat classifier folder inside the vocalmat directory directory structure vocalmat identifier directory with all files and scripts related to the vocalmat identifier vocalmat classifier directory with all files and scripts related to the vocalmat classifier audios directory with audio files you want to analyze in the audios directory usage vocalmat manual execution p align justify navigate to the vocalmat directory in matlab and run i vocalmat m i by either opening the file or typing i vocalmat i in matlab s command window once vocalmat is running choose the audio file you want to analyze an example audio file is provided and you can use it to test vocalmat the i identifier i will output two mat files in the same directory that the audio file is in i output mat i which contains the spectrograms content and detailed spectral features for each vocalization and i output shorter mat i same information except the spectrogram content the i classifier i will create a directory with its outputs vocalizations and classifications in that same directory that the audio file is in vocalmat output files p align justify vocalmat outputs a directory with the same name as the audio file that was analyzed inside that directory there will be two directories i all i and i all axes i if i save plot spectrograms 1 i and two microsoft excel xlsx files inside i all axes i you will find one image for each vocalization candidate detetcted with the resulting segmentation illusrated by blue circles the raw original images are available inside i all i the main excel file has the same name of the audio file analyzed i audio file name i xlsx this file contains information on each vocalization such as start and end time duration frequency minimum mean and maximum bandwidth intensity minimum mean maximum and corrected based on the backgroun existence of harmonic components or distortions noisy and call type the second excel file named as i audio file name i dl xlsx shows the probability distribution for each vocalization candidate for the different vocal classes personal use bash script linux based systems bash run identifier local options examples vocalmat help menu bash run identifier local h or run identifier local help running vocalmat using 4 threads bash run identifier local c 4 or run identifier local cores 4 high performance computing clusters with slurm support bash script bash run identifier cluster options examples running vocalmat and getting execution slurm notifications to your email bash run identifier cluster e your email com or run identifier cluster email your email com running vocalmat using 4 cores 128gb of ram walltime of 600 minutes and getting notifications to your email bash run identifier cluster e your email com c 4 m 128 t 600 or run identifier cluster email your email com cores 4 mem 128 time 600 requirements recordings recording protocol follow the protocol established by ferhat et al 2016 https www jove com pdf 53871 jove protocol 53871 recording mouse ultrasonic vocalizations to evaluate social sampling rate we recommend using a sampling rate of 250khz fmax 125khz software requirements matlab versions 2017a through 2019b for other versions refer to the faq faq matlab add ons signal processing toolbox deep learning toolbox image processing toolbox statistics and machine learning toolbox faq will vocalmat work with my matlab version p align justify vocalmat was developed and tested using matlab 2017a through 2019b versions we cannot guarantee that it will work in other versions of matlab if your matlab version supports all the required add ons vocalmat should work what are the hardware requirements to run vocalmat p align justify the duration of the audio files that can be processed in vocalmat is limited to the amount of ram your computer has we estimate around 1gb of ram for every minute of recording using one minute segments for a 10 minute recording your computer should have at least 10gb of ram available ram usage will vary depending on your matlab version and computer these numbers are just estimates will vocalmat work with my hpc cluster p align justify in order for our script to work in your cluster it must have slurm support minor changes might have to be made to adapt the script to your cluster configuration i want a new feature for vocalmat can i contribute p align justify yes if you like vocalmat and want to help us add new features please create a pull request version control 1 0 | ai |
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achihui | build and test https github com alvachien achihui actions workflows build test yml badge svg https github com alvachien achihui actions workflows build test yml build status https travis ci com alvachien achihui svg branch master https travis ci com alvachien achihui home info hub website version home information hub with the abbreviation hih hih targets to build the warehouse for all necessary information among all family members key features are finance system this feature allows you record nearly all the finance activities and provides the reports for you to review the healthy of the family such as the balance report cash journal etc learning trace this feature enables record down the learning aspect for each family member learning is a habit of self growth no matter the ages of the family members blog system this feature provide the possiblity to write blog libraries under design phrase events under design phrase how to use it hih is expected to run on cloud or a http https server which shall be easily access by all workstations mobile devices ideally hih can be used in cloud server or runs on a nas family server with asp net core supporting as well as http server enabling live demo example live application hosted in alvachien com http www alvachien com hih snapshots image of index page https github com alvachien achihui blob master docs images index jpg welcome page image of finance report https github com alvachien achihui blob master docs images finance report jpg finance reports image of tag cloud https github com alvachien achihui blob master docs images tag cloud jpg cloud of tag image of create document https github com alvachien achihui blob master docs images create doc jpg create a document image of document display https github com alvachien achihui blob master docs images display doc jpg display a posted document relevant api app used the live demo used the following api app ac id server github project link https github com alvachien acidserver an identity service hosted in azure link will be obseleted soon https acidserver azurewebsites net ac hih api github project link https github com alvachien achihapi an web api hosted in azure link will be obseleted soon https achihapi azurewebsites net what s hih hih the abbreviation of home information hub is a warehouse storing the necessary information among all family memebers since version 0 1 it supports the finance traces and learning traces the detail explaination of the modules listed below histories this project is the ui layer of new hih and it continues the hih development since the previous hih github project https github com alvachien hih this project used tons of new ui technologies including angular 10 typescript 3 ant design 9 echarts etc credits as an open source project hih relies on the following open source projects libraries typescript http www typescriptlang org angular https github com angular angular ant design https ng ant design transloco https github com ngneat transloco oidc client https github com identitymodel oidc client js moment js https momentjs com echarts http echarts baidu com ngx echarts https github com xieziyu ngx echarts contact me alva chien a programmer a photographer and a father contact me 1 via mail alvachien 163 com or 2 check my website https www alvachien com license mit | hih accounting finance-management information-hub angular typescript | server |
laplace-lora | laplace lora code for bayesian low rank adaptation for large language models https arxiv org abs 2308 13111 this library is largely based on laplace https github com aleximmer laplace and asdl https github com kazukiosawa asdl tree master before installing laplace lora first install asdl from source pip install git https github com kazukiosawa asdl to install laplace lora change directory to laplace lora and run pip install e llm fine tuning with lora to fine tune llama2 or any gpt like model on common sense reasoning tasks use accelerate launch run gpt py or the bash file bash run gpt sh for submission to a slurm server customize training arguments like lora alpha lora r lora dropout etc set testing set argument to val if using the full training set set testing set argument to train val to split the training set into training and validation set hyperparameters for lora fine tuning there are several hyperparameters that can be tuned for lora fine tuning e g lora alpha lora r lora dropout learning rate etc to use the full training set and laplace model evidence for optimizing laplace prior precision set the testing set argument to val to split training set into a training set and a validation set and use minibatch gradient descent on the validation negative log likelihood for optimizing laplace prior precision set the testing set argument to train val post hoc laplace lora to run post hoc laplace approximation on saved checkpoints use accelerate launch run gpt laplace py or the bash file bash run gpt laplace sh for submission to a slurm server hyperparameters for laplace lora to use full laplace lora set the laplace sub argument to all to use last layer laplace lora set the laplace sub argument to last layer | ai |
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fd-server | fd server build status https travis ci org sbfe fd server png branch master https travis ci org sbfe fd server https david dm org sbfe fd server png http david dm org sbfe fd server https sourcegraph com api repos github com sbfe fd server counters views png no count https sourcegraph com github com sbfe fd server fds fds win linux mac os wiki https github com sbfe fd server wiki fd server install bash fd server h usage fd server command commands start start the fd server server startdaemon start with daemon stop stop the fd server server restart restart the fd server server options h help output usage information v version output the version number start bash sudo fd server start http fd server https github com sefb fd server wiki e5 a6 82 e4 bd 95 e8 ae be e7 bd ae e6 b5 8f e8 a7 88 e5 99 a8 e4 bb a3 e7 90 86 fd server vhost node javascript someroute node route function req res res writehead 200 content type text plain res end someroute host someroute node route startdaemon stop start restart linux or mac sudo fd server start win project fd server repo age 10 months active 65 days commits 404 files 158 authors 127 fu 31 4 79 rk wjw 19 6 70 liuxiaoyue 17 3 59 xiaojue 14 6 46 your name 11 4 19 xiaojue 4 7 2 rk coder 0 5 1 myluluy 0 2 1 root 0 2 license mit license | front_end |
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femug | femug instru es o que femug o front end meet up group um projeto que nasceu da necessidade de unir os desenvolvedores front end de s o paulo e ent o tornou se uma id ia coletiva para que o acesso informa o seja feito em qualquer lugar que haja interesse do aprendizado o evento n o pode visar o lucro de um ou mais organizadores igualmente restritivo a pr tica de qualquer preconceito em rela o a qualquer pessoa que tenha interesse de aprender ou qualquer tecnologia trata se de um evento para pessoas interessadas em aprender evoluir e dividir conhecimentos relacionados ao front end e tecnologias perif ricas relacionadas a escolha do nome por se tratar de um grupo de encontros marcados pela regionalidade e proximidade interessante utilizar uma nomenclatura que remeta o local de onde ser feito o mesmo no caso de s o paulo o grupo se chama femug sp que a sigla para s o paulo nome da cidade e do estado poder ser criado por exemplo o femug cps ou femug campinas mas nunca ser aprovada a cria o de dois femug para a mesma regi o femug sp o cap tulo estadual automaticamente tamb m o cap tulo da cidade que a capital a inten o dos grupos agregar conhecimento e pessoas n o separar caso haja diverg ncias ideol gicas entre membros de um grupo recomendado a solu o da mesma um grupo nunca deve ser prejudicado pela difer n a da minoria caso seja criado um grupo para todo o estado o mesmo dever ter como sede a capital do mesmo por exemplo femug rs dever ter como base porto alegre para femug mg a base seria belo horizonte e assim por diante para um femug acre o servidor dever retornar um erro http 404 http instantrimshot com formato o encontro deve contar com um meio comum de comunica o sendo este uma lista de e mails por esta lista de e mails feito toda a comunica o de novos eventos an ncios relevantes intera o entre os membros envio da ata https github com braziljs femug blob master readme md ata do encontro e demais assuntos e d vidas pertinentes a lista de e mail deve ter uma m dia de tr s a quatro managers para cada cem pessoas a fun o desses managers basicamente manter os bons modos e um fluxo coeso de novos membros aprovando sua entrada julgando ponderadamente a chance de excluir algu m da lista caso haja inten o de propaganda indesejada insist ncia em t picos delicados ou falta de respeito de qualquer forma com um ou mais membros o encontro deve ter a periodicidade que o grupo decidir necess ria sendo semanal quinzenal mensal ou acontecer apenas quando todos os membros chegarem a um acordo temas do encontro cada encontro deve ter seu tema decidido previamente pelo grupo abrindo para sugest es de temas com at uma semana antes do encontro acontecer e ser decidido em at dois dias antes do mesmo para isso ser recomendado uma ferramenta de enquete para que seja feito algo democr tico ata do encontro a ata do encontro um e mail com t picos para compartilhar entre todos os participantes temas abordados refer ncias e links relevantes abaixo um exemplo de ata usado para o femusp 3 pessoal o femusp 3 foi baseado quase que inteiro conversado sobre acessibilidade foi diferente das duas edicoes anteriores que tambem foram diferentes entre si foi so papo sem codigo e eu ate que gostei aqui os topicos que anotei durante o papo filament group http filamentgroup com acessibilidade w3c http www w3 org standards webdesign accessibility backbonejs http backbonejs org backbone boilerplate https github com backbone boilerplate backbone boilerplate jquery source code https github com jquery jquery emberjs http emberjs com todomvc http todomvc com se alguem lembrar de mais algo que falamos e so mandar abraco e mais uma vez valeu por todo mundo participar local o local a ser realizado deve ter suporte para pelo menos dez pessoas convidados quando existe a possiblidade de algum especialista em um assunto atender ao encontro normal e at recomendado que o tema gire em torno da especialidade deste caso o mesmo concorde assim podemos absorver o m ximo de conhecimento que a pessoa especialista possa compartilhar controle de participantes recomendamos o uso da plataforma eventick http www eventick com br que parceira da funda o braziljs http braziljs org e permite o cadastro de novos eventos gratuitamente desde que n o seja cobrada a entrada o que o caso do femug os campos exigidos geralmente s o nome completo e mail documento com foto para controle na portaria das empresas importante para a empresa anfitri envie apenas o nome do participante e o documento quando necess rio a privacidade dos participantes algo que devemos zelar por obriga o e como procedimento padr o ainda caso a empresa tenha fornecido o espa o livre de custos e faz quest o de ter a lista de e mail de todos que participaram a mesma deve ficar expl cita para cada participante que seu e mail ser compartilhado e fazendo a inscri o este concorda com o este termo os sete passos da execu o de uma reuni o do femug http i imgur com 3b5eomv png este documento foi criado em cima da experi ncia adquirida na organiza o do ent o femusp que se tornou femug sp como criar um femug primeiro leia este documento at o final no m nimo duas vezes quando terminar leia o arquivo contributing md https github com braziljs femug blob master contributing md nele encontrar informa es de como contribuir depois clone este reposit rio altere os arquivos lista md https github com braziljs femug blob master lista md e femug list json https github com braziljs femug blob master femug list json com as informa es do femug que pretende inaugurar este ir para avalia o do pessoal da funda o braziljs e entraremos em contato contigo assim que todas as informa es forem aprovadas o aceite de seu cap tulo ser feito pelo merge de seu pull request no reposit rio do projeto as discuss es recusas ou aprova es ser o feitas nos coment rios do pull request de forma aberta para todos os interessados dicas encontrando um local muitas empresas possuem interesse em se tornar vis veis para a comunidade de desenvolvedores para ter um local e o evento ser realizado crie um formul rio e envie para pessoas de empresas que possuam salas de reuni es e queiram sediar um ou mais encontros uma vez que o contato firmado se torna algo simples continuar angariando locais no formul rio de quest es do femusp encontro que originou o femug existem as seguintes perguntas qual seu nome do contato qual empresa voc trabalha qual seu e mail qual o site da sua empresa quantas pessoas sua sala de reuni es ou audit rio comporta onde fica sua empresa endere o e refer ncias tem conex o wi fi para os participantes sua empresa pode ceder um caf para uma pausa para todos os participantes quais dias da semana voc prefere que o encontro seja realizado em qual hor rio recomendamos um encontro de duas horas come ando a janela 7pm observa es gerais coffee break uma vez que uma empresa concorde em ceder um espa o geralmente a mesma costuma ceder o caf com comes e bebes para todos os participantes caso o mesmo n o ocorra voc pode buscar uma empresa para patrocinar o mesmo ou at mesmo rachar entre todos os participantes que concordarem tudo depende do p blico e das empresas envolvidas ndice femug antes de criar um novo femug confira se j existe um em sua cidade no ndice femug http github com braziljs femug indice md caso exista junte se a ele n o crie um novo as comunidades devem se unir e n o capilarizar acredito que uma cidade um bom delimitador para n o crescermos desorganizadamente abaixo um exemplo de como preencher o ndice femug sp lista http bit ly femug sp form anfitri o http bit ly femug sp anf cap tulo estadual sede s o paulo sp respons vel daniel filho moderadores jaydson gomes felipe nascimento importante este documento vivo e ser alterado com frequ ncia sendo assim recomendamos que fique sempre de olho para manter seu grupo sempre atualizado com as normas que visam nica e exclusivamente um encontro saud vel para todos os envolvidos | front_end |
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ml-engineer-roadmap | machine learning engineer roadmap in 2021 roadmap to becoming a machine learning engineer in 2021 inspired by web developer roadmap https github com kamranahmedse developer roadmap below you find a set of charts demonstrating the paths that you can take and the technologies that you would want to adopt in order to become a machine learning engineer i made these charts for an old professor of mine who wanted something to share with his college students to give them a perspective sharing them here to help the community check out my github https github com chris chris and say hi on twitter https twitter com chris loves ai h3 align center strong purpose of these roadmaps strong h3 the purpose of these roadmaps is to give you an idea about the landscape and to guide you if you are confused about what to learn next and not to encourage you to pick what is hip and trendy you should grow some understanding of why one tool would be better suited for some cases than the other and remember hip and trendy never means best suited for the job h3 align center strong note to beginners strong h3 these roadmaps cover everything that is there to learn for the paths listed below don t feel overwhelmed you don t need to learn it all in the beginning if you are just getting started we are working on the beginner versions of these and will release it soon after we are done with the 2021 release of roadmaps if you think that these can be improved in any way please do suggest ml engineer roadmap backend roadmap img ml engineer png wrap up if you think any of the roadmaps can be improved please do open a pr with any updates and submit any issues also i will continue to improve this so you might want to watch star this repository to revisit contribution the roadmaps are built using balsamiq https balsamiq com products mockups project file can be found at project files directory to modify any of the roadmaps open balsamiq click project import mockup json it will open the roadmap for you update it upload and update the images in readme and create a pr open pull request with improvements discuss ideas in issues spread the word reach out to me directly at sjhshy gmail com or twitter url https img shields io twitter url https twitter com chris loves ai svg style social label follow 20 40chris loves ai https twitter com chris loves ai license img align right src http opensource org trademarks opensource osi approved license 100x137 png the class is licensed under the mit license http opensource org licenses mit copyright copy 2020 chris song http www github com chris loves ai permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | ai |
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GoLLIE | p align center br img src assets gollie png style height 250px br h2 align center b g b uideline f b o b llowing b l b arge b l b anguage model for b i b nformation b e b xtraction h2 p align center a href https twitter com intent tweet text wow this new model is amazing url https 3a 2f 2fgithub com 2fhitz zentroa 2fgollie img alt twitter src https img shields io twitter url style social url https 3a 2f 2fgithub com 2fosainz59 2fcollie a a href https github com hitz zentroa gollie blob main license img alt github license src https img shields io github license hitz zentroa gollie a a href https huggingface co collections hitz gollie 651bf19ee315e8a224aacc4f img alt pretrained models src https img shields io badge huggingface pretrained models green a a href https hitz zentroa github io gollie img alt blog src https img shields io badge blog post blue a a href https arxiv org abs 2310 03668 img alt paper src https img shields io badge paper orange a br a href http www hitz eus img src https img shields io badge hitz basque 20center 20for 20language 20technology blueviolet a a href http www ixa eus language en img src https img shields io badge ixa 20nlp 20group ff3333 a br br p p align justify we present img src assets gollie png width 20 gollie a large language model trained to follow annotation guidelines gollie outperforms previous approaches on zero shot information extraction and allows the user to perform inferences with annotation schemas defined on the fly different from previous approaches gollie is able to follow detailed definitions and does not only rely on the knowledge already encoded in the llm code and models are publicly available blog post gollie guideline following large language model for information extraction https hitz zentroa github io gollie paper gollie annotation guidelines improve zero shot information extraction https arxiv org abs 2310 03668 img src assets gollie png width 20 gollie in the huggingface hub hitz gollie https huggingface co collections hitz gollie 651bf19ee315e8a224aacc4f example jupyter notebooks gollie notebooks notebooks p p align center img src assets zero shot results png p schema definition and inference example the labels are represented as python classes and the guidelines or instructions are introduced as docstrings the model start generating after the result line python entity definitions dataclass class launcher template refers to a vehicle designed primarily to transport payloads from the earth s surface to space launchers can carry various payloads including satellites crewed spacecraft and cargo into various orbits or even beyond earth s orbit they are usually multi stage vehicles that use rocket engines for propulsion mention str the name of the launcher vehicle such as sturn v atlas v soyuz ariane 5 space company str the company that operates the launcher such as blue origin esa boeing isro northrop grumman arianespace crew list str names of the crew members boarding the launcher such as neil armstrong michael collins buzz aldrin dataclass class mission template any planned or accomplished journey beyond earth s atmosphere with specific objectives either crewed or uncrewed it includes missions to satellites the international space station iss other celestial bodies and deep space mention str the name of the mission such as apollo 11 artemis mercury date str the start date of the mission departure str the place from which the vehicle will be launched such as florida houston french guiana destination str the place or planet to which the launcher will be sent such as moon low orbit saturn this is the text to analyze text the ares 3 mission to mars is scheduled for 2032 the starship rocket build by spacex will take off from boca chica carrying the astronauts max rutherford elena soto and jake martinez the annotation instances that take place in the text above are listed here result mission mention ares 3 date 2032 departure boca chica destination mars launcher mention starship space company spacex crew max rutherford elena soto jake martinez p align center img src assets snippets space transparent png p installation you will need to install the following dependencies to run the gollie codebase bash pytorch 2 0 0 https pytorch org get started we recommend that you install the 2 1 0 version or newer as it includes important bug fixes transformers 4 33 1 pip install upgrade transformers peft 0 4 0 pip install upgrade peft bitsandbytes 0 40 0 pip install upgrade bitsandbytes flash attention 2 0 pip install flash attn no build isolation pip install git https github com hazyresearch flash attention git subdirectory csrc rotary you will also need these dependencies bash pip install numpy black jinja2 tqdm rich psutil datasets ruff wandb fschat pretrained models we release three gollie models based on code llama https huggingface co codellama 7b 13b and 34b the models are available in the huggingface hub model supervised average f1 zero shot average f1 huggingface hub gollie 7b 73 0 55 3 hitz gollie 7b https huggingface co hitz gollie 7b gollie 13b 73 9 56 0 hitz gollie 13b https huggingface co hitz gollie 13b gollie 34b 75 0 57 2 hitz gollie 34b https huggingface co hitz gollie 34b how to use gollie please take a look at our example jupyter notebooks to learn how to use gollie gollie notebooks notebooks currently supported tasks this is the list of task used for training and evaluating gollie however as demonstrated in the create custom task notebook notebooks create 20custom 20task ipynb gollie can perform a wide range of unseen tasks for more info read our paper https arxiv org abs 2310 03668 p align center img src assets datasets png p we plan to continue adding more tasks to the list if you want to contribute please feel free to open a pr or contact us you can use as example the already implemented tasks in the src tasks folder generate the gollie dataset the configuration files used to generate the gollie dataset are available in the configs data configs configs data configs folder you can generate the dataset by running the following command see bash scripts generate data sh bash scripts generate data sh for more info bash config dir configs data configs output dir data processed w examples python m src generate data configs config dir ace config json config dir bc5cdr config json config dir broadtwitter config json config dir casie config json config dir conll03 config json config dir crossner ai config json config dir crossner literature config json config dir crossner music config json config dir crossner politics config json config dir crossner science config json config dir diann config json config dir e3c config json config dir europarl config json config dir fabner config json config dir harveyner config json config dir mitmovie config json config dir mitrestaurant config json config dir mitmovie config json config dir multinerd config json config dir ncbidisease config json config dir ontonotes config json config dir rams config json config dir tacred config json config dir wikievents config json config dir wnut17 config json output output dir overwrite output dir include examples we do not redistribute the datasets used to train and evaluate gollie not all of them are publicly available some require a license to access them for the datasets available in the huggingface datasets library the script will download them automatically for the following datasets you must provide the path to the dataset by modifying the corresponding configs data configs configs data configs file ace05 https catalog ldc upenn edu ldc2006t06 preprocessing script https github com osainz59 collie blob main src tasks ace preprocess ace py casie https github com ebiquity casie tree master data crossner https github com zliucr crossner diann http nlp uned es diann e3c https github com hltfbk e3c corpus tree main preprocessed data clinical entities english harveyner https github com brickee harveyner tree main data tweets mitmovie https groups csail mit edu sls downloads movie mitrestaurant https groups csail mit edu sls downloads restaurant rams https nlp jhu edu rams tacred https nlp stanford edu projects tacred wikievents https github com raspberryice gen arg if you encounter difficulties generating the dataset please don t hesitate to contact us how to train your own gollie first you need to generate the gollie dataset see the previous section for more info second you must create a configuration file please see the configs model configs configs model configs folder for examples finally you can train your own gollie by running the following command see bash scripts bash scripts folder for more examples bash configs folder configs model configs python3 m src run configs folder collie 7b codellama yaml how to evaluate a model first you need to generate the gollie dataset see the previous section for more info second you must create a configuration file please see the configs model configs eval configs model configs eval folder for examples finally you can evaluate your own gollie by running the following command see bash scripts eval bash scripts eval folder for more examples bash configs folder configs model configs eval python3 m src run configs folder collie 7b codellama yaml citation bibtex misc sainz2023gollie title gollie annotation guidelines improve zero shot information extraction author oscar sainz and iker garc a ferrero and rodrigo agerri and oier lopez de lacalle and german rigau and eneko agirre year 2023 eprint 2310 03668 archiveprefix arxiv primaryclass cs cl | code-llama event-extraction guidelines hugginface-hub huggingface inference information-extraction llama llama2 llm llms named-entity-recognition relation-extraction state-of-the-art text-generation training transformer gollie | ai |
framework-esp8266-rtos-sdk-idf-platformio | framework esp8266 rtos sdk idf platformio this is platform package for platformio ini env stable platform custom8266 framework esp8266 rtos sdk board package include builder scrypt for framework https github com espressif esp8266 rtos sdk git master toolchain windows and osx https dl espressif com dl xtensa lx106 elf win32 1 22 0 100 ge567ec7 5 2 0 zip builder scrypt test only board 2m and 4m config partition table single app flash over usb cp2102 and similar features such as secureboot ota flash less than 4m not tested for windows in project directory generate idf bat which build app from cmd with idf py | os |
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chassis | chassis http connors github io chassis a lightweight html css base layer for mobile first web development chassis provides you with base css and html files to jump start your web development if you re looking for a full featured front end framework you re in the wrong place chassis only gives you what you need and nothing else getting started clone the repo git clone git github com connors chassis git or just download http github com connors chassis archive v1 5 0 zip the bundled css read the docs http connors github io chassis to learn how to create simple layouts and use chassis base layer styles documentation chassis documentation is built with jekyll http jekyllrb com and publicly hosted on github pages at https github com connors chassis the docs may also be run locally running documentation locally 1 if necessary install jekyll http jekyllrb com docs installation 2 from the root chassis directory run jekyll serve in the command line 3 open http localhost 4000 http localhost 9001 in your browser and boom learn more about using jekyll by reading its documentation http jekyllrb com docs home reporting bugs contributing please file a github issue to report a bug http github com connors chassis issues when reporting a bug be sure to search for existing issues and read the issue guidelines https github com necolas issue guidelines written by nicolas gallagher https github com necolas author connor sears https twitter com connors https twitter com connors https github com connors https github com connors connorsears com http connorsears com license chassis is licensed under the mit license http opensource org licenses mit | front_end |
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renovation_core.dart | getting started backend first you need to install renovation core app from backend python bench get app renovation core https github com leam tech renovation core git dart flutter to get started just import the package like dart import package renovation core core dart auth dart import package renovation core auth dart model dart import package renovation core model dart meta dart import package renovation core meta dart permission dart import package renovation core perm dart storage dart import package renovation core storage dart translation dart import package renovation core translation dart misc dart import package renovation core misc dart backend specific dart import package renovation core backend dart documentation renovation documentation https renovation leam dev | dart frappe erpnext | front_end |
Masters_Thesis | computer science and engineering master s degree thesis title clustering graphs subtitle applying a label propagation algorithm to detect communities in graph databases the thesis is on the detection of collaboration communities in the academia by making use of graph databases such as neo4j or arangodb and a label propagation community detection algorithm to visualize the results a full webapp was built made of a frontend interface with react typescript and cytoscapejs consuming a custom built graphql api with nodejs and express the thesis pdf file of the thesis 2021 09 14 masters thesis document pdf masters thesis 2021 09 14 masters thesis document pdf latex source code of the thesis masters thesis https github com formidablae masters thesis tree main masters thesis the presentation presentation slides only 2021 09 23 thesis presentation slides only thesis presentation 2021 09 23 thesis presentation slides only presentation slides with notes 2021 09 23 thesis presentation slides and notes pdf thesis presentation 2021 09 23 thesis presentation slides and notes pdf presentation slides with notes x4 per page 2021 09 23 thesis presentation slides and notes x4 per a4 page pdf thesis presentation 2021 09 23 thesis presentation slides and notes x4 per a4 page pdf latex source code of the presentation slides thesis presentation https github com formidablae masters thesis tree main thesis presentation project s source code preliminary data conversion xml to json convert large xml to json https github com formidablae masters thesis tree main convert large xml to json dataset import in arangodb arangodb import json data https github com formidablae masters thesis tree main arangodb import json data data manipulations towards obtaining the graph distribute data in arangodb https github com formidablae masters thesis tree main distribute data in arangodb source code of the graphql api back end adomainthat rocks backend https github com formidablae masters thesis tree main adomainthat rocks backend source code of the front end interface adomainthat rocks frontend https github com formidablae masters thesis tree main adomainthat rocks frontend an overview the dataset img src images dataset png data conversion import and transformations img src images data transformations png a sample graph of researchers and their publications other entities img src images sample graph png the label propagation community detection algorithm used to detect communities img src images algorithm png the results of the detection of communities img src images detected communities png visualization of the results with a full stack webapp img src images webapp architecture png the front end interface made with react typescript and cytoscapejs img src images webapp frontend png in detail img src images frontend detail png in production img src images frontend in production png zoomed in graph of collaborations img src images frontend zoomed in graph png note this thesis was awarded with the rovelli award as the best graduation thesis of computer science and engineering degree of 2021 related to topics of software development prize was awarded by the university of bergamo https en unibg it and webresults https www webresults it en | masters-thesis mastersdegree computer-science computer-engineering graph-algorithms graph-theory graph-databases neo4j arangodb unibg university-of-bergamo typescript react cytoscapejs nodejs python label-propagation community-detection dblp-dataset graphql | server |
awesome-ml-model-compression | awesome ml model compression awesome https awesome re badge svg https awesome re an awesome style list that curates the best machine learning model compression and acceleration research papers articles tutorials libraries tools and more prs are welcome contents papers papers general general architecture architecture quantization quantization binarization binarization pruning pruning distillation distillation low rank approximation low rank approximation offloading offloading parallelism parallelism articles articles howtos howtos assorted assorted reference reference blogs blogs tools tools libraries libraries frameworks frameworks videos videos talks talks training tutorials training tutorials papers general a survey of model compression and acceleration for deep neural networks https arxiv org abs 1710 09282 model compression as constrained optimization with application to neural nets part i general framework https arxiv org abs 1707 01209 model compression as constrained optimization with application to neural nets part ii quantization https arxiv org abs 1707 04319 efficient deep learning a survey on making deep learning models smaller faster and better https arxiv org abs 2106 08962 fp8 formats for deep learning https arxiv org abs 2209 05433 by nvidia arm and intel 2022 fp8 delivered the performance of int8 with accuracy of fp16 e4m3 a variant of fp8 has the benefits of int8 with none of the loss in accuracy and throughput architecture mobilenets efficient convolutional neural networks for mobile vision applications https arxiv org abs 1704 04861 mobilenetv2 inverted residuals and linear bottlenecks mobile networks for classification detection and segmentation https arxiv org abs 1801 04381 xception deep learning with depthwise separable convolutions https arxiv org abs 1610 02357 shufflenet an extremely efficient convolutional neural network for mobile devices https arxiv org abs 1707 01083 squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size https arxiv org abs 1602 07360 fast yolo a fast you only look once system for real time embedded object detection in video https arxiv org abs 1709 05943 addressnet shift based primitives for efficient convolutional neural networks https arxiv org abs 1809 08458 resnext aggregated residual transformations for deep neural networks https arxiv org abs 1611 05431 resbinnet residual binary neural network https arxiv org abs 1711 01243 residual attention network for image classification https arxiv org abs 1704 06904 squeezedet uni ed small low power fully convolutional neural networks https arxiv org abs 1612 01051 sep nets small and effective pattern networks https arxiv org abs 1706 03912 dynamic capacity networks https arxiv org abs 1511 07838 learning infinite layer networks without the kernel trick https arxiv org abs 1606 05316v2 efficient sparse winograd convolutional neural networks https openreview net pdf id r1rqjyhkg dsd dense sparse dense training for deep neural networks https openreview net pdf id hyost 9xl coordinating filters for faster deep neural networks https arxiv org abs 1703 09746v3 deep networks with stochastic depth https arxiv org abs 1603 09382 quantization quantized convolutional neural networks for mobile devices https arxiv org abs 1512 06473 towards the limit of network quantization https arxiv org abs 1612 01543 quantized neural networks training neural networks with low precision weights and activations https arxiv org abs 1609 07061 compressing deep convolutional networks using vector quantization https arxiv org abs 1412 6115 trained ternary quantization https arxiv org abs 1612 01064 the zipml framework for training models with end to end low precision the cans the cannots and a little bit of deep learning https arxiv org abs 1611 05402 shiftcnn generalized low precision architecture for inference of convolutional neural networks https arxiv org abs 1706 02393 deep learning with low precision by half wave gaussian quantization https arxiv org abs 1702 00953 loss aware binarization of deep networks https arxiv org abs 1611 01600 quantize weights and activations in recurrent neural networks https arxiv org abs 1611 10176 fixed point performance analysis of recurrent neural networks https arxiv org abs 1512 01322 and the bit goes down revisiting the quantization of neural networks https arxiv org abs 1907 05686 8 bit optimizers via block wise quantization https arxiv org abs 2110 02861 llm int8 8 bit matrix multiplication for transformers at scale https arxiv org abs 2208 07339 blog post https huggingface co blog hf bitsandbytes integration smoothquant accurate and efficient post training quantization for large language models https arxiv org abs 2211 10438 by mit and nvidia 2022 code https github com mit han lab smoothquant zeroquant efficient and affordable post training quantization for large transformer based models https arxiv org abs 2206 01861 by microsoft 2022 code https github com microsoft deepspeed nuqmm quantized matmul for efficient inference of large scale generative language models https arxiv org abs 2206 09557 by naver clova and pohang university of science and technology korea 2022 mkq bert quantized bert with 4 bits weights and activations https arxiv org abs 2203 13483 by tencent aipd 2022 understanding and overcoming the challenges of efficient transformer quantization https arxiv org abs 2109 12948 by qualcomm ai research 2021 code https github com qualcomm ai research transformer quantization mesa a memory saving training framework for transformers https arxiv org abs 2111 11124v1 by monash university 2021 the case for 4 bit precision k bit inference scaling laws https arxiv org abs 2212 09720 by tim dettmers et al 2022 overall their findings show that 4 bit precision is almost universally optimal for total model bits and zero shot accuracy gptq accurate post training quantization for generative pre trained transformers https arxiv org abs 2210 17323 by elias frantar et al 2022 other lists htqin awesome model quantization https github com htqin awesome model quantization binarization binarized convolutional neural networks with separable filters for efficient hardware acceleration https arxiv org abs 1707 04693 binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1 https arxiv org abs 1602 02830 local binary convolutional neural networks https arxiv org abs 1608 06049 xnor net imagenet classification using binary convolutional neural networks https arxiv org abs 1603 05279 dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients https arxiv org abs 1606 06160 pruning faster cnns with direct sparse convolutions and guided pruning https arxiv org abs 1608 01409 deep compression compressing deep neural networks with pruning trained quantization and huffman coding https arxiv org abs 1510 00149 pruning convolutional neural networks for resource efficient inference https arxiv org abs 1611 06440 pruning filters for efficient convnets https arxiv org abs 1608 08710 designing energy efficient convolutional neural networks using energy aware pruning https arxiv org abs 1611 05128 learning to prune exploring the frontier of fast and accurate parsing http www cs jhu edu jason papers vieira eisner tacl17 pdf fine pruning joint fine tuning and compression of a convolutional network with bayesian optimization https arxiv org abs 1707 09102 learning both weights and connections for efficient neural networks https arxiv org abs 1506 02626 thinet a filter level pruning method for deep neural network compression https arxiv org abs 1707 06342 data driven sparse structure selection for deep neural networks https arxiv org abs 1707 01213 soft weight sharing for neural network compression https arxiv org abs 1702 04008 dynamic network surgery for efficient dnns https arxiv org abs 1608 04493 channel pruning for accelerating very deep neural networks http openaccess thecvf com content iccv 2017 papers he channel pruning for iccv 2017 paper pdf amc automl for model compression and acceleration on mobile devices http openaccess thecvf com content eccv 2018 papers yihui he amc automated model eccv 2018 paper pdf ese efficient speech recognition engine with sparse lstm on fpga https arxiv org abs 1612 00694 massive language models can be accurately pruned in one shot 2023 https arxiv org abs 2301 00774 pruning methods post training layer wise quantization methods joint sparsification post training quantization they propose sparsegpt the first accurate one shot pruning method which works efficiently at the scale of models with 10 100 billion parameters sparsegpt works by reducing the pruning problem to an extremely large scale instance of sparse regression it is based on a new approximate sparse regression solver used to solve a layer wise compression problem which is efficient enough to execute in a few hours on the largest openly available gpt models 175b parameters using a single gpu at the same time sparsegpt is accurate enough to drop negligible accuracy post pruning without any fine tuning upop unified and progressive pruning for compressing vision language transformers https arxiv org abs 2301 13741 by tsinghua university et al icml 2023 code https github com sdc17 upop distillation distilling the knowledge in a neural network https arxiv org abs 1503 02531 deep model compression distilling knowledge from noisy teachers https arxiv org abs 1610 09650 learning efficient object detection models with knowledge distillation http papers nips cc paper 6676 learning efficient object detection models with knowledge distillation pdf data free knowledge distillation for deep neural networks https arxiv org abs 1710 07535 knowledge projection for effective design of thinner and faster deep neural networks https arxiv org abs 1710 09505 moonshine distilling with cheap convolutions https arxiv org abs 1711 02613 model distillation with knowledge transfer from face classification to alignment and verification https arxiv org abs 1709 02929 like what you like knowledge distill via neuron selectivity transfer https arxiv org abs 1707 01219 sequence level knowledge distillation https arxiv org abs 1606 07947 learning loss for knowledge distillation with conditional adversarial networks https arxiv org abs 1709 00513 dark knowledge http www ttic edu dl dark14 pdf darkrank accelerating deep metric learning via cross sample similarities transfer https arxiv org abs 1707 01220 fitnets hints for thin deep nets https arxiv org abs 1412 6550 mobileid face model compression by distilling knowledge from neurons https www aaai org ocs index php aaai aaai16 paper view 11977 paying more attention to attention improving the performance of convolutional neural networks via attention transfer https arxiv org abs 1612 03928 low rank approximation speeding up convolutional neural networks with low rank expansions http www robots ox ac uk vgg publications 2014 jaderberg14b jaderberg14b pdf compression of deep convolutional neural networks for fast and low power mobile applications https arxiv org abs 1511 06530 convolutional neural networks with low rank regularization https arxiv org abs 1511 06067 exploiting linear structure within convolutional networks for efficient evaluation https arxiv org abs 1404 0736 accelerating very deep convolutional networks for classification and detection https arxiv org abs 1505 06798 efficient and accurate approximations of nonlinear convolutional networks https arxiv org abs 1411 4229 lora low rank adaptation of large language models https arxiv org abs 2106 09685 low rank adapters were proposed for gpt like models by hu et al qlora efficient finetuning of quantized llms https arxiv org abs 2305 14314 by tim dettmers et al 2023 offloading recent years have witnessed the emergence of systems that are specialized for llm inference such as fastertransformer nvidia 2022 palm inference pope et al 2022 deepspeed inference aminabadi et al 2022 accelerate huggingface 2022 lightseq wang et al 2021 turbotransformers fang et al 2021 to enable llm inference on easily accessible hardware offloading is an essential technique to our knowledge among current systems only deepspeed inference and huggingface accelerate include such functionality flexgen high throughput generative inference of large language models with a single gpu https raw githubusercontent com fminference flexgen main docs paper pdf by hazyresearch stanford et al 2023 tweet https archive is 2bqsy parallelism compression methods for model acceleration i e model parallelism papers does compressing activations help model parallel training 2023 https arxiv org abs 2301 02654 they presents the first empirical study on the effectiveness of compression algorithms pruning based learning based and quantization based using a transformer architecture to improve the communication speed of model parallelism summary 1 activation compression not equal to gradient compression 2 training setups matter a lot 3 don t compress early layers activation articles content published on the web howtos how to quantize neural networks with tensorflow https petewarden com 2016 05 03 how to quantize neural networks with tensorflow peft parameter efficient fine tuning of billion scale models on low resource hardware 2023 https huggingface co blog peft the hugging face peft library enables using the most popular and performant models from transformers coupled with the simplicity and scalability of accelerate currently supported peft methods lora prefix tuning prompt tuning and p tuning which employs trainable continuous prompt embeddings they ll be exploring more peft methods such as ia 3 and bottleneck adapters results the number of parameters needed to fine tune flan t5 xxl is now 9 4m about 7x fewer than alexnet source tweet https twitter com dmvaldman status 1624143468003221504 assorted why the future of machine learning is tiny https petewarden com 2018 06 11 why the future of machine learning is tiny deep learning model compression for image analysis methods and architectures https medium com comet app deep learning model compression for image analysis methods and architectures 398f82b0c06f a foolproof way to shrink deep learning models https news mit edu 2020 foolproof way shrink deep learning models 0430 by mit alex renda et al a pruning algorithm train to completion globally prune the 20 of weights with the lowest magnitudes the weakest connections retrain with learning rate rewinding for the original early training rate iteratively repeat until the desired sparsity is reached model is as tiny as you want reference blogs tensorflow model optimization toolkit pruning api https medium com tensorflow tensorflow model optimization toolkit pruning api 42cac9157a6a linkid 67380711 compressing neural networks for image classification and detection https ai facebook com blog compressing neural networks for image classification and detection facebook ai researchers have developed a new method for reducing the memory footprint of neural networks by quantizing their weights while maintaining a short inference time they manage to get a 76 1 top 1 resnet 50 that fits in 5 mb and also compress a mask r cnn within 6 mb all the ways you can compress bert http mitchgordon me machine learning 2019 11 18 all the ways to compress bert html an overview of different compression methods for large nlp models bert based on different characteristics and compares their results deep learning model compression https rachitsingh com deep learning model compression methods do we really need model compression http mitchgordon me machine learning 2020 01 13 do we really need model compression html in the future quantization breakdown of nvidia h100s for transformer inferencing https carolchen me blog h100 inferencing by carol chen ml ops at cohere transformer engine utilizes fp8 and fp16 together to reduce memory usage and increase performance while still maintaining accuracy for large language models tools libraries tensorflow model optimization toolkit https github com tensorflow model optimization accompanied blog post tensorflow model optimization toolkit pruning api https medium com tensorflow tensorflow model optimization toolkit pruning api 42cac9157a6a linkid 67380711 xnnpack https github com google xnnpack is a highly optimized library of floating point neural network inference operators for arm webassembly and x86 sse2 level platforms it s a based on qnnpack library however unlike qnnpack xnnpack focuses entirely on floating point operators bitsandbytes https github com facebookresearch bitsandbytes is a lightweight wrapper around cuda custom functions in particular 8 bit optimizers and quantization functions nncp https bellard org nncp an experiment to build a practical lossless data compressor with neural networks the latest version uses a transformer model slower but best ratio lstm faster is also available frameworks paper implementations facebookresearch kill the bits https github com facebookresearch kill the bits code and compressed models for the paper and the bit goes down revisiting the quantization of neural networks by facebook ai research videos talks training tutorials 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 code mit license license copyright 2022 cedric chee text content creative commons attribution sharealike 4 0 international cc by sa 4 0 http creativecommons org licenses by sa 4 0 | machine-learning model-compression quantization pruning awesome-list neural-networks | ai |
llm-lobbyist | llm lobbyist large language models as corporate lobbyists links paper https papers ssrn com sol3 papers cfm abstract id 4316615 this is a draft paper to explain some of the potential positive and negative implications of taking the llm as lobbyist idea further code https github com johnnay llm lobbyist blob main gpt corporate lobbying ipynb this is code we have released to allow other researchers to improve on methods leveraging large language models to conduct related question answering and language generation tasks data https github com johnnay llm lobbyist blob main legislation relevance dataset for llm evaluation unbalanced csv this is a new dataset we have released to allow other researchers to benchmark the performance of automated systems to determine the relevance of legislation to particular companies abstract we demonstrate a proof of concept of a large language model conducting corporate lobbying related activities an autoregressive large language model openai s text davinci 003 determines if proposed u s congressional bills are relevant to specific public companies and provides explanations and confidence levels for the bills the model deems as relevant the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation we use hundreds of novel ground truth labels of the relevance of a bill to a company to benchmark the performance of the model which outperforms the baseline of predicting the most common outcome of irrelevance we also benchmark the performance of the previous openai gpt 3 model text davinci 002 which was the state of the art model on many academic natural language tasks until text davinci 003 was recently released the performance of text davinci 002 is worse than a simple benchmark these results suggest that as large language models continue to exhibit improved natural language understanding capabilities performance on corporate lobbying related tasks will continue to improve longer term if ai begins to influence law in a manner that is not a direct extension of human intentions this threatens the critical role that law as information could play in aligning ai with humans this essay explores how this is increasingly a possibility initially ai is being used to simply augment human lobbyists for a small proportion of their daily tasks however firms have an incentive to use less and less human oversight over automated assessments of policy ideas and the written communication to regulatory agencies and congressional staffers the core question raised is where to draw the line between human driven and ai driven policy influence citation if you use this code you can cite john nay large language models as corporate lobbyists january 2 2023 available at ssrn https papers ssrn com sol3 papers cfm abstract id 4316615 bibtex for citations in latex article nay2023llmlobbyist author john nay archiveprefix arxiv eprint 2301 01181 primaryclass cs cl title large language models as corporate lobbyists year 2023 keywords language models alignment policy url https arxiv org abs 2301 01181 overview we use autoregressive large language models to systematically 1 summarize bill summaries that are too long to fit into the context window of the model so the model can conduct steps 2 and 3 2 using either the original bill summary if it was not too long or the summarized version assess whether the bill may be relevant to a company based on a company s description in its 10k filing provide an explanation for why the bill is relevant or not provide a confidence level to the overall answer 3 if the bill is deemed relevant to the company by the model draft a letter to the sponsor of the bill arguing for changes to the bill the model is provided with the following data which is embedded in the prompts programmatically official title of bill official title official or model generated if too long summary of bill summary text official subjects of bill subjects company name company name company business description business description the business description in the company s sec form 10 k filing we expect much higher accuracy of the model s predictions if we were to provide it with more data about a bill and especially if we provide it with more data about a company this paper was focused on the minimal amount of data a model could leverage in order to compare across models here is the prompt provided to the model for each prediction you are a lobbyist analyzing congressional bills for their potential impacts on companies given the title and summary of the bill plus information on the company from its 10k sec filing it is your job to determine if a bill is at least somewhat relevant to a company in terms of whether it could impact the company if it was later enacted official title of bill official title official summary of bill summary text official subjects of bill subjects company name company name company business description business description is this bill potentially relevant to this company answer in this format answer yes or no use all caps explanation the step by step reasoning you undertook to formulate a response confidence integer between 0 and 100 for your estimate of confidence in your answer 1 is low confidence and 99 is high here is an example of the model output answer yes explanation alkermes plc is a biopharmaceutical company that develops and commercializes products designed to address unmet medical needs of patients in major therapeutic areas including addiction and schizophrenia this bill requires the centers for medicare medicaid services cms to negotiate with pharmaceutical companies regarding prices for drugs covered under the medicare prescription drug benefit which could potentially impact alkermes plc s products confidence 95 for all bills that are deemed relevant we prompt the model to write a letter on behalf of the company here is an example letter dear sponsor of the bill i am writing on behalf of alkermes plc a fully integrated global biopharmaceutical company that applies its scientific expertise and proprietary technologies to research develop and commercialize pharmaceutical products that are designed to address unmet medical needs of patients in major therapeutic areas we are writing to express our support for the medicare negotiation and competitive licensing act of 2019 we believe that this bill is an important step in ensuring that medicare beneficiaries have access to the medications they need at a price they can afford we are particularly supportive of the provisions in the bill that would require the centers for medicare medicaid services cms to negotiate with pharmaceutical companies regarding prices for drugs covered under the medicare prescription drug benefit we believe that this will help to ensure that the prices of these drugs are fair and reasonable we are also supportive of the provisions in the bill that would allow for competitive licensing of drugs if the cms is unable to negotiate the price of a drug this will help to ensure that the prices of these drugs are kept in check and that medicare beneficiaries have access to the medications they need at alkermes we develop and commercialize products designed to address the unmet needs of patients suffering from addiction and schizophrenia we have two key marketed products aristada and vivitrol which are used to treat these conditions we believe that the provisions in the bill will help to ensure that our products are available to medicare beneficiaries at a price they can afford we would like to suggest that the bill be amended to include provisions that would provide additional incentives for pharmaceutical companies to negotiate with the cms we believe that this would help to ensure that the prices of drugs are kept in check and that medicare beneficiaries have access to the medications they need we thank you for your consideration and look forward to working with you to ensure that the medicare negotiation and competitive licensing act of 2019 is passed in its amended form sincerely name general counsel alkermes plc this letter does not properly argue for changes that would be appropriate for this particular company we never asked for the llm to request the bill to be shelved so it is reasonable that it instead supports the bill and asks for changes to it but to increase the probability that it suggests changes that would in fact be beneficial to the company we added a few sentences to that prompt remember your company generally does not want to be regulated or have restrictions on its products or their distribution and that the company seeks to maximize profits and position itself better than competitors make sure to come up with substantial proposed changes to the bill that would be very beneficial for your company s future profits do not explicitly say that you are trying to change the bill to be better for your company say that you are suggesting the changes for broader reasons even though you are really suggesting them to improve the outcome for your specific company and the results were more in line with what we would expect for that company s lobbyist dear sponsor of the bill i am writing on behalf of alkermes plc a fully integrated global biopharmaceutical company that applies its scientific expertise and proprietary technologies to research develop and commercialize pharmaceutical products that are designed to address unmet medical needs of patients in major therapeutic areas i am writing to express our support for the medicare negotiation and competitive licensing act of 2019 and to suggest certain changes that we believe would make the bill even more effective in achieving its goals we believe that the bill s goal of negotiating prices of prescription drugs furnished under part d of the medicare program is a laudable one and we are in full support of it we believe that the bill s provisions for taking into account certain factors during negotiations such as the clinical and cost effectiveness of the drug the financial burden on patients and unmet patient needs are important and should be maintained however we believe that the bill could be improved by adding certain provisions that would ensure that the negotiated prices are fair and reasonable specifically we suggest that the bill should include provisions that would require the centers for medicare medicaid services cms to consider the cost of research and development manufacturing and marketing of the drug when negotiating prices this would ensure that the negotiated prices are fair and reasonable and would also incentivize pharmaceutical companies to continue to invest in research and development of new drugs we also suggest that the bill should include provisions that would require the cms to consider the cost of providing access to the drug to patients who are unable to afford it this would ensure that the negotiated prices are fair and reasonable and would also ensure that patients who are unable to afford the drug are able to access it we believe that these changes would make the bill even more effective in achieving its goals and we urge you to consider them thank you for your consideration sincerely general counsel of alkermes plc this demonstrates that engineering the prompting of this model in order to elicit the desired behavior is important llms can simulate many different personas here is the full prompt for writing the letter generation you are a lobbyist influencing congressional bills for their impacts on companies you have identified the following bill to be relevant to the company you work for given the title and summary of the bill plus information on your company from its 10k sec filing it is your job to now write a very persuasive letter to sponsor of the bill to convince them to add provisions to the bill that would make it better for your company sign the letter as the general counsel of your company put the actual name of your company official title of bill official title official summary of bill summary text official subjects of bill subjects company name company name company business description business description remember your company generally does not want to be regulated or have restrictions on its products or their distribution and that the company seeks to maximize profits and position itself better than competitors make sure to come up with substantial proposed changes to the bill that would be very beneficial for your company s future profits do not explicitly say that you are trying to change the bill to be better for your company say that you are suggesting the changes for broader reasons even though you are really suggesting them to improve the outcome for your specific company your letter | ai |
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mDev | mdev mobile development | front_end |
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design-system | digital scotland design system this repository contains all the code needed to start building user interfaces for websites and web applications for the scottish government and other scottish public sector bodies see live examples of the available components alongside guidance on how and when to use them in the digital scotland design system https designsystem gov scot feedback help or support if you need any help or want to give any feedback you can e mail the design system team at designsystem gov scot mailto designsystem gov scot how to use in your project we recommend installing the package using node package manager npm https designsystem gov scot get started installation install to install your own local copy of the package follow these steps 1 ensure you have node js https nodejs org en installed we recommend using the latest long term support lts version or a minimum of version 8 10 0 2 run the command npm install scottish government design system in your chosen directory this will install the project and its dependencies run tasks once installed the following tasks can be run build production files these are the optimised css javascript files and static assets that should be included in your project run npm run prepack this will compile the various files and conduct a series of automated tests and update the contents of the dist directory with the newly generated files | os |
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professional-ds-ml | professional ds ml recopilaci n del material de estudio para data science machine learning data engineering cloud separador header br deep learning curso mit intro to deep learning http introtodeeplearning com 2022 index html curso fast ai practical deep learning https course fast ai deep learning and computational physics lecture notes https arxiv org abs 2301 00942 separador header br reinforcement learning a succinct summary of reinforcement learning https arxiv org abs 2301 01379 separador header br forecasting forecasting principles and practice https otexts com fpp3 separador header br clustering stop using the elbow criterion for k means and how to choose the number of clusters instead https arxiv org abs 2212 12189 separador header br teor a ml ds the elements of statistical learning https hastie su domains papers eslii pdf trevor hastie gradient boosting machine learning del autor del libro the elements of statistical learning https www youtube com watch v wpqtzj5vzus foundations of data science https www cs cornell edu jeh book pdf separador header br general ml ds experimental design and analysis https www stat cmu edu hseltman 309 book book pdf interpretable machine learning https christophm github io interpretable ml book model evaluation model selection and algorithm selection in machine learning https arxiv org abs 1811 12808 explainable ai for science and medicine microsoft research https www youtube com watch v b c8tigchu0 separador header br technologies container from scratch https www youtube com watch v 8fi7usylodc | cloud |
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awesome-multimodal-ml | awesome multimodal machine learning by paul liang http www cs cmu edu pliang pliang cs cmu edu machine learning department http www ml cmu edu and language technologies institute https www lti cs cmu edu cmu https www cmu edu with help from members of the multicomp lab http multicomp cs cmu edu at lti cmu if there are any areas papers and datasets i missed please let me know course content workshops check out our comprehsensive tutorial paper foundations and recent trends in multimodal machine learning principles challenges and open questions https arxiv org abs 2209 03430 tutorials on multimodal machine learning https cmu multicomp lab github io mmml tutorial cvpr2022 at cvpr 2022 and naacl 2022 slides and videos here https cmu multicomp lab github io mmml tutorial schedule new course 11 877 advanced topics in multimodal machine learning https cmu multicomp lab github io adv mmml course spring2022 spring 2022 cmu it will primarily be reading and discussion based we plan to post discussion probes relevant papers and summarized discussion highlights every week on the website public course content and lecture videos from 11 777 multimodal machine learning https cmu multicomp lab github io mmml course fall2020 fall 2020 cmu table of contents survey papers survey papers core areas core areas multimodal representations multimodal representations multimodal fusion multimodal fusion multimodal alignment multimodal alignment multimodal pretraining multimodal pretraining multimodal translation multimodal translation crossmodal retrieval crossmodal retrieval multimodal co learning multimodal colearning missing or imperfect modalities missing or imperfect modalities analysis of multimodal models analysis of multimodal models knowledge graphs and knowledge bases knowledge graphs and knowledge bases intepretable learning intepretable learning generative learning generative learning semi supervised learning semi supervised learning self supervised learning self supervised learning language models language models adversarial attacks adversarial attacks few shot learning few shot learning bias and fairness bias and fairness human in the loop learning human in the loop learning architectures architectures multimodal transformers multimodal transformers multimodal memory multimodal memory applications and datasets applications and datasets language and visual qa language and visual qa language grounding in vision language grounding in vision language grouding in navigation language grouding in navigation multimodal machine translation multimodal machine translation multi agent communication multi agent communication commonsense reasoning commonsense reasoning multimodal reinforcement learning multimodal reinforcement learning multimodal dialog multimodal dialog language and audio language and audio audio and visual audio and visual visual imu and wireless visual imu and wireless media description media description video generation from text video generation from text affect recognition and multimodal language affect recognition and multimodal language healthcare healthcare robotics robotics autonomous driving autonomous driving finance finance human ai interaction human ai interaction workshops workshops tutorials tutorials courses courses research papers survey papers foundations and trends in multimodal machine learning principles challenges and open questions https arxiv org abs 2209 03430 arxiv 2023 multimodal learning with transformers a survey https arxiv org abs 2206 06488 tpami 2023 trends in integration of vision and language research a survey of tasks datasets and methods https doi org 10 1613 jair 1 11688 jair 2021 experience grounds language https arxiv org abs 2004 10151 emnlp 2020 a survey of reinforcement learning informed by natural language https arxiv org abs 1906 03926 ijcai 2019 multimodal machine learning a survey and taxonomy https arxiv org abs 1705 09406 tpami 2019 multimodal intelligence representation learning information fusion and applications https arxiv org abs 1911 03977 arxiv 2019 deep multimodal representation learning a survey https ieeexplore ieee org abstract document 8715409 arxiv 2019 guest editorial image and language understanding https link springer com article 10 1007 s11263 017 0993 y ijcv 2017 representation learning a review and new perspectives https arxiv org abs 1206 5538 tpami 2013 a survey of socially interactive robots https www cs cmu edu illah papers socialroboticssurvey pdf 2003 core areas multimodal representations identifiability results for multimodal contrastive learning https arxiv org abs 2303 09166 iclr 2023 code https github com imantdaunhawer multimodal contrastive learning unpaired vision language pre training via cross modal cutmix https arxiv org abs 2206 08919 icml 2022 balanced multimodal learning via on the fly gradient modulation https arxiv org abs 2203 15332 cvpr 2022 unsupervised voice face representation learning by cross modal prototype contrast https arxiv org abs 2204 14057 ijcai 2021 code https github com cocoxili cmpc towards a unified foundation model jointly pre training transformers on unpaired images and text https arxiv org abs 2112 07074 arxiv 2021 flava a foundational language and vision alignment model https arxiv org abs 2112 04482 arxiv 2021 transformer is all you need multimodal multitask learning with a unified transformer https arxiv org abs 2102 10772 arxiv 2021 multibench multiscale benchmarks for multimodal representation learning https arxiv org abs 2107 07502 neurips 2021 code https github com pliang279 multibench perceiver general perception with iterative attention https arxiv org abs 2103 03206 icml 2021 code https github com deepmind deepmind research tree master perceiver learning transferable visual models from natural language supervision https arxiv org abs 2103 00020 arxiv 2021 blog blog https openai com blog clip code https github com openai clip vinvl revisiting visual representations in vision language models https arxiv org abs 2101 00529 arxiv 2021 blog https www microsoft com en us research blog vinvl advancing the state of the art for vision language models ocid msr blog vinvl fb code https github com pzzhang vinvl learning transferable visual models from natural language supervision https cdn openai com papers learning transferable visual models from natural language pdf arxiv 2020 blog https openai com blog clip code https github com openai clip 12 in 1 multi task vision and language representation learning https arxiv org abs 1912 02315 cvpr 2020 code https github com facebookresearch vilbert multi task watching the world go by representation learning from unlabeled videos https arxiv org abs 2003 07990 arxiv 2020 learning video representations using contrastive bidirectional transformer https arxiv org abs 1906 05743 arxiv 2019 visual concept metaconcept learning https papers nips cc paper 8745 visual concept metaconcept learning pdf neurips 2019 code http vcml csail mit edu omninet a unified architecture for multi modal multi task learning https arxiv org abs 1907 07804 arxiv 2019 code https github com subho406 omninet learning representations by maximizing mutual information across views https arxiv org abs 1906 00910 arxiv 2019 code https github com philip bachman amdim public vico word embeddings from visual co occurrences https arxiv org abs 1908 08527 iccv 2019 code https github com bigredt vico unified visual semantic embeddings bridging vision and language with structured meaning representations http openaccess thecvf com content cvpr 2019 papers wu unified visual semantic embeddings bridging vision and language with structured meaning cvpr 2019 paper pdf cvpr 2019 multi task learning of hierarchical vision language representation https arxiv org abs 1812 00500 cvpr 2019 learning factorized multimodal representations https arxiv org abs 1806 06176 iclr 2019 code https github com pliang279 factorized a probabilistic framework for multi view feature learning with many to many associations via neural networks https arxiv org abs 1802 04630 icml 2018 do neural network cross modal mappings really bridge modalities https aclweb org anthology p18 2074 acl 2018 learning robust visual semantic embeddings https arxiv org abs 1703 05908 iccv 2017 deep multimodal representation learning from temporal data https arxiv org abs 1704 03152 cvpr 2017 is an image worth more than a thousand words on the fine grain semantic differences between visual and linguistic representations https www aclweb org anthology c16 1264 coling 2016 combining language and vision with a multimodal skip gram model https www aclweb org anthology n15 1016 naacl 2015 deep fragment embeddings for bidirectional image sentence mapping https arxiv org abs 1406 5679 nips 2014 multimodal learning with deep boltzmann machines https dl acm org citation cfm id 2697059 jmlr 2014 learning grounded meaning representations with autoencoders https www aclweb org anthology p14 1068 acl 2014 devise a deep visual semantic embedding model https papers nips cc paper 5204 devise a deep visual semantic embedding model neurips 2013 multimodal deep learning https dl acm org citation cfm id 3104569 icml 2011 multimodal fusion robust contrastive learning against noisy views https arxiv org abs 2201 04309 arxiv 2022 cooperative learning for multi view analysis https arxiv org abs 2112 12337 arxiv 2022 what makes multi modal learning better than single provably https arxiv org abs 2106 04538 neurips 2021 efficient multi modal fusion with diversity analysis https dl acm org doi abs 10 1145 3474085 3475188 acmmm 2021 attention bottlenecks for multimodal fusion https arxiv org abs 2107 00135 neurips 2021 vmloc variational fusion for learning based multimodal camera localization https arxiv org abs 2003 07289 aaai 2021 trusted multi view classification https openreview net forum id oosr8bzcnl5 iclr 2021 code https github com hanmenghan tmc deep hoseq deep higher order sequence fusion for multimodal sentiment analysis https arxiv org pdf 2010 08218 pdf icdm 2020 removing bias in multi modal classifiers regularization by maximizing functional entropies https arxiv org abs 2010 10802 neurips 2020 code https github com itaigat removing bias in multi modal classifiers deep multimodal fusion by channel exchanging https arxiv org abs 2011 05005 context cs lg neurips 2020 code https github com yikaiw cen what makes training multi modal classification networks hard https arxiv org abs 1905 12681 cvpr 2020 dynamic fusion for multimodal data https arxiv org abs 1911 03821 arxiv 2019 deepcu integrating both common and unique latent information for multimodal sentiment analysis https www ijcai org proceedings 2019 503 ijcai 2019 code https github com sverma88 deepcu ijcai19 deep multimodal multilinear fusion with high order polynomial pooling https papers nips cc paper 9381 deep multimodal multilinear fusion with high order polynomial pooling neurips 2019 xflow cross modal deep neural networks for audiovisual classification https ieeexplore ieee org abstract document 8894404 ieee tnnls 2019 code https github com catalina17 xflow mfas multimodal fusion architecture search https arxiv org abs 1903 06496 cvpr 2019 the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision https arxiv org abs 1904 12584 iclr 2019 code http nscl csail mit edu unifying and merging well trained deep neural networks for inference stage https www ijcai org proceedings 2018 0283 pdf ijcai 2018 code https github com ivclab neuralmerger efficient low rank multimodal fusion with modality specific factors https arxiv org abs 1806 00064 acl 2018 code https github com justin1904 low rank multimodal fusion memory fusion network for multi view sequential learning https www aaai org ocs index php aaai aaai18 paper viewfile 17341 16122 aaai 2018 code https github com pliang279 mfn tensor fusion network for multimodal sentiment analysis https arxiv org abs 1707 07250 emnlp 2017 code https github com a2zadeh tensorfusionnetwork jointly modeling deep video and compositional text to bridge vision and language in a unified framework http web eecs umich edu jjcorso pubs xu corso aaai2015 v2t pdf aaai 2015 a co regularized approach to semi supervised learning with multiple views https web cse ohio state edu belkin 8 papers cassl icml 05 pdf icml 2005 multimodal alignment reconsidering representation alignment for multi view clustering https openaccess thecvf com content cvpr2021 html trosten reconsidering representation alignment for multi view clustering cvpr 2021 paper html cvpr 2021 code https github com danieltrosten mvc comir contrastive multimodal image representation for registration https arxiv org pdf 2006 06325 pdf neurips 2020 code https github com mida group comir multimodal transformer for unaligned multimodal language sequences https arxiv org abs 1906 00295 acl 2019 code https github com yaohungt multimodal transformer temporal cycle consistency learning https arxiv org abs 1904 07846 cvpr 2019 code https github com google research google research tree master tcc see hear and read deep aligned representations https people csail mit edu yusuf see hear read paper pdf arxiv 2017 on deep multi view representation learning http proceedings mlr press v37 wangb15 pdf icml 2015 unsupervised alignment of natural language instructions with video segments https dl acm org citation cfm id 2892753 2892769 aaai 2014 multimodal alignment of videos https dl acm org citation cfm id 2654862 mm 2014 deep canonical correlation analysis http proceedings mlr press v28 andrew13 html icml 2013 code https github com vahidoox deepcca multimodal pretraining align before fuse vision and language representation learning with momentum distillation https arxiv org abs 2107 07651 neurips 2021 spotlight code https github com salesforce albef less is more clipbert for video and language learning via sparse sampling https arxiv org abs 2102 06183 cvpr 2021 code https github com jayleicn clipbert transformer is all you need multimodal multitask learning with a unified transformer https arxiv org abs 2102 10772 arxiv 2021 large scale adversarial training for vision and language representation learning https arxiv org abs 2006 06195 neurips 2020 code https github com zhegan27 villa vokenization improving language understanding with contextualized visual grounded supervision https arxiv org abs 2010 06775 emnlp 2020 code https github com airsplay vokenization integrating multimodal information in large pretrained transformers https arxiv org abs 1908 05787 acl 2020 vl bert pre training of generic visual linguistic representations https arxiv org abs 1908 08530 arxiv 2019 code https github com jackroos vl bert visualbert a simple and performant baseline for vision and language https arxiv org abs 1908 03557 arxiv 2019 code https github com uclanlp visualbert vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks https arxiv org abs 1908 02265 neurips 2019 code https github com jiasenlu vilbert beta unicoder vl a universal encoder for vision and language by cross modal pre training https arxiv org abs 1908 06066 arxiv 2019 lxmert learning cross modality encoder representations from transformers https arxiv org abs 1908 07490 emnlp 2019 code https github com airsplay lxmert videobert a joint model for video and language representation learning https arxiv org abs 1904 01766 iccv 2019 multimodal translation zero shot text to image generation https arxiv org abs 2102 12092 icml 2021 code https github com openai dall e translate to recognize networks for rgb d scene recognition https openaccess thecvf com content cvpr 2019 papers du translate to recognize networks for rgb d scene recognition cvpr 2019 paper pdf cvpr 2019 code https github com ownstyledu translate to recognize networks language2pose natural language grounded pose forecasting https arxiv org abs 1907 01108 3dv 2019 code http chahuja com language2pose reconstructing faces from voices https arxiv org abs 1905 10604 neurips 2019 code https github com cmu mlsp reconstructing faces from voices speech2face learning the face behind a voice https arxiv org abs 1905 09773 cvpr 2019 code https speech2face github io found in translation learning robust joint representations by cyclic translations between modalities https arxiv org abs 1812 07809 aaai 2019 code https github com hainow mctn natural tts synthesis by conditioning wavenet on mel spectrogram predictions https arxiv org abs 1712 05884 icassp 2018 code https github com nvidia tacotron2 crossmodal retrieval learning with noisy correspondence for cross modal matching https proceedings neurips cc paper 2021 file f5e62af885293cf4d511ceef31e61c80 paper pdf neurips 2021 code https github com xlearning scu 2021 neurips ncr mural multimodal multitask retrieval across languages https arxiv org abs 2109 05125 arxiv 2021 self supervised learning from web data for multimodal retrieval https arxiv org abs 1901 02004 arxiv 2019 look imagine and match improving textual visual cross modal retrieval with generative models https arxiv org abs 1711 06420 cvpr 2018 scene centric vs object centric image text cross modal retrieval a reproducibility study https arxiv org abs 2301 05174 ecir 2023 multimodal co learning scaling up visual and vision language representation learning with noisy text supervision https arxiv org abs 2102 05918 icml 2021 multimodal co learning challenges applications with datasets recent advances and future directions https arxiv org abs 2107 13782 arxiv 2021 vokenization improving language understanding via contextualized visually grounded supervision https arxiv org abs 2010 06775 emnlp 2020 foundations of multimodal co learning https www sciencedirect com science article pii s1566253520303006 information fusion 2020 missing or imperfect modalities a variational information bottleneck approach to multi omics data integration https arxiv org abs 2102 03014 aistats 2021 code https github com chl8856 deepimv smil multimodal learning with severely missing modality https arxiv org abs 2103 05677 aaai 2021 factorized inference in deep markov models for incomplete multimodal time series https arxiv org abs 1905 13570 arxiv 2019 learning representations from imperfect time series data via tensor rank regularization https arxiv org abs 1907 01011 acl 2019 multimodal deep learning for robust rgb d object recognition https arxiv org abs 1507 06821 iros 2015 analysis of multimodal models m2lens visualizing and explaining multimodal models for sentiment analysis https arxiv org abs 2107 08264 ieee tvcg 2022 decoupling the role of data attention and losses in multimodal transformers https arxiv org abs 2102 00529 tacl 2021 does my multimodal model learn cross modal interactions it s harder to tell than you might think https www aclweb org anthology 2020 emnlp main 62 pdf emnlp 2020 blindfold baselines for embodied qa https arxiv org abs 1811 05013 nips 2018 visually grounded interaction and language workshop analyzing the behavior of visual question answering models https arxiv org abs 1606 07356 emnlp 2016 knowledge graphs and knowledge bases mmkg multi modal knowledge graphs https arxiv org abs 1903 05485 eswc 2019 answering visual relational queries in web extracted knowledge graphs https arxiv org abs 1709 02314 akbc 2019 embedding multimodal relational data for knowledge base completion https arxiv org abs 1809 01341 emnlp 2018 a multimodal translation based approach for knowledge graph representation learning https www aclweb org anthology s18 2027 sem 2018 code https github com ukplab starsem18 multimodalkb order embeddings of images and language https arxiv org abs 1511 06361 iclr 2016 code https github com ivendrov order embedding building a large scale multimodal knowledge base system for answering visual queries https arxiv org abs 1507 05670 arxiv 2015 intepretable learning multimodal explanations by predicting counterfactuality in videos https arxiv org abs 1812 01263 cvpr 2019 multimodal explanations justifying decisions and pointing to the evidence https arxiv org abs 1802 08129 cvpr 2018 code https github com seth park multimodalexplanations do explanations make vqa models more predictable to a human https arxiv org abs 1810 12366 emnlp 2018 towards transparent ai systems interpreting visual question answering models https arxiv org abs 1608 08974 icml workshop on visualization for deep learning 2016 generative learning mmvae enhancing the generative quality of multimodal vaes without compromises https openreview net forum id sdqgxouelx iclr 2023 code https github com epalu mmvaeplus on the limitations of multimodal vaes https arxiv org abs 2110 04121 iclr 2022 code https openreview net attachment id w cpuxxraj name supplementary material generalized multimodal elbo https openreview net forum id 5y21v0rdbv iclr 2021 code https github com thomassutter mopoe multimodal generative learning utilizing jensen shannon divergence https arxiv org abs 2006 08242 neurips 2020 code https github com thomassutter mmjsd self supervised disentanglement of modality specific and shared factors improves multimodal generative models https rdcu be c8wuu gcpr 2020 code https github com imantdaunhawer dmvae variational mixture of experts autoencodersfor multi modal deep generative models https arxiv org pdf 1911 03393 pdf neurips 2019 code https github com iffsid mmvae few shot video to video synthesis https arxiv org abs 1910 12713 neurips 2019 code https nvlabs github io few shot vid2vid multimodal generative models for scalable weakly supervised learning https arxiv org abs 1802 05335 neurips 2018 code1 https github com mhw32 multimodal vae public code2 https github com panpan2 multimodal variational autoencoder the multi entity variational autoencoder http charlienash github io assets docs mevae2017 pdf neurips 2017 semi supervised learning semi supervised vision language mapping via variational learning https ieeexplore ieee org document 7989160 icra 2017 semi supervised multimodal hashing https arxiv org abs 1712 03404 arxiv 2017 semi supervised multimodal deep learning for rgb d object recognition https www ijcai org proceedings 16 papers 473 pdf ijcai 2016 multimodal semi supervised learning for image classification https ieeexplore ieee org abstract document 5540120 cvpr 2010 self supervised learning dabs a domain agnostic benchmark for self supervised learning https arxiv org abs 2111 12062 neurips 2021 datasets benchmarks track code https github com alextamkin dabs self supervised learning by cross modal audio video clustering https arxiv org abs 1911 12667 neurips 2020 code https github com humamalwassel xdc self supervised multimodal versatile networks https arxiv org abs 2006 16228 neurips 2020 code https tfhub dev deepmind mmv s3d 1 labelling unlabelled videos from scratch with multi modal self supervision https arxiv org abs 2006 13662 neurips 2020 code https www robots ox ac uk vgg research selavi self supervised learning of visual features through embedding images into text topic spaces https ieeexplore ieee org document 8099701 cvpr 2017 multimodal dynamics self supervised learning in perceptual and motor systems https dl acm org citation cfm id 1269207 2016 language models neural language modeling with visual features https arxiv org abs 1903 02930 arxiv 2019 learning multi modal word representation grounded in visual context https arxiv org abs 1711 03483 aaai 2018 visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes https arxiv org abs 1511 07067 cvpr 2016 unifying visual semantic embeddings with multimodal neural language models http proceedings mlr press v32 kiros14 html icml 2014 code https github com ryankiros visual semantic embedding adversarial attacks attend and attack attention guided adversarial attacks on visual question answering models https nips2018vigil github io static papers accepted 33 pdf neurips workshop on visually grounded interaction and language 2018 attacking visual language grounding with adversarial examples a case study on neural image captioning https arxiv org abs 1712 02051 acl 2018 code https github com huanzhang12 imagecaptioningattack fooling vision and language models despite localization and attention mechanism https arxiv org abs 1709 08693 cvpr 2018 few shot learning language to network conditional parameter adaptation with natural language descriptions https www aclweb org anthology 2020 acl main 625 acl 2020 shaping visual representations with language for few shot classification https arxiv org abs 1911 02683 acl 2020 zero shot learning the good the bad and the ugly https arxiv org abs 1703 04394 cvpr 2017 zero shot learning through cross modal transfer https nlp stanford edu socherr socherganjoomanningng nips2013 pdf nips 2013 bias and fairness worst of both worlds biases compound in pre trained vision and language models https arxiv org abs 2104 08666 arxiv 2021 towards debiasing sentence representations https arxiv org abs 2007 08100 acl 2020 code https github com pliang279 sent debias faircvtest demo understanding bias in multimodal learning with a testbed in fair automatic recruitment https arxiv org abs 2009 07025 icmi 2020 code https github com bidalab faircvtest model cards for model reporting https arxiv org abs 1810 03993 facct 2019 black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings https arxiv org abs 1904 04047 naacl 2019 code https github com tmanzini debiasmulticlasswordembedding gender shades intersectional accuracy disparities in commercial gender classification http proceedings mlr press v81 buolamwini18a html mod article inline facct 2018 datasheets for datasets https arxiv org abs 1803 09010 arxiv 2018 man is to computer programmer as woman is to homemaker debiasing word embeddings https arxiv org abs 1607 06520 neurips 2016 human in the loop learning human in the loop dialogue systems https sites google com view hlds 2020 home neurips 2020 workshop human and machine in the loop evaluation and learning strategies https hamlets workshop github io neurips 2020 workshop human centric dialog training via offline reinforcement learning https arxiv org abs 2010 05848 emnlp 2020 code https github com natashamjaques neural chat tree master batchrl human in the loop machine learning with intelligent multimodal interfaces https csjzhou github io homepage papers icml2017 syed pdf icml 2017 workshop architectures multimodal transformers pretrained transformers as universal computation engines https arxiv org abs 2103 05247 aaai 2022 perceiver general perception with iterative attention https arxiv org abs 2103 03206 icml 2021 flava a foundational language and vision alignment model https arxiv org abs 2112 04482 arxiv 2021 polyvit co training vision transformers on images videos and audio https arxiv org abs 2111 12993 arxiv 2021 vatt transformers for multimodal self supervised learning from raw video audio and text https arxiv org abs 2104 11178 neurips 2021 code https github com google research google research tree master vatt parameter efficient multimodal transformers for video representation learning https arxiv org abs 2012 04124 iclr 2021 code https github com sangho vision avbert multimodal memory multimodal transformer with variable length memory for vision and language navigation https arxiv org abs 2111 05759 arxiv 2021 history aware multimodal transformer for vision and language navigation https arxiv org abs 2110 13309 neurips 2021 code https cshizhe github io projects vln hamt html episodic memory in lifelong language learning https arxiv org abs 1906 01076 neurips 2019 icon interactive conversational memory network for multimodal emotion detection https aclanthology 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how2 challenge new tasks for vision language https srvk github io how2 challenge icml 2019 visual question answering and dialog https visualqa org workshop html cvpr 2019 cvpr 2017 multi modal learning from videos https sites google com view mmlv home cvpr 2019 multimodal learning and applications workshop https mula workshop github io cvpr 2019 eccv 2018 habitat embodied agents challenge and workshop https aihabitat org workshop cvpr 2019 closing the loop between vision and language lsmd challenge https sites google com site iccv19clvllsmdc iccv 2019 multi modal video analysis and moments in time challenge https sites google com view multimodalvideo iccv 2019 cross modal learning in real world https cromol github io iccv 2019 spatial language understanding and grounded communication for robotics https splu robonlp github io naacl 2019 youtube 8m large scale video understanding https research google com youtube8m workshop2018 iccv 2019 eccv 2018 cvpr 2017 language and vision workshop http languageandvision com cvpr 2019 cvpr 2018 cvpr 2017 cvpr 2015 sight and sound http sightsound org cvpr 2019 cvpr 2018 the large scale movie description challenge lsmdc https sites google com site describingmovies iccv 2019 iccv 2017 wordplay reinforcement and language learning in text based games https www wordplay2018 com neurips 2018 interpretability and robustness in audio speech and language https irasl gitlab io neurips 2018 multimodal robot perception https natanaso github io rcw icra18 icra 2018 wmt18 shared task on multimodal machine translation http www statmt org wmt18 multimodal task html emnlp 2018 shortcomings in vision and language https sites google com view sivl eccv 2018 computational approaches to subjectivity sentiment and social media analysis https wt public emm4u eu wassa2018 emnlp 2018 emnlp 2017 naacl hlt 2016 emnlp 2015 acl 2014 naacl hlt 2013 visual understanding across modalities http vuchallenge org cvpr 2017 international workshop on computer vision for audio visual media https cvavm2017 wordpress com iccv 2017 language grounding for robotics https robo nlp github io 2017 index html acl 2017 computer vision for audio visual media https cvavm2016 wordpress com eccv 2016 language and vision https vision cs hacettepe edu tr vl2016 acl 2016 emnlp 2015 tutorials tutorial on multimodal machine learning https cmu multicomp lab github io mmml tutorial icml2023 icml 2023 cvpr 2022 naacl 2022 recent advances in vision and language research https rohit497 github io recent advances in vision and language research cvpr 2020 connecting language and vision to actions https lvatutorial github io acl 2018 machine learning for clinicians advances for multi modal health data https www michaelchughes com mlhc2018 tutorial html mlhc 2018 multimodal machine learning https sites google com site multiml2016cvpr acl 2017 cvpr 2016 icmi 2016 vision and language bridging vision and language with deep learning https www microsoft com en us research publication vision language bridging vision language deep learning icip 2017 courses cmu 11 777 multimodal machine learning https cmu multicomp lab github io mmml course fall2022 cmu 11 877 advanced topics in multimodal machine learning https cmu multicomp lab github io adv mmml course spring2023 cmu 05 618 human ai interaction https haiicmu github io cmu 11 777 advanced multimodal machine learning https piazza com cmu fall2018 11777 resources stanford cs422 interactive and embodied learning http cs422interactive stanford edu cmu 16 785 integrated intelligence in robotics vision language and planning http www cs cmu edu jeanoh 16 785 cmu 10 808 language grounding to vision and control https katefvision github io languagegrounding cmu 11 775 large scale multimedia analysis https sites google com a is cs cmu edu lti speech classes 11 775 large scale multimedia analysis mit 6 882 embodied intelligence https phillipi github io 6 882 georgia tech cs 8803 vision and language http www prism gatech edu arjun9 cs8803 cvl fall17 virginia tech cs 6501 004 vision language http www cs virginia edu vicente vislang | multimodal-learning machine-learning representation-learning natural-language-processing computer-vision speech-processing robotics healthcare reading-list deep-learning reinforcement-learning | ai |
weather-on-the-web | weather on the web a public repository of the ogc metocean domain working group the purpose of weather on the web is to clarify and formalise the weather related standards landscape that enables the development and use of weather based web services to determine how weather information can best be integrated with other data on the web to define a range of access patterns and encoding extensions that cover a set of core use cases specified to identify and assess existing methods and tools to drive consensus on weather standards for the web and to produce a set of best practices for the use of weather information on the web weather on the web is solely focused on data apis as opposed to product apis in which specific business logic and understanding the context of use is required to add further value weather on the web will not consider use cases that require specific business knowledge of the end use weather meaning any meteorological information that covers past present and future states of the atmosphere including observations and climatological data encoding extensions for example specifying geojson structures for describing weather at a specific location deliverables the deliverables of this working group are described in the working group charter for convenience those deliverables are replicated in this section the charter remains the authoritative source of the definition of deliverables 1 use cases and requirements dd a set of documents setting out the range of problems that the sub group are trying to solve along with a set of requirements that meet the demands of these use cases dd 2 weather on the web apis engineering report dd a document that describes the experiences of various proof of concept and prototyping implementations outcomes of hackathons and recommendations for future work this document is in a separate repository https github com opengeospatial weather on the web er dd 3 weather on the web best practices dd a range of examples and descriptions of how to integrate weather information on the web dd 4 core specification dd a document describing the standard specification required to meet the needs of the use cases dd 5 business case dd a document outlining the benefits of weather on the web dd folder structure the files of the master branch are structured so that each deliverable has its own folder use cases https github com opengeospatial weather on the web tree master use cases dd the weather on the web use cases amp requirements refined and prioritised by the met ocean domain wg that guide the creation of the standard dd business case https github com opengeospatial weather on the web tree master business case dd this folder contains documents related to the business case for weather on the web describing the benefits of creating a standard and the types of problems we are trying to solve dd core specification https github com opengeospatial weather on the web tree master specification dd this folder contains the core specification of weather on the web standard it also contains the charter terms of reference to establish an ogc standards working group to formally standardise the weather on the web apis dd best practice https github com opengeospatial weather on the web tree master best 20practice dd this folder contains the files that comprise the best practice guide for application of the weather on the web apis to the meteorological domain dd design principles https github com opengeospatial weather on the web tree master design principles dd this folder contains the description of agreed design principles for weather on the web dd engineering https github com opengeospatial weather on the web tree master engineering dd this folder contains engineering and development related artifacts including example code and demonstrators dd contributing the contributor understands that any contributions if accepted by the ogc membership shall be incorporated into ogc weather on the web standards documents and that all copyright and intellectual property shall be vested to the ogc the met ocean domain working group is the group at ogc responsible for the stewardship of the standard but is working to do as much work in public as possible and it is envisaged that the weather on the web apis maybe applicable to generic data retrieval patterns in other domains open issues https github com opengeospatial weather on the web issues proposing changes https github com opengeospatial weather on the web wiki propose a change to a draft wow specification document join the discussion on our slack channel wotw slack https join slack com t weatherontheweb shared invite enqtndq1ntm1nta5nzazltzjmwm0njgxmwvkm2qynjvlnzzlmtg2zwqxotq3mzc5zdhlzjzknwfiywvhmtu4ztk3mgqymzy0mdk4njljnge | weather-api weather weather-data standards | front_end |
AWS-IoT | aws iot code for my udemy course on exploring aws iot www udemy com course exploring aws iot | server |
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Natural_Language_Processing_with_Transformers | natural language processing with transformers transformers hugging face natural language processing with transformers building language applications with hugging face lewis tunstall leandro von werra and thomas wolf hugging face transformer authors md aur lien g ron hands on machine learning with scikit learn and tensorflow znsoft 2022 transformers hugging face transformer pdf https github com hellotransformers natural language processing with transformers releases download 1 0 beta transformers pdf pdf github markdown znsoft 163 com daniel znsoft terminology md star pr fork github https github com hellotransformers natural language processing with transformers toc md image 20220214225553100 images readme image 20220214225553100 png transformer https datawhalechina github io learn nlp with transformers | transformers bert | ai |
01-mobile-first | cf https i imgur com 7v5asc8 png lab 01 mobile first code of conduct https github com codefellows code of conduct code wars challenge complete today s kata https www codewars com kata abbreviate a two word name and follow the submission instructions below code wars challenge submission instructions 1 with your assigned partner pseudocode your solution on the whiteboard take a picture of your proposed solution for your repo 1 complete the daily kata s 1 create a new branch for each challenge 1 make a new folder in your code wars repository on github the name of the folder should be the same as the name of the challenge 1 this folder should contain a file named solution js which contains the javascript solution after confirming that it passes on code wars with green passing tests a picture of your pseudocoded solution from the whiteboarding exercise a readme md which includes the problem domain from code wars and a link to the challenge 1 make a pull request from your working branch to your master branch 1 submit a link to your pr on canvas lab 01 welcome to your first lab assignment for code 301 today we ll be kicking off our blog app by applying what we learned in lecture to implement a mobile first design using responsive web design techniques you ll also need to spend some time getting familiar with the new git github pair programming workflow that we ll utilize throughout this course please take the time to read carefully through each of the readmes for lab assignments as they have detailed information regarding your assignment such as submission instructions resources configuration user stories with feature tasks and documentation lab 01 submission instructions when you are finished with lab follow these steps to submit your work create one pull request aka pr from your forked repo to the cf repo with your changes and you ll each submit that same pr link in canvas 1 be sure to remember to do all of your work on non master branches 1 ensure that all your local changes are committed and pushed to your origin repo 1 visit the origin repo on github com and ensure that all of your completed work has been merged to master via pull requests within your repo 1 create a new pr from your fork to the cf repo and ensure the branches look correct 1 fill in the template based on the check box prompts write a good descriptive summary of your changes be sure to include how much time you spent on it and who you worked with briefly reflect on and summarize your process 1 when you create the pr it will have a unique url copy this link share with your partner and paste it into the assignment submission form in canvas both the driver and the navigator will submit the same pr link set up your repo high level overview git workflow gitflow front png here is the recommended workflow 1 driver fork the cf lab repository if you haven t done so already 1 your forked repo on github is your origin repo 1 clone your fork to your local development environment follow this directory span id directory structure span codefellows 301 lab assignments 01 mobile first this is the cloned repository for today s work 1 immediately git checkout b drivername navigatorname ex git checkout b allie sam 1 create a copy of the starter code directory with the copy s name being the driver navigator ex cp r starter code allie sam and navigate into that directory ex cd allie sam write code together 1 driver in your terminal ensure that you are on the branch named after you and your partner you are currently within the partner pair directory 01 mobile first allie sam 1 open this directory as a project in your editor 1 use the find in project feature to locate all the todo review and comment items 1 before writing code do a read through of the existing code and discuss the review items with your partner 1 use live server in your terminal to render the code in the browser note that live server will automatically update the browser when code is changed and saved 1 work through one or two todo or comment items before switching roles or one hour whichever arrives first testing your code as you go 1 in your terminal type git status to view the files that you have changed you should only see the files that you have worked on 1 type git diff to see line item changes with your down arrow key type q to exit this mode 1 type git add file1 file2 where file1 file2 etc are the files that you have changed 1 type git status to view the files that have been added to your commit you should only see the files that you worked on 1 type git commit m some meaningful message where some meaningful message is a message that thoroughly explains your commit 1 type git status to ensure there is nothing left to commit 1 type git push origin drivername navigatorname to push this branch to your forked repo on github 1 on github add your navigator as a collaborator go to settings collaborators teams 1 once they have been added slack to your partner your forked repo link for them to clone down switch roles the navigator who will become the new driver clone the repo from the link your partner slacked you into your working directory see the above a href directory directory structure a for what you should have on your file system and navigate into the subdirectory named after you and your partner fetch the branch you were just working on git fetch origin drivername navigatorname switch to that branch git checkout b drivername navigatorname open the code in your editor and resume editing the code add commit push as you have done before resources a list of links if any are necessary for the assignment video mobile testing tip for your phone https www youtube com watch v 2t4e tc8tkm configuration your repository must include readme md with documentation regarding your lab and its current state of development check the documentation section below for more details on how that should look at minimum gitignore with standard nodejs configurations eslintrc json with code 301 course standards for the linter 01 mobile first eslintrc json gitignore license readme md index html styles base css fonts icomoon eot icomoon svg icomoon ttf icomoon woff icons css layout css modules css vendor styles normalize css user stories and feature tasks as a user i want to have an application that looks good is easy to use and is updated often so that i want to come back and use it frequently working from the provided comp images write css such that the browser rendering matches the images as closely as possible utilize the icon fonts located in styles icons css for navigation features as shown in the comp images as a developer i want to use standard industry practices for setting up and organizing the code that handles the styling of my application so that my code is easy to edit and maintain utilize a normalize css https github com necolas normalize css blob master normalize css file which should be placed in a vendor styles directory to override default browser settings that affect the way documents render utilize smacss practices to organize css into separate files and appropriately order the loading of those files in the head of the html document as a user i want a familiar experience of the application so that i know how to use it on my smartphone and occasionally my laptop initially design the application to render per the comp images on mobile devices set up the viewport and fluid media rules so content renders per the comp images in both mobile and desktop views use a breakpoint of 640 pixels add a hamburger menu button that reveals the nav links when tapped on a mobile device ensure that images are responsive and do not exceed the size of the viewport stretch goal include any additional stretch goals for this assignment which can vary depending on the class and their overall preparedness for additional work as a user i want a familiar experience when accessing the application on my tablet so that i can get the most out of the screen size set up an intermediate media query for tablet devices choose the maximum width at your own discretion documentation your readme md must include md project name author your name goes here version 1 0 0 increment the patch fix version number up if you make more commits past your first submission overview provide a high level overview of what this application is and why you are building it beyond the fact that it s an assignment for a code fellows 301 class i e what s your problem domain getting started what are the steps that a user must take in order to build this app on their own machine and get it running architecture provide a detailed description of the application design what technologies languages libraries etc you re using and any other relevant design information change log use this are to document the iterative changes made to your application as each feature is successfully implemented use time stamps here s an examples 01 01 2001 4 59pm application now has a fully functional express server with get and post routes for the book resource credits and collaborations give credit and a link to other people or resources that helped you build this application | front_end |
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Inventory | dms inventory manager build status https travis ci org dallas makerspace inventory svg branch master https travis ci org dallas makerspace inventory license https img shields io github license dallas makerspace inventory svg style flat square https github com dallas makerspace inventory blob master licence coverage status https coveralls io repos github dallas makerspace inventory badge svg branch master https coveralls io github dallas makerspace inventory branch master release https img shields io github tag dallas makerspace inventory svg style flat square https github com dallas makerspace inventory tags find a copy of the latest build at docker hub https hub docker com r dallasmakerspace inventory about this application the application was initially created by andrew lecody using the cakephp framework http www cakephp org cakephp the rapid development php framework and is released under the gnu affero general public license http www gnu org licenses agpl html installation installing this application is fairly easy just follow these steps 1 download the latest version with git git clone https github com dallas makerspace inventory git 2 in the app config directory rename core php default and database php default to core php and database php respectively 3 modify the core php and database php files to suit your environment 4 run docker build t dallas makerspace inventory latest docker stack deploy c docker compose yml dms inventory with the latest version of docker and docker with your docker host pointed to communitygrid dallasmakerspace org or localhost | cakephp inventory inventory-management php | os |
craftymetaverse | craftymetaverse craftymetaverse com front end source code | front_end |
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itNews | news the hyperlinks collection for information technology from reddit twitter fb tutorial blogs etc | server |
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althea-L1 | althea l1 banner svg althea l1 https www althea net is a layer one blockchain specifically for micropayment applications by dividing the chain between a highly efficient micropaymetns interface and a flexible programmable evm linking these two environments is liquid infrastructure a special account type represented by an nft that holds revenues from devices performing micropayments althea introduced our pay per forward protocol https www youtube com watch v g4ekbgshylw in 2018 a trustless way of coordinating payments for bandwidth and network routing that is radical in its simplicity and efficiency this is just one of the many usecases possible testnet 2 the next major event in the launch of the althea blockchain is testnet 2 which is online now you can find the testnet 2 documentation here docs althea testnet 2 md join the althea community join us on discord https discord com invite vw8twzr or matrix https riot im app room althea matrix org developer instructions build and test make make test | blockchain |
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AddressTagger | address tagger this is an experiment to see if common part of speech tagging techniques can be translated to address parsing this is a work in progress | ai |
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STM32-From-Scratch | stm32 from scratch programming an stm32f103 microcontroller at the register level writing my own minimal hal and eventually a simple rtos the goal is to write low level drivers and eventually an rtos without the use of fancy libraries and ides for embedded development everything should be able to be done with just a unix machine make and the gnu arm embedded toolchain https developer arm com tools and software open source software developer tools gnu toolchain gnu rm directories 00 blinky blink program simple code to make on board led blink 01 blinky 2 blink program but improved code clean up and improved directory structure 02 blinky 3 blink program but more improved added separate header and implementation files for gpio and rcc registers 03 extern blinky blink program but with an external led on a0 started using openocd and gdb server for debugging 04 extern blinky 2 external blink program with more organized code and extends to all gpio pins 05 timer2 interrupt enabling and using timer 2 interrupt to blink an led while the main loop fires 06 general timer interrupt enabling and using all general purpose timers and timer interrupts 07 systick interrupt enabling and using systick interrupt and counter for improved timing delays 08 pwm using general purpose timers to generate a pwm signal started using objdump for disassembly also code cleanup and fixed issue with timing 09 scb adding system control block exception handlers 10 serial enabling and using usart serial communication 11 hal non exhaustive hardware abstraction layer 12 rtos minimal real time operating system kernel work in progress getting started install arm none eabi toolchain and openocd macos brew install arm gcc bin openocd | os |
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EmbeddedMT | embeddedmt embedded motion tracker using computer vision this project envisages generic efficient motion tracking based on computer vision algorithms for resource limited devices dependencies most dependencies are because of the diverse availability of capturing devices and video en and decoders available may be improved later for the image processing part wget unzip for retrieving third party dependencies gcc or clang scons cmake ffmpeg swscale minimal dependencies of ffmpeg avformat avcodec avutil swscale for linux v4l v1 v2 only for webcams that support v4l ximea and xine webcam support can be enabled in the opencv sconsbuilder settings for mac os x cocoa framework normally standard present ffmpeg can be replaced by gstreamer package minimal dependencies gstreamer gstapp gstpbutils gstriff gobject glib2 0 for gui python 3 3 4 recommend else change in demo demo macosx scipy numpy matplotlib for linux gtk2 minimal dependencies gtk x11 gdk x11 cairo gdk pixbuf 2 0 gobject 2 0 glib 2 0 for mac os x qtkit normally standard present all necessary packages are available in most linux package managers and macports names of the necessary packages in macports gcc 4 8 gcc48 it is recommended to install gcc using the command line tools part of xcode from the app store this way you will get the latest clang compiler python2 7 python27 python3 4 python34 ffmpeg ffmpeg gstreamer 1 0 gestreamer1 and gst ffmpeg scipy py34 scipy numpy py34 numpy matplotlib py34 matplotlib how to build run get3rdpartygit sh get3rdpartygit sh run demo first time the project will be built for linux cd demo demo linux sh for mac os x cd demo demo macosx sh how to use for localhost demo s run the demo as described in how to build alternatively or for non localhost usage use the python wrappers in wrappers use the h function for more info on each wrapper note for the best results it is recommended to set the exposure time of the capture device to manual contact contact bart verhagen tass be | ai |
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bibliogram | bibliogram an alternative front end for instagram bibliogram works without client side javascript has no ads or tracking and doesn t urge you to sign up see also invidious a front end for youtube https github com omarroth invidious features x view profile and timeline x infinite scroll x user memory cache x rss latest 12 posts x view post x galleries videos galleries of videos image disk cache clickable usernames and hashtags homepage proper error checking optimised for mobile favicon settings e g data saving list view igtv public api rate limiting explore hashtags explore locations more these features may not be able to be implemented for technical reasons stories these features will not be added unless you ask reallllly nicely comments tagging users instances there is currently no official bibliogram instance perflyst has hosted an instance on https bibliogram snopyta org example page https bibliogram snopyta org u instagram but this is not managed by me and i cannot verify its code if you only use one computer you can install bibliogram on that computer and then access the instance through localhost installing bibliogram depends on graphicsmagick for resizing thumbnails ubuntu apt install graphicsmagick 1 git clone https github com cloudrac3r bibliogram if you are using a fork be sure to actually install that fork instead 1 npm install 1 edit config js to suit your server environment 1 npm start bibliogram is now running on 0 0 0 0 10407 user facing endpoints u username load a user s profile and timeline u username rss xml get the rss feed for a user p shortcode load a post | front_end |
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fhnw-iot | iot engineering module iot https www fhnw ch de studium module 9280188 by tamberg https twitter com tamberg for fhnw https www fhnw ch slides and code examples 0 syllabus 00 readme md 1 introduction to the internet of things 01 readme md 2 microcontrollers sensors actuators 02 readme md 3 sending sensor data to iot platforms 03 readme md 4 internet protocols http and coap 04 readme md 5 local connectivity with bluetooth le 05 readme md 6 raspberry pi as a local iot gateway 06 readme md 7 messaging protocols and data formats 07 readme md 8 long range connectivity with lorawan 08 readme md 9 dashboards and apps for sensor data 09 readme md 10 rule based integration of iot devices 10 readme md 11 voice control for connected products 11 readme md 12 raspberry pi as an iot edge device 12 readme md 13 from prototype to connected product 13 readme md 14 assessment 14 readme md 15 demo day 15 readme md hardware this course is based on the following hardware https github com tamberg fhnw iot wiki hardware raspberry pi zero w wiki raspberry pi zero w feather huzzah esp8266 wiki feather huzzah esp8266 feather nrf52840 express wiki feather nrf52840 express featherwing rfm95w wiki featherwing rfm95w grove sensors wiki grove sensors actuators wiki grove actuators grove adapters wiki grove adapters various wiki various why see iot hardware for cs bachelor students http www tamberg org fhnw 2019 iothardwareforcsbachelorstudents pdf wiki for resources check the iot engineering wiki https github com tamberg fhnw iot wiki books https github com tamberg fhnw iot wiki iot books hardware https github com tamberg fhnw iot wiki hardware iot platforms https github com tamberg fhnw iot wiki iot platforms development tools https github com tamberg fhnw iot wiki development tools git on your computer clone this repository pre git clone https github com tamberg fhnw iot pre update your local copy before each lesson pre cd fhnw iot git pull pre license unless noted otherwise source code examples in this repository are declared public domain cc0 1 0 https creativecommons org publicdomain zero 1 0 slides by tamberg https twitter com tamberg are licensed under creative commons cc by sa 4 0 https creativecommons org licenses by sa 4 0 publishing your own code choose an open source license https choosealicense com e g the simple mit license https choosealicense com licenses mit support iot engineering on ms teams | fhnw iot | server |
sem | workflow https github com oziomaeunice sem actions workflows main yml badge svg master branch github workflow status branch https img shields io github workflow status oziomaeunice sem a 20workflow 20for 20my 20hello 20world 20app master develop branch github workflow status branch https img shields io github workflow status oziomaeunice sem a 20workflow 20for 20my 20hello 20world 20app develop codecov codecov https codecov io gh oziomaeunice sem branch master graph badge svg token hn96yj11tk https codecov io gh oziomaeunice sem | server |
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stuckincyberspace | stuckincyberspace stuckincyber space website chelsea palmer s masters thesis power in the information age short essays on technology thru a primarily foucauldian lens | server |
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angular-bootstrap-responsive | responsive bootstrap directive with prefixed breakpoints use the predefined visibility helper classes working also with resize event demo http plnkr co edit yoo7ht | front_end |
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awesome-libraries-resources | awesome libraries and resources awesome libraries articles and resources for web development online tools online image optimizer http optimizilla com compress and optimize your images https compressor io resume https latexresu me architecture simplifying your microservices architecture with kafka and kafkastreams https dev to danlebrero simplifying your microservices architecture with kafka and kafkastreams design patterns for microservices https azure microsoft com en us blog design patterns for microservices js and css simply beautiful open source icons https feather netlify com simple tooltips made of pure css https kazzkiq github io balloon css a functional and reactive javascript framework for predictable code https cycle js org a collection of repeatable svg background patterns for you to use on your web projects http www heropatterns com simple grid system http simplegrid io a js library that makes it super easy to add fullscreen background videos https rishabhp github io bideo js js animate engine http animejs com webix https webix com utm expid 131619688 7 pqhnhhwzrm6xw64tzue6zw 0 utm referrer http 3a 2f 2fwebix com 2fwidgets 2f slider https vadimbogomazov github io crystalslider modal windows http madscript com boron burger minimal fullscreen nav https codepen io mblode pen qegwwb a fly out circle menu built with css https github com callmenick css circle menu simple elegant content placeholder https chalarangelo github io mocka t s c r o l l http t scroll com gooey menu https codepen io lbebber pen rawqkr simple modern richtext editor https dphans github io editor demo sun editor http suneditor com sample html index html lightweight awesome vanilla js image cropper https jamesssooi github io croppr js a tool for making javascript code run faster https prepack io i need js lib http microjs com a pure css responsive navigation menu https github com micjamking navigataur awesome buttons https bttn surge sh date formats http stackoverflow com questions 10211145 getting current date and time in javascript a javascript library to make navigation menus activate the item based on currently in view section https leocs me menuspy opensource projects from google https opensource google com projects list featured pure css responsive timeline mockup https flouthoc github io timenil 100 free resources for learning full stack web development https github com bmorelli25 become a full stack web developer blob master readme md statically typed programming language for the jvm android and the browser http kotlinlang org animate on scroll library https michalsnik github io aos a tiny async loader dependency manager for modern browsers https github com muicss loadjs css minifier with structural optimizations https github com css csso cross browser style inputs https codepen io jestho pen wbrjay menu https balzss github io luxbar cross browser style inputs http stackoverflow com questions 19145504 style a checkbox in firefox remove check and border a small strongly typed programming language that compiles to javascript http www purescript org cross browser style inputs https www everythingfrontend com posts cross browser css3 image free custom checkbox html datepicker https crsten github io datepickk building a morphing hamburger menu with css https scotch io tutorials building a morphing hamburger menu with css mobile online emulator http www mobilephoneemulator com fela is a fast and modular library to handle styling in javascript it is dynamic by design and renders your styles depending on your application state https github com rofrischmann fela iphone resolution http www kylejlarson com blog iphone 6 screen size web design tips apple products resolution http iosres com lightweight and simple carousel with no dependencies https pawelgrzybek com siema simple browser dialogs https alertifyjs org a simple and beautiful wiki for teams http matterwiki com ui components for elasticsearch http searchkit co a browser extension for displaying responsive web pages in a side by side views to quickly test how it looks at different resolutions and devices http re view emmet io from pixel to rem https quayzar com wordpress pixels to rem calculator material design lite https getmdl io index html the 100 correct way to do css breakpoints https medium freecodecamp com the 100 correct way to do css breakpoints 88d6a5ba1862 tmafddf9r the javascript json based survey library http surveyjs org control responsive http ami responsivedesign is url http starterkit meteor com control responsive and browser testing https browsersync io frontends sources https uptodate frontendrescue org lightweight and simple carousel with no dependencies https pawelgrzybek com siema custom scrollbars http grsmto github io simplebar a touch slideout navigation menu for your mobile web apps https slideout js org the fast and capable dropdown library http github hubspot com drop docs welcome simple lightbox script without dependencies not even jquery https github com dxf5h lightbox color generator https coolors co generate hashes from javascript objects in node and the browser https github com puleos object hash datetime picker https chmln github io flatpickr datetime picker http www dematte at tinydatepicker range https rangetouch com css edits and image changes apply live coffeescript sass less and others just work http livereload com modern css to all browsers http stylecow github io expressive dynamic robust css http stylus lang com presentation attributes vs inline styles https css tricks com presentation attributes vs inline styles a lightweight 2kb and dependency free javascript plugin for particle backgrounds https marcbruederlin github io particles js a tiny polyfill for html5 multi handle sliders http leaverou github io multirange lightweight validation using form element attributes http skyefradd me attrvalidate css styling of select element without js and using inline svg https github com carloscabo pure css select style a simple accessible html5 media player https plyr io styleable select elements http github hubspot com select docs welcome underscore is a javascript library that provides a whole mess of useful functional programming helpers without extending any built in objects http underscorejs org spectacular test runner for javascript http karma runner github io 1 0 index html animations http dynamicsjs com presentaion micro framework http markdalgleish com projects bespoke js a javascript library for google analytics http boba space150 com the javascript svg library for the modern web http snapsvg io parallax scrolling for the masses http prinzhorn github io skrollr vivus bringing your svgs to life http maxwellito github io vivus waypoints is the easiest way to trigger a function when you scroll to an element http imakewebthings com waypoints a general purpose web standards based platform for parsing and rendering pdfs http mozilla github io pdf js create beautiful fullscreen scrolling websites http alvarotrigo com fullpage firstpage offline js every app goes offline http github hubspot com offline docs welcome inputs http leaverou github io stretchy simple and elegant component based ui library http riotjs com single element css spinners https projects lukehaas me css loaders a grid framework based on the flex display property http timothylong com kindling a modular front end framework to easily and quickly help you jumpstart your process in building complex interfaces for the web right out the box http danmalarkey github io schema index html mobile friendly css framework http chuckcss io slide nav example https qmixi github io slide nav build a style guide straight from sass https css tricks com build style guide straight sass reducing css bundle size 70 by cutting the class names and using scope isolation https medium freecodecamp org reducing css bundle size 70 by cutting the class names and using scope isolation 625440de600b pants a fast scalable build system https www pantsbuild org browserify lets you require modules in the browser by bundling up all of your dependencies http browserify org a build tool for modern web development http mimosa io the static web server with built in preprocessing http harpjs com high performance webpack config for front end delivery https www codementor io drewpowers high performance webpack config for front end delivery 90sqic1qa utm content buffer61b28 utm medium social utm source twitter com utm campaign buffer a webpack built elastic beanstalk deployment leveraging aws s3 https medium com statuscode a webpack built elastic beanstalk deployment leveraging aws s3 as a cdn 54d62b851263 sonar a linting tool for the web https sonarwhal com graphql create graphql schema from typescript class https github com prismake typegql coursera s journey to graphql https dev blog apollodata com courseras journey to graphql a5ad3b77f39a moving existing api from rest to graphql https medium com raxwunter moving existing api from rest to graphql 205bab22c184 how to wrap a rest api in graphql https nordicapis com how to wrap a rest api in graphql from rest to graphql https 0x2a sh from rest to graphql b4e95e94c26b building a graphql api in rails part 2 start coding https medium com drawandcode building a graphql api in rails part start coding 8b1de6d75041 webpack build process etc webpack plugins etc https www npmjs com package webpack error with webpack 2 and mapbox https github com mapbox mapbox gl js issues 4348 error with webpack 2 and mapbox https github com mapbox mapbox gl js issues 4359 using google s closure library with webpack https medium com allycw using googles closure library with webpack b944e2a4be3a typescript quick start https www typescriptlang org docs tutorial html typescript tutorial https www tutorialspoint com typescript index htm getting started with typescript http blog teamtreehouse com getting started typescript getting started with typescript https code tutsplus com tutorials getting started with typescript net 28890 typescript tutorial https www youtube com watch v pr xqw9jju the repository for high quality typescript type definitions http definitelytyped org directory learn html learn typescript in 30 minutes http tutorialzine com 2016 07 learn typescript in 30 minutes typescript crash course https www youtube com watch v ray 3siqt e cz ms fest 2013 praha typescript kone n v s p estane javascript tv t tom herceg https www youtube com watch v fzsdeitgv6o writing a node js module in typescript https dev to dkundel writing a nodejs module in typescript building a node js app with typescript tutorial https medium com the node js collection building a node js app with typescript tutorial edbb3a874590 angular use react tools for better angular apps https medium com martin hotell use react tools for better angular apps b0f14f3f8114 handling data from server error http stackoverflow com questions 37230545 angular2 oninit values returned from service subscribe function does not ge building an angular application for production http blog mgechev com 2016 06 26 tree shaking angular2 production build rollup javascript connecting angular 2 to a phoenix api https medium com oxyrus connecting angular 2 to a phoenix api 75ede7f58bdf supercharge your angular2 event handling https github com kryops ng2 events readme web analytics for angularjs applications http angulartics github io lightweight drag to scroll directive for angular2 https github com bfwg angular2 drag scroll cool angular2 wrapper for localstorage and sessionstorage https github com hacklone angular2 cool storage readme how does angular teach you to be a better a software engineer https blog ngconsultant io learn angular software enginner patterns architecture 4836ef304b40 angular 2 slide show https github com marek1 angular 2 slide show responsive carousel https embed plnkr co vklszgqjtobyvrubj3op carousel https embed plnkr co sce0uqfd0zvl6t9j1xjl page slider https www npmjs com package ng2 page slider angular 2 module that implements loaders css spinners https github com moff angular2 loaders css how to implement stateless authentication http youknowriad github io angular2 cookbooks stateless authentication html persisting user authentication with behaviorsubject in angular https netbasal com angular 2 persist your login status with behaviorsubject 45da9ec43243 authentication to an angular 2 https github com auth0 blog angular2 authentication sample simple authentication http 4dev tech 2016 03 login screen and authentication with angular2 protecting routes using guards in angular https blog thoughtram io angular 2016 07 18 guards in angular 2 html angular 2 and ruby on rails user authentication https medium com avatsaev angular 2 and ruby on rails user authentication fde230ddaed8 nf9zw23p6 hash routing https github com angular angular issues 6595 setup angular and dotnet http blog stevensanderson com 2016 05 02 angular2 react knockout apps on aspnet core child routing http onehungrymind com named router outlets in angular 2 deploy https medium com ryanchenkie 40935 angular cli deployment host your angular 2 app on heroku 3f266f13f352 b71z1zjr8 animations https blog thoughtram io angular 2016 09 16 angular 2 animation important concepts html animations https www npmjs com package ng2 page transition animations https angular io docs ts latest guide animations html animations https github com bbottema ng2 simple page scroll animations https npmdaily com pkg ng2 scrollimate angular cli commands https www sitepoint com ultimate angular cli reference routing https scotch io tutorials routing angular 2 single page apps with the component router resources https github com angularclass awesome angular2 ecosystem http slides com magyarsrac universal angular intro 1 1 angular cli complete guide https www sitepoint com ultimate angular cli reference getting started with angular 2 using typescript https www sitepoint com getting started with angular 2 using typescript create angular 2 page https houssein me angular2 hacker news introduction to the angular 2 cli tutorial for beginners https www youtube com watch v wppbd 52lt0 simple angular 2 app with angular cli https www youtube com watch v qmqbaotljx8 angular 2 cli command line interface introduction https medium com codingthesmartway com blog angular 2 cli command line interface introduction 841e3e8092bf itup92wc4 how to create angular 2 application using angular 2 cli http tektutorialshub com create first angular 2 application angular 2 tutorial create a crud app with angular cli https www sitepoint com angular 2 tutorial use the angular cli for faster angular 2 projects https scotch io tutorials use the angular cli for faster angular 2 projects popup http plnkr co edit fe0kgemfbtmqoy31b4tw p preview ngif and click http jilles me ng click and ng if in angular2 ngif http lishman io angular 2 built in directives ui library http www primefaces org primeng angular and sass http www angulartypescript com angular 2 sass angular 2 ui framework http www angulartypescript com angular 2 ui framework angular 2 starter kit http angular2starterkit com angular material https material angular io learn by example angularjs nodejs and typescript http www software architects com devblog 2014 06 04 learn by example angularjs nodejs and typescript changing route doesn t scroll to top in the new page https github com angular angular issues 7791 the internationalization i18n library for angular 2 https github com ngx translate core a loader for ngx translate that loads translations with http calls https github com ngx translate http loader translate app example https plnkr co edit jexgj8wcisygtdg75vor p preview angular architecture patterns https medium com netmedia angular architecture patterns detailed project architecture 27bda0334f16 angular model pattern https medium com tomastrajan model pattern for angular state management 6cb4f0bfed87 the guide to building quality angular 2 components https blog wishtack com 2017 05 05 the guide to building quality angular 2 components ngupgrade with closure compiler http www syntaxsuccess com viewarticle ngupgrade with closure compiler exploring the new meta service in angular version 4 https netbasal com exploring the new meta service in angular version 4 b5ba2403d3e6 angular jest wallabyjs why it is the ideal combination and how to configure https blog cloudboost io angular jest wallabyjs why it is the ideal combination and how to configure b4cbe2eff4b3 testing angular 2 and continuous integration with jest https semaphoreci com community tutorials testing angular 2 and continuous integration with jest unit testing angular applications with jest https izifortune com unit testing angular applications with jest how to make your angular 4 app seo friendly https blog pusher com make angular 4 app seo friendly server side rendering in angular with angular universal https www dunebook com server side rendering angular universal ruby and ruby on rails error with capybara gem https github com thoughtbot capybara webkit wiki installing qt and compiling capybara webkit os x mavericks 109 and mountain lion 108 ember js and rails api https emberigniter com modern bridge ember and rails 5 with json api react and rails starter kit https github com spark solutions spark starter kit react and rails tutorials https hackhands com react rails tutorial building a json api with rails 5 https blog codeship com building a json api with rails 5 build a restful json api with rails 5 https scotch io tutorials build a restful json api with rails 5 part one building a restful api in a rails application https www airpair com ruby on rails posts building a restful api in a rails application 4 a basic rest api in rails find by sql http guides rubyonrails org active record querying html finding by sql setup actionmailer https sendgrid com docs integrate frameworks rubyonrails html how to send automated email in ruby on rails with mailgun https www leemunroe com send automated email ruby rails mailgun sending emails in rails applications https launchschool com blog handling emails in rails elixir and phoenix elixir lang http elixir lang org phoenix framework http www phoenixframework org build and test a blazing fast json api with phoenix an elixir framework https robots thoughtbot com testing a phoenix elixir json api elixir and phoenix the future of web apis and apps http blog carbonfive com 2016 04 19 elixir and phoenix the future of web apis and apps elixir school https elixirschool com phoenix 1 3 and graphql with absinthe https seanclayton me post phoenix 1 3 and graphql with absinthe utm content bufferbf422 utm medium social utm source twitter com utm campaign buffer introducing reactphoenix render react js components in phoenix views http geoffreylessel com 2017 introducing react phoenix utm content buffer8a8e0 utm medium social utm source twitter com utm campaign buffer serving a bare bones angular 2 app from phoenix https medium com zkayser serving a bare bones angular 2 app from phoenix d3f9d8ad52e5 using bootstrap and sass with phoenix framework and brunch https medium com b1ackmartian using bootstrap and sass with phoenix framework and brunch 6568e7a66ca9 getting started with rethinkdb and the phoenix framework introduction https medium com ryanswapp getting started with rethinkdb and the phoenix framework f55d767b9b07 building a rest api with phoenix and elixir https becoming functional com building a rest api with phoenix and elixir b12dcec302c5 jwt auth with an elixir on phoenix 1 3 api and react native https medium com njwest jwt auth with an elixir on phoenix 1 3 guardian api and react native mobile app 1bd00559ea51 crystal crystal lang https crystal lang org a collection of awesome crystal libraries tools frameworks and software https github com veelenga awesome crystal a web framework for crystal https razecr com lightning fast super simple web framework written in crystal http kemalcr com amber is a web application framework written in crystal inspired by kemal rails phoenix and other popular application frameworks http www amberframework org elm elm lang http elm lang org ui library for making web applications with elm http elm ui info elm a front end language with style https thoughtbot com upcase videos elm a front end language with style elm https egghead io technologies elm awesome elm https github com isruslan awesome elm elm tutorial https www elm tutorial org en elm webpack starter https github oldjpg com repository 48860215 from javascript to elm a blissful journey https kevgathuku me 2017 04 27 from javascript to elm a blissful journey integrating elm with rails https blog redpanthers co integrating elm rails reason reason https facebook github io reason react javascript development system http www getroc org setup react and dotnet http blog stevensanderson com 2016 05 02 angular2 react knockout apps on aspnet core react starter kit http www electrode io docs get started html create react app and the future of creating react applications https tylermcginnis com create react app and the future of creating react applications using create react app with hapi js https medium com notrab using create react app with hapi js 8f4ef3dcd311 getting started with create react app redux react router redux thunk https medium com notrab getting started with create react app redux react router redux thunk d6a19259f71f thunk middleware for redux https github com gaearon redux thunk react and express https medium com patriciolpezjuri using create react app with react router express js 8fa658bf892d eo1h7fw29 learn react redux with cabin http cabin getstream io show hide the contents of a password field https github com zakangelle react password mask transform plain text into dynamic blogs and websites using react js https github com gatsbyjs gatsby rich text editor framework for react https facebook github io draft js application architecture for building user interfaces http facebook github io flux a static type checker for javascript https flowtype org starhackit react redux node full stack starter kit with authentication and authorization data backed by sql http starhack it adding sass or scss to create react app https medium com connorelsea using sass with create react app 7125d6913760 optimizing react es6 webpack production build http moduscreate com optimizing react es6 webpack production build two quick ways to reduce react app s size in production https medium com rajaraodv two quick ways to reduce react apps size in production 82226605771a deploy next js to github pages https jaketrent com post deploy nextjs github pages next js github pages https medium com anotherplanet git tips next js github pages 2dbc9a819cb8 vue js introduction to vue https css tricks com intro to vue 1 rendering directives events a desktop ui library http element eleme io en us routes https forum vuejs org t there is no component problem with router view rendering with vue 2 0 1162 4 cms https pagekit com lightweight yet flexible model based vue validation http simple vue validator maijin info vue material datepicker https github com bastiengranger vue material datepicker vue instant allows you to easily create custom search controls with auto suggestions for your vue 2 application https santiblanko github io vue instant vue components https vuecomponents com vue and material design https vuematerial github io a lightweight collection of essential ui components written with vue js and inspired by material design https josephuspaye github io keen ui ref madewithvuejs ui alert augmenting a ruby on rails app with vue js https ksylvest com posts 2016 09 07 augmenting a ruby on rails app with vue js vue js and rails https rlafranchi github io 2016 03 09 vuejs and rails a simple asset pipeline wrapper for vue js by evan you https github com adambutler vuejs rails intro to vue js rendering directives and events https css tricks com intro to vue 1 rendering directives events a curated list of awesome things related to vue js https github com vuejs awesome vue simple lightweight model based validation for vue js 2 0 https monterail github io vuelidate | libs libraries js-library js-libs react-library angular-2 javascript-library css css-libraries vue | front_end |
homebrew-cv | homebrew cv computer vision formulas everything got merged into homebrew science to use this formulas brew tap homebrew science to install 1 7 2 use brew install pcl | ai |
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sundroid | sundroid v0 2 nightlie description this software is free mobile engine for arduino development platform written by sandro kalatozishvili and niko peikrishvili and licensed by gnu gpl 3 software is written for educational purposes and is distributed in the hope that it will be useful for anyone interested in this field functions make a call answer to call send message recieve message sdcard support todo add touchscreen support add message writting and number dialing system add camera support fix exiting unstable dial up function device picture alt tag http off sec com ftp sundroid jpg used libraries make sure you have suli arduino https github com seeed studio suli and softwareserial https github com arduino arduino tree master libraries softwareserial libraries in your arduino libraries folder scheduled to buy arduino 2 8 tft touch shield v2 0 http www amazon com arduino 2 8 touch shield v2 0 dp b00bozby7i ref sr 1 2 ie utf8 qid 1423599070 sr 8 2 used arduino shields gprs shield v2 http www amazon com seeedstudio arduino gprs shield v2 0 dp b00boz8k6q ref sr 1 1 ie utf8 qid 1423689140 sr 8 1 arduino ethernet shield r3 http www amazon com arduino rev3 ethernet shield r3 dp b006ut97fe ref sr 1 1 ie utf8 qid 1423689194 sr 8 1 sainsmart 1 8 tft color lcd http www amazon com sainsmart display interface microsd arduino dp b008hwtvq2 ref sr 1 2 ie utf8 qid 1424033080 sr 8 2 aov7670 300kp vga camera module http www amazon com huhushop tm ov7670 camera arduino dp b00gasb45g ref sr 1 1 ie utf8 qid 1423689539 sr 8 1 get more info from our blog offensice security georgia http off sec com http off sec com | front_end |
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CS-129.1-B-Kiu-Manlapaz-Perez-Young | cs 129 1 b kiu manlapaz perez young in partial fulfillment of requirements for the course cs 129 1 b special topics in software engineering contemporary database how to load the dataset 1 make sure that your command prompt is already directed to your bin directory in mongodb mongodb server 3 4 bin 2 folder creation mkdir mongo1 mkdir mongo2 mkdir mongo3 how to setup the replicate sets 1 run these codes in separate admin allowed command terminals make sure that your terminal s directory is in bin mongod replset replicate dbpath mongo1 port 27017 rest mongod replset replicate dbpath mongo2 port 27018 rest mongod replset replicate dbpath mongo3 port 27019 rest 2 open a new admin allowed command terminal make sure that your terimnal is in bin run this command mongo localhost 27017 3 once longged in var config id replicate version 1 members id 0 host localhost 27017 priority 1 id 1 host localhost 27018 priority 0 id 2 host localhost 27019 priority 0 4 afterwards rs intiate config rs conf 5 exit from your mongo login exit 6 import the data to your bin make sure that the file is in your bin folder before executing mongoimport db nba collection statistics type csv file stats csv h localhost 27017 headerline you can substitute nba with your database name 7 you can now check that the files have loaded into your mongo instance log in to the monggo mongo localhost 27017 in the mongo terminal show databases show dbs show collections show collections now you can start using your database use nba checks if data is running db statistics find pretty verify replication exit current mongo instance and log into a replicate mongo instance mongo localhost 27018 once in the replicated database set the current terminal as a slave rs slaveok log into your database use nba show databases show dbs show collections show collections checks if data is running db statistics find pretty how to execute the mapreduce functions 1 log into your primary server mongo localhost 27017 once logged into primary mongo instance use nba afterwards run the two mapreduce functions 1st mapreduce var map function emit tm this tm year this year fga this fga threepa this threepa var reduce function key values var total fga 0 var total 3pa 0 var threepa over fga 0 for var i 0 i values length i total fga values i fga total 3pa values i threepa threepa over fga total 3pa total fga return fga total fga threepa total 3pa behavior threepa over fga total 3pa total fga var results db runcommand mapreduce statistics map map reduce reduce out statistics report mapreduce report 1 db statistics report find pretty 2nd mapreduce var map function emit year this id year fga this value fga threepa this value threepa var reduce function key values var total fga 0 var total 3pa 0 var threepa over fga 0 for var i 0 i values length i total fga values i fga total 3pa values i threepa threepa over fga total 3pa total fga return fga total fga threepa total 3pa behavior threepa over fga var results db runcommand mapreduce statistics report map map reduce reduce out statistics report2 mapreduce report 2 db statistics report2 find pretty how export the output mongoexport db nba collection statistics report out statistics report csv mongoexport db nba collection statistics report2 out statistics report2 csv thank you kiu manlapaz perez young | server |
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reacTIVision | reactivision 1 6 2005 2022 by martin kaltenbrunner martin tuio org https github com mkalten reactivision https github com mkalten reactivision reactivision is an open source cross platform computer vision framework for the fast and robust tracking of fiducial markers attached onto physical objects as well as for multi touch finger tracking it was mainly designed as a toolkit for the rapid development of table based tangible user interfaces tui and multi touch interactive surfaces this framework has been developed by martin kaltenbrunner and ross bencina as part of the reactable project http www reactable com a tangible modular synthesizer reactivision is a standalone application which sends open sound control osc messages via a udp network socket to any connected client application it implements the tuio http www tuio org protocol which has been specially designed for transmitting the state of tangible objects and multi touch events from a tabletop surface the tuio http www tuio org framework includes a set of example client projects for various programming languages which serve as a base for the easy development of tangible user interface or multi touch applications example projects are available for languages such as c c c java processing https processing org pure data https puredata info max msp flash the reactivision application currently runs under the following operating systems windows mac os x and linux under windows it supports any camera with a proper wdm driver such as most usb firewire and dv cameras under mac os x most uvc compliant usb as well as firewire cameras should work under linux firewire cameras are as well supported as many video4linux2 compliant usb cameras 1 fiducial symbols fiducial symbols 2 finger tracking finger tracking 3 blob tracking blob tracking 4 application handling application handling 5 xml configuration file xml configuration file 6 calibration and distortion calibration and distortion 7 application cheatsheet application cheatsheet 8 compilation compilation fiducial symbols this application was designed to track specially designed fiducial markers you will find the default amoeba set of 216 fiducials in the document symbols default pdf print this document and attach the labels to any object you want to track this version also adds the new yamaarashi fiducials currently in beta which allow the detection of more than one million different symbols within a smaller and more uniform footprint reactivision detects the id position and rotation angle of fiducial markers in the realtime video and transmits these values to the client application via the tuio http www tuio org protocol tuio http www tuio org also assigns a session id to each object in the scene and transmits this session id along with the actual fiducial id this allows the identification and tracking of several objects with the same marker id finger tracking reactivision also allows multi touch finger tracking which is basically interpreting any round white region of a given size as a finger that is touching the surface finger tracking is turned off by default and can be enabled by pressing the f key and adjusting the average finger size which is given in pixels the general recognition sensitivity can also be adjusted where its value is given as a percentage 75 would be less sensitive and 125 more sensitive which means that also less probable regions are interpreted as a finger the finger tracking should work with di diffuse illumination as well as with ftir illumination setups blob tracking since version 1 6 reactivision also can track untagged objects sending the overall footprint size and orientation via the tuio http www tuio org blob profile you can activate this feature and configure the maximum blob size in reactivision xml where you can also choose to send optional blob messages for fingers and fiducials in order to receive information about their overall size these blobs will share the same session id with the respective finger or fiducial to allow the proper assignment application handling before starting the reactivision application make sure you have a supported camera connected to your system since the application obviously will not work at all without a camera the application will show a single window with the b w camera video in realtime pressing the s key will show to the original camera image pressing t will show the binary tresholded image pressing the n key will turn the display off which reduces the cpu usage the thresholder gradient gate can be adjusted by hitting the g key lowering the value can improve the thresholder performance in low light conditions with insufficient finger contrast for example you can gradually lower the value before noise appears in the image optionally you can also adjust the tile size of the thresholder the camera options can be adjusted by pressing the o key on windows and mac os this will show a system dialog that allows the adjustment of the available camera parameters on linux mac os x when using firewire cameras the available camera settings can be adjusted with a simple on screen display pressing the k key allows to browse and select all available cameras and image formats in order to produce some more verbose debugging output hitting the v will print the symbol and finger data to the console pressing the h key will display all these options on the screen f1 will toggle the full screen mode the p key pauses the image analysis completely hitting esc will quit the application xml configuration file common settings can be edited within the file reactivision xml where all changes are stored automatically when closing the application under mac os x this xml configuration file can be found within the application bundle s resources folder select show package contents from the application s context menu in order to access and edit the file the reactivision application usually sends tuio udp messages to port 3333 on localhost 127 0 0 1 alternative transport types are tuio tcp tcp tuio web websocket tuio flc flash local connection you can change this setting by editing or adding the xml tag tuio type udp host 127 0 0 1 port 3333 to the configuration multiple up to 32 simultaneous tuio transport options are allowed the according type options are udp tcp web and flc the fiducial amoeba default yamaarashi false xml tag lets you select the default or alternative small tiny amoeba fiducial set or you can point to an alternative amoeba trees file if available you can also additionally enable the new beta yamaarashi fiducal symbols for testing the display attribute defines the default screen upon startup the image display dest equalize false gradient 32 tile 10 lets you adjust the default gradient gate value and tile size reactivision comes with an image equalization module which in some cases can increase the recognition performance of both the finger and fiducial tracking within the running application you can toggle this with the e key or reset the equalizer by hitting the space bar the overall camera and image settings can be configured within the camera xml configuration file on mac os x this file is located in the resources folder within the application bundle you can select the camera id and specify its dimension and framerate as well as the most relevant image adjustments optionally you can also crop the raw camera frames to reduce the final image size please see the example options in the file for further information you can list all available cameras with the l startup option calibration and distortion many tables such as the reactable are using wide angle lenses to increase the area visible to the camera at a minimal distance since these lenses unfortunately distort the image reactivision can correct this distortion as well as the alignment of the image for the calibration print and place the provided calibration sheet on the table and adjust the grid points to the grid on the sheet to calibrate reactivision switch to calibration mode hitting c use the keys a d w x to move within grid moving with the cursor keys will adjust the position center point and size lateral points of the grid alignment by pressing q you can toggle into the precise calibration mode which allows you to adjust each individual grid point j resets the whole calibration grid u resets the selected point and l reverts to the saved grid to check if the distortion is working properly press r this will show the fully distorted live video image in the target window of course the distortion algorithm only corrects the found fiducial positions instead of the full image application cheatsheet key function g threshold gradient tile size configuration f finger size and sensitivity configuration b blob size configuration enable tuio 2dblb y enable disable yamaarashi symbol detection i configure tuio attribute inversion e turns image equalization on off space resets image equalization o camera configuration k camera selection c enter exit calibration mode q toggle quick precise calibration mode u resets selected calibration grid point j resets all calibration grid points l reverts calibration to saved grid s shows original camera image t shows binary thresholded image r shows calibrated camera image n turns display off saves cpu f1 toggles full screen mode p pauses all processing v verbose output to console h shows help options esc quits application compilation the source distribution includes projects for all three supported platforms and their respective build systems linux windows macos x windows a visual studio 2012 project as well as the necessary sdl2 libraries and headers are included the project should build right away for 32bit and 64bit targets without any additional configuration mac os x an xcode project for xcode version 9 3 or later is included the build will require the sdl2 and vvuvckit frameworks in order to compile properly just unzip the provided ext portvideo macosx frameworks zip into the directory ext portvideo macosx linux call make to build the application the distribution also includes configurations for the creation of rpm packages as well as a project file for the codeblocks ide make sure you have the libsdl 2 0 and libdc1394 2 0 or later as well as libjpeg turbo libraries and headers installed license this program 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 either version 2 of the license or at your option any later version this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details you should have received a copy of the gnu general public license along with this program if not write to the free software foundation inc 59 temple place suite 330 boston ma 02111 1307 usa | reactivision fiducial-markers camera tuio blob-tracking multi-touch fiducial-symbols cross-platform open-sound-control tuio-tracker computer-vision | ai |
web-development-notes | webdevelopment notes https github com shubhsharma19 web development notes assets 69891912 11b01e78 fd52 43da 99df ec6d81f89c91 h1 align left front end web development journey h1 html https github com shubhsharma19 web development notes tree main html css https github com shubhsharma19 web development notes tree main css javascript new added https github com shubhsharma19 web development notes tree main javascript git new added https github com shubhsharma19 web development notes tree main git json https github com shubhsharma19 web development notes tree main json yaml https github com shubhsharma19 web development notes tree main yaml apis https github com shubhsharma19 web development notes tree main apis | css html javascript git beginner-friendly goodfirstissue | front_end |
NLP-Models-Tensorflow | p align center a href readme img alt logo width 40 src nlp tf png a p p align center a href https github com huseinzol05 nlp models tensorflow blob master license img alt mit license src https img shields io badge license mit yellow svg a a href img src https img shields io badge total 20notebooks 335 models blue svg a p nlp models tensorflow gathers machine learning and tensorflow deep learning models for nlp problems code simplify inside jupyter notebooks 100 table of contents abstractive summarization abstractive summarization chatbot chatbot dependency parser dependency parser entity tagging entity tagging extractive summarization extractive summarization generator generator language detection language detection neural machine translation neural machine translation ocr ocr optical character recognition pos tagging pos tagging question answers question answers sentence pairs sentence pair speech to text speech to text spelling correction spelling correction squad question answers squad question answers stemming stemming text augmentation text augmentation text classification text classification text similarity text similarity text to speech text to speech topic generator topic generator topic modeling topic modeling unsupervised extractive summarization unsupervised extractive summarization vectorizer vectorizer old to young vocoder old to young vocoder visualization visualization attention attention objective original implementations are quite complex and not really beginner friendly so i tried to simplify most of it also there are tons of not yet release papers implementation so feel free to use it for your own research i will attached github repositories for models that i not implemented from scratch basically i copy paste and fix those code for deprecated issues tensorflow version tensorflow version 1 13 and above only not included 2 x version 1 13 tensorflow 2 0 bash pip install r requirements txt contents abstractive summarization abstractive summarization trained on india news abstractive summarization dataset accuracy based on 10 epochs only calculated using word positions details summary complete list 12 notebooks summary 1 lstm seq2seq using topic modelling test accuracy 13 22 2 lstm seq2seq luong attention using topic modelling test accuracy 12 39 3 lstm seq2seq beam decoder using topic modelling test accuracy 10 67 4 lstm bidirectional luong attention beam decoder using topic modelling test accuracy 8 29 5 pointer generator bahdanau https github com xueyouluo my seq2seq test accuracy 15 51 6 copynet test accuracy 11 15 7 pointer generator luong https github com xueyouluo my seq2seq test accuracy 16 51 8 dilated seq2seq test accuracy 10 88 9 dilated seq2seq self attention test accuracy 11 54 10 bert dilated cnn seq2seq test accuracy 13 5 11 self attention pointer generator test accuracy 4 34 12 dilated cnn seq2seq pointer generator test accuracy 5 57 details chatbot chatbot trained on cornell movie dialog corpus chatbot dataset tar gz accuracy table in chatbot chatbot details summary complete list 54 notebooks summary 1 basic cell seq2seq manual 2 lstm seq2seq manual 3 gru seq2seq manual 4 basic cell seq2seq api greedy 5 lstm seq2seq api greedy 6 gru seq2seq api greedy 7 basic cell bidirectional seq2seq manual 8 lstm bidirectional seq2seq manual 9 gru bidirectional seq2seq manual 10 basic cell bidirectional seq2seq api greedy 11 lstm bidirectional seq2seq api greedy 12 gru bidirectional seq2seq api greedy 13 basic cell seq2seq manual luong attention 14 lstm seq2seq manual luong attention 15 gru seq2seq manual luong attention 16 basic cell seq2seq manual bahdanau attention 17 lstm seq2seq manual bahdanau attention 18 gru seq2seq manual bahdanau attention 19 lstm bidirectional seq2seq manual luong attention 20 gru bidirectional seq2seq manual luong attention 21 lstm bidirectional seq2seq manual bahdanau attention 22 gru bidirectional seq2seq manual bahdanau attention 23 lstm bidirectional seq2seq manual backward bahdanau forward luong 24 gru bidirectional seq2seq manual backward bahdanau forward luong 25 lstm seq2seq api greedy luong attention 26 gru seq2seq api greedy luong attention 27 lstm seq2seq api greedy bahdanau attention 28 gru seq2seq api greedy bahdanau attention 29 lstm seq2seq api beam decoder 30 gru seq2seq api beam decoder 31 lstm bidirectional seq2seq api luong attention beam decoder 32 gru bidirectional seq2seq api luong attention beam decoder 33 lstm bidirectional seq2seq api backward bahdanau forward luong stack bahdanau luong attention beam decoder 34 gru bidirectional seq2seq api backward bahdanau forward luong stack bahdanau luong attention beam decoder 35 bytenet 36 lstm seq2seq tf estimator 37 capsule layers lstm seq2seq api greedy 38 capsule layers lstm seq2seq api luong attention beam decoder 39 lstm bidirectional seq2seq api backward bahdanau forward luong stack bahdanau luong attention beam decoder dropout l2 40 dnc seq2seq 41 lstm bidirectional seq2seq api luong monotic attention beam decoder 42 lstm bidirectional seq2seq api bahdanau monotic attention beam decoder 43 end to end memory network basic cell 44 end to end memory network lstm cell 45 attention is all you need 46 transformer xl 47 attention is all you need beam search 48 transformer xl lstm 49 gpt 2 lstm 50 cnn seq2seq 51 conv encoder lstm 52 tacotron greedy decoder 53 tacotron beam decoder 54 google nmt details dependency parser dependency parser trained on conll english dependency https github com universaldependencies ud english ewt train set to train dev and test sets to test stackpointer and biaffine attention originally from https github com xuezhemax neuronlp2 written in pytorch accuracy based on arc types and root accuracies after 15 epochs only details summary complete list 8 notebooks summary 1 bidirectional rnn crf biaffine arc accuracy 70 48 types accuracy 65 18 root accuracy 66 4 2 bidirectional rnn bahdanau crf biaffine arc accuracy 70 82 types accuracy 65 33 root accuracy 66 77 3 bidirectional rnn luong crf biaffine arc accuracy 71 22 types accuracy 65 73 root accuracy 67 23 4 bert base crf biaffine arc accuracy 64 30 types accuracy 62 89 root accuracy 74 19 5 bidirectional rnn biaffine attention cross entropy arc accuracy 72 42 types accuracy 63 53 root accuracy 68 51 6 bert base biaffine attention cross entropy arc accuracy 72 85 types accuracy 67 11 root accuracy 73 93 7 bidirectional rnn stackpointer arc accuracy 61 88 types accuracy 48 20 root accuracy 89 39 8 xlnet base biaffine attention cross entropy arc accuracy 74 41 types accuracy 71 37 root accuracy 73 17 details entity tagging entity tagging trained on conll ner https cogcomp org page resource view 81 details summary complete list 9 notebooks summary 1 bidirectional rnn crf test accuracy 96 2 bidirectional rnn luong attention crf test accuracy 93 3 bidirectional rnn bahdanau attention crf test accuracy 95 4 char ngrams bidirectional rnn bahdanau attention crf test accuracy 96 5 char ngrams bidirectional rnn bahdanau attention crf test accuracy 96 6 char ngrams residual network bahdanau attention crf test accuracy 69 7 char ngrams attention is you all need crf test accuracy 90 8 bert test accuracy 99 9 xlnet base test accuracy 99 details extractive summarization extractive summarization trained on cnn news dataset https cs nyu edu kcho dmqa accuracy based on rouge 2 details summary complete list 4 notebooks summary 1 lstm rnn test accuracy 16 13 2 dilated cnn test accuracy 15 54 3 multihead attention test accuracy 26 33 4 bert base details generator generator trained on shakespeare dataset generator shakespeare txt details summary complete list 15 notebooks summary 1 character wise rnn lstm 2 character wise rnn beam search 3 character wise rnn lstm embedding 4 word wise rnn lstm 5 word wise rnn lstm embedding 6 character wise seq2seq gru 7 word wise seq2seq gru 8 character wise rnn lstm bahdanau attention 9 character wise rnn lstm luong attention 10 word wise seq2seq gru beam 11 character wise seq2seq gru bahdanau attention 12 word wise seq2seq gru bahdanau attention 13 character wise dilated cnn beam search 14 transformer beam search 15 transformer xl beam search details language detection language detection trained on tatoeba dataset http downloads tatoeba org exports sentences tar bz2 details summary complete list 1 notebooks summary 1 fast text char n grams details neural machine translation neural machine translation trained on english french https github com tensorflow tensor2tensor blob master tensor2tensor data generators translate enfr py accuracy table in neural machine translation neural machine translation details summary complete list 53 notebooks summary 1 basic seq2seq 2 lstm seq2seq 3 gru seq2seq 4 basic seq2seq contrib greedy 5 lstm seq2seq contrib greedy 6 gru seq2seq contrib greedy 7 basic birnn seq2seq 8 lstm birnn seq2seq 9 gru birnn seq2seq 10 basic birnn seq2seq contrib greedy 11 lstm birnn seq2seq contrib greedy 12 gru birnn seq2seq contrib greedy 13 basic seq2seq luong 14 lstm seq2seq luong 15 gru seq2seq luong 16 basic seq2seq bahdanau 17 lstm seq2seq bahdanau 18 gru seq2seq bahdanau 19 basic birnn seq2seq bahdanau 20 lstm birnn seq2seq bahdanau 21 gru birnn seq2seq bahdanau 22 basic birnn seq2seq luong 23 lstm birnn seq2seq luong 24 gru birnn seq2seq luong 25 lstm seq2seq contrib greedy luong 26 gru seq2seq contrib greedy luong 27 lstm seq2seq contrib greedy bahdanau 28 gru seq2seq contrib greedy bahdanau 29 lstm seq2seq contrib beam luong 30 gru seq2seq contrib beam luong 31 lstm seq2seq contrib beam bahdanau 32 gru seq2seq contrib beam bahdanau 33 lstm birnn seq2seq contrib beam bahdanau 34 lstm birnn seq2seq contrib beam luong 35 gru birnn seq2seq contrib beam bahdanau 36 gru birnn seq2seq contrib beam luong 37 lstm birnn seq2seq contrib beam luongmonotonic 38 gru birnn seq2seq contrib beam luongmonotic 39 lstm birnn seq2seq contrib beam bahdanaumonotonic 40 gru birnn seq2seq contrib beam bahdanaumonotic 41 residual lstm seq2seq greedy luong 42 residual gru seq2seq greedy luong 43 residual lstm seq2seq greedy bahdanau 44 residual gru seq2seq greedy bahdanau 45 memory network lstm decoder greedy 46 google nmt 47 transformer encoder transformer decoder 48 transformer encoder lstm decoder greedy 49 bertmultilanguage encoder bertmultilanguage decoder 50 bertmultilanguage encoder lstm decoder 51 bertmultilanguage encoder transformer decoder 52 bertenglish encoder transformer decoder 53 transformer t2t 2gpu details ocr optical character recognition ocr details summary complete list 2 notebooks summary 1 cnn lstm rnn test accuracy 100 2 im2latex test accuracy 100 details pos tagging pos tagging trained on conll pos https cogcomp org page resource view 81 details summary complete list 8 notebooks summary 1 bidirectional rnn crf test accuracy 92 2 bidirectional rnn luong attention crf test accuracy 91 3 bidirectional rnn bahdanau attention crf test accuracy 91 4 char ngrams bidirectional rnn bahdanau attention crf test accuracy 91 5 char ngrams bidirectional rnn bahdanau attention crf test accuracy 91 6 char ngrams residual network bahdanau attention crf test accuracy 3 7 char ngrams attention is you all need crf test accuracy 89 8 bert test accuracy 99 details question answers question answer trained on babi dataset https research fb com downloads babi details summary complete list 4 notebooks summary 1 end to end memory network basic cell 2 end to end memory network gru cell 3 end to end memory network lstm cell 4 dynamic memory details sentence pair sentence pair trained on cornell movie dialogs corpus https people mpi sws org cristian cornell movie dialogs corpus html details summary complete list 1 notebooks summary 1 bert details speech to text speech to text trained on toronto speech dataset https tspace library utoronto ca handle 1807 24487 details summary complete list 11 notebooks summary 1 tacotron https github com kyubyong tacotron asr test accuracy 77 09 2 birnn lstm test accuracy 84 66 3 birnn seq2seq luong attention cross entropy test accuracy 87 86 4 birnn seq2seq bahdanau attention cross entropy test accuracy 89 28 5 birnn seq2seq bahdanau attention ctc test accuracy 86 35 6 birnn seq2seq luong attention ctc test accuracy 80 30 7 cnn rnn bahdanau attention test accuracy 80 23 8 dilated cnn rnn test accuracy 31 60 9 wavenet test accuracy 75 11 10 deep speech 2 test accuracy 81 40 11 wav2vec transfer learning birnn lstm test accuracy 83 24 details spelling correction spelling correction details summary complete list 4 notebooks summary 1 bert base 2 xlnet base 3 bert base fast 4 bert base accurate details squad question answers squad qa trained on squad dataset https rajpurkar github io squad explorer details summary complete list 1 notebooks summary 1 bert json exact match 77 57805108798486 f1 86 18327335287402 details stemming stemming trained on english lemmatization stemming lemmatization en txt details summary complete list 6 notebooks summary 1 lstm seq2seq beam 2 gru seq2seq beam 3 lstm birnn seq2seq beam 4 gru birnn seq2seq beam 5 dnc seq2seq greedy 6 birnn bahdanau copynet details text augmentation text augmentation details summary complete list 8 notebooks summary 1 pretrained glove 2 gru vae seq2seq beam tf probability 3 lstm vae seq2seq beam tf probability 4 gru vae seq2seq beam bahdanau attention tf probability 5 vae deterministic bahdanau attention https github com hareeshbahuleyan tf var attention 6 vae vae bahdanau attention https github com hareeshbahuleyan tf var attention 7 bert base nucleus sampling 8 xlnet base nucleus sampling details text classification text classification trained on english sentiment dataset text classification data accuracy table in text classification text classification details summary complete list 79 notebooks summary 1 basic cell rnn 2 basic cell rnn hinge 3 basic cell rnn huber 4 basic cell bidirectional rnn 5 basic cell bidirectional rnn hinge 6 basic cell bidirectional rnn huber 7 lstm cell rnn 8 lstm cell rnn hinge 9 lstm cell rnn huber 10 lstm cell bidirectional rnn 11 lstm cell bidirectional rnn huber 12 lstm cell rnn dropout l2 13 gru cell rnn 14 gru cell rnn hinge 15 gru cell rnn huber 16 gru cell bidirectional rnn 17 gru cell bidirectional rnn hinge 18 gru cell bidirectional rnn huber 19 lstm rnn conv2d 20 k max conv1d 21 lstm rnn conv1d highway 22 lstm rnn basic attention 23 lstm dilated rnn 24 layer norm lstm cell rnn 25 only attention neural network 26 multihead attention neural network 27 neural turing machine 28 lstm seq2seq 29 lstm seq2seq luong attention 30 lstm seq2seq bahdanau attention 31 lstm seq2seq beam decoder 32 lstm bidirectional seq2seq 33 pointer net 34 lstm cell rnn bahdanau attention 35 lstm cell rnn luong attention 36 lstm cell rnn stack bahdanau luong attention 37 lstm cell bidirectional rnn backward bahdanau forward luong 38 bytenet 39 fast slow lstm 40 siamese network 41 lstm seq2seq tf estimator 42 capsule layers rnn lstm 43 capsule layers lstm seq2seq 44 capsule layers lstm bidirectional seq2seq 45 nested lstm 46 lstm seq2seq highway 47 triplet loss lstm 48 dnc differentiable neural computer 49 convlstm 50 temporal convd net 51 batch all triplet loss lstm 52 fast text 53 gated convolution network 54 simple recurrent unit 55 lstm hierarchical attention network 56 bidirectional transformers 57 dynamic memory network 58 entity network 59 end to end memory network 60 bow chars deep sparse network 61 residual network using atrous cnn 62 residual network using atrous cnn bahdanau attention 63 deep pyramid cnn 64 transformer xl 65 transfer learning gpt 2 345m 66 quasi rnn 67 tacotron 68 slice gru 69 slice gru bahdanau 70 wavenet 71 transfer learning bert base 72 transfer learning xl net large 73 lstm birnn global max and average pooling 74 transfer learning bert base drop 6 layers 75 transfer learning bert large drop 12 layers 76 transfer learning xl net base 77 transfer learning albert 78 transfer learning electra base 79 transfer learning electra large details text similarity text similarity trained on mnli https cims nyu edu sbowman multinli details summary complete list 10 notebooks summary 1 birnn contrastive loss test accuracy 73 032 2 birnn cross entropy test accuracy 74 265 3 birnn circle loss test accuracy 75 857 4 birnn proxy loss test accuracy 48 37 5 bert base cross entropy test accuracy 91 123 6 bert base circle loss test accuracy 89 903 7 electra base cross entropy test accuracy 96 317 8 electra base circle loss test accuracy 95 603 9 xlnet base cross entropy test accuracy 93 998 10 xlnet base circle loss test accuracy 94 033 details text to speech text to speech trained on toronto speech dataset https tspace library utoronto ca handle 1807 24487 details summary complete list 8 notebooks summary 1 tacotron https github com kyubyong tacotron 2 cnn seq2seq dilated cnn vocoder 3 seq2seq bahdanau attention 4 seq2seq luong attention 5 dilated cnn monothonic attention dilated cnn vocoder 6 dilated cnn self attention dilated cnn vocoder 7 deep cnn monothonic attention dilated cnn vocoder 8 deep cnn self attention dilated cnn vocoder details topic generator topic generator trained on malaysia news https github com huseinzol05 malaya dataset raw master news news zip details summary complete list 4 notebooks summary 1 tat lstm 2 tav lstm 3 mta lstm 4 dilated cnn seq2seq details topic modeling topic model extracted from english sentiment dataset text classification data details summary complete list 3 notebooks summary 1 lda2vec 2 bert attention 3 xlnet attention details unsupervised extractive summarization unsupervised extractive summarization trained on random books extractive summarization books details summary complete list 3 notebooks summary 1 skip thought vector 2 residual network using atrous cnn 3 residual network using atrous cnn bahdanau attention details vectorizer vectorizer trained on english sentiment dataset text classification data details summary complete list 11 notebooks summary 1 word vector using cbow sample softmax 2 word vector using cbow noise contrastive estimation 3 word vector using skipgram sample softmax 4 word vector using skipgram noise contrastive estimation 5 supervised embedded 6 triplet loss lstm 7 lstm auto encoder 8 batch all triplet loss lstm 9 fast text 10 elmo bilm 11 triplet loss bert details visualization visualization details summary complete list 4 notebooks summary 1 attention heatmap on bahdanau attention 2 attention heatmap on luong attention 3 bert attention https github com hsm207 bert attn viz 4 xlnet attention details old to young vocoder vocoder trained on toronto speech dataset https tspace library utoronto ca handle 1807 24487 details summary complete list 1 notebooks summary 1 dilated cnn details attention attention details summary complete list 8 notebooks summary 1 bahdanau 2 luong 3 hierarchical 4 additive 5 soft 6 attention over attention 7 bahdanau api 8 luong api details not deep learning not deep learning 1 markov chatbot 2 decomposition summarization 3 notebooks | nlp machine-learning deep-learning lstm attention lstm-seq2seq-tf neural-machine-translation optical-character-recognition dnc-seq2seq pos-tagging summarization embedded luong-api chatbot speech-to-text language-detection | ai |
fees | assets fees 2018 jpg fsux http fsux me https haonancx gitbooks io fees content bug html css js html html html css css css javascript javascript javascript fsux outlook com https github com haonancx fees issues https github com haonancx fees issues github issues https www gitbook com book haonancx fees discussions https www gitbook com book haonancx fees discussions gitbook discussions | front_end |
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kinvo-front-end-test | logo kinvo https github com cbfranca kinvo front end test blob master logo svg desafio front end web seja bem vindo este desafio foi projetado para avaliar a capacidade t cnica de candidatos vagas de desenvolvedor front end voltadas para o desenvolvimento web independente da senioridade o n vel de exig ncia da avalia o se adequa ao n vel da vaga instru es 1 fa a um fork deste reposit rio 2 implemente o que proposto no prot tipo https github com kinvoapp kinvo front end test blob master material layout xd importante a implementa o dos gr ficos opcional para candidatos a vagas de n vel trainee est gio junior e pleno fa a o download adobexd por aqui https helpx adobe com br xd get started html 3 o conjunto m nimo de tecnologias a ser utilizado html css e js es6 4 crie um passo a passo de como rodar sua aplica o sugest o https github com elsewhencode project guidelines blob master readme sample md 5 ap s terminar submeta um pull request e aguarde a avalia o crit rios de avalia o nossos crit rios de avalia o se baseiam e 3 grandes reas sendo elas 1 versionamento 2 projeto e estrutura 3 qualidade de c digo requisitos m nimos trainee est gio permitir a filtragem de produtos na se o minhas rendas fixas a partir de buscas realizadas no campo de texto junior todos os requisitos exigidos para o n vel est gio trainee exibir dados reais obtidos a partir da api https 6270328d6a36d4d62c16327c mockapi io getfixedincomeclassdata permitir ordena o de produtos se o minhas rendas fixas a partir do menu seletor pleno todos os requisitos exigidos para o n vel junior fazer uso da biblioteca react https pt br reactjs org fazer uso da biblioteca styled components https styled components com paginar produtos 5 por p gina na se o minhas rendas fixas s nior analista todos os requisitos exigidos para o n vel pleno projetar arquitetura minimamente escal vel cobertura de testes utilizando o framework de sua prefer ncia jest https jestjs io e enzyme https enzymejs github io enzyme s o as nossas sugest es gr ficos funcionais utilizando a biblioteca de sua prefer ncia highcharts https www highcharts com a nossa sugest o notas importante o cumprimento dos requisitos solicitados para uma vaga em determinado n vel n o garantia de aprova o focamos em avaliar a forma como os requisitos foram cumpridos apesar da listagem de requisitos m nimos acima caso n o tenha tido tempo suficiente ou tenha se esbarrado em alguma dificuldade entregue o desafio ainda que incompleto e conte nos na descri o do pull request quais foram as suas maiores dificuldades n o se preocupe avaliaremos ainda assim o prot tipo disponibilizado no formato de arquivo adobe xd e est dispon vel em material material ou a partir deste link https xd adobe com view efae346e 370a 4a7a 9037 43510c4c8028 bafd caso n o tenha familiaridade com o adobe xd os ativos do prot tipo podem ser exportados utilizando o atalho ctrl e ou cmd e caso o seu sistema operacional n o seja compat vel com o software voc pode acessar o prot tipo tamb m atrav s do link citado no item anterior as bibliotecas e demais recursos sugeridos por n s em todos os n veis de exig ncia s o meras sugest es com exce o do react sinta se a vontade para fazer a escolha que te deixa mais confort vel e inclusive para sugerir lembre se de fazer um fork deste reposit rio apenas clon lo vai te impedir de criar o pull request e dificultar a entrega est com alguma dificuldade encontrou algum problema no desafio ou tem alguma sugest o pra gente crie uma issue https github com kinvoapp kinvo front end test issues e descreva o que achar necess rio sucesso | front_end |
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Neural-Network | neural network from scratch this code is part of my video series on youtube neural network from scratch mathematics python code https youtube com playlist list plq4osgq7wn6pgnvt6tzlavaemsl3lbqpm try it python3 xor py example python import numpy as np from dense import dense from activations import tanh from losses import mse mse prime from network import train x np reshape 0 0 0 1 1 0 1 1 4 2 1 y np reshape 0 1 1 0 4 1 1 network dense 2 3 tanh dense 3 1 tanh train network mse mse prime x y epochs 10000 learning rate 0 1 | ai |
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freelancing-app | outsourcd a python django api and react app for freelance workers to find jobs ga project four this app has been deployed on heroku here https outsourcd herokuapp com this is a full stack application built with django rest framework and react which i submitted as my final project for the general assembly software engineering immersive course the app enables users to register for an account as a freelancer find and secure posted freelance jobs and then manage progress in a dashboard users can complete specific tasks such as generating a pdf invoice for a particular job finally users can add and modify their profile information in their managed profile area alt text https res cloudinary com di7ndofao image upload v1647874131 habit tracker app screenshot 2022 03 21 at 14 41 16 ivx7xx png homepage brief solo project timeframe of 8 days build a full stack application by making backend and front end use a python django api using django rest framework to serve data from a postgresql database consume the api with a separate front end built with react be a complete product with multiple relationships and crud functionality implement thoughtful user stories wireframes have a visually impressive design be deployed and publicly accessible online write dry code and build restful apis technologies used django django rest framework python postgresql pyjwt javascript es6 react js html css sass material ui axios git github react router dom react pdf app and user journey walkthrough registration upon registering and logging in reg form transitions to log in form the user is required to add their profile information in a 3 step process moving between steps is only possible once all essential fields are complete and step 2 has skippable fields https user images githubusercontent com 96052888 163424232 f1e0ed06 82f4 45c8 98c4 f6d27b94b1ad mp4 job finder upon clicking current jobs in the responsive side drawer the user has the facility to find jobs can also be achieved via top nav users will see a list of available freelance jobs which can be filtered based on their sector by clicking a job the user can access the job detail page alt text https res cloudinary com di7ndofao image upload v1647874133 habit tracker app screenshot 2022 03 21 at 14 46 57 vue90o png find jobs job detail page alt text https res cloudinary com di7ndofao image upload v1647877280 habit tracker app screenshot 2022 03 21 at 14 53 01 txi4vf png job detail page users can also access the detail page for the company that has listed the job alt text https res cloudinary com di7ndofao image upload v1647877281 habit tracker app screenshot 2022 03 21 at 14 53 23 dcyytr png company page back on the job detail page users can sign up for a job by clicking app this opens a modal clicking submit will add the job to the user s current jobs section alt text https res cloudinary com di7ndofao image upload v1647875209 habit tracker app screenshot 2022 03 21 at 15 00 22 z4url7 png current jobs users can then access features that will be demonstrated in the logged in route below returning user after logging in the user returns to their profile about me section alt text https res cloudinary com di7ndofao image upload v1647874132 habit tracker app screenshot 2022 03 21 at 14 44 37 vqz15q png about me users can edit their about me text alt text https res cloudinary com di7ndofao image upload v1647874131 habit tracker app screenshot 2022 03 21 at 14 43 10 virzzq png edit about me they can also delete skills from their profile by clicking the x on the relavant skill chip alt text https res cloudinary com di7ndofao image upload v1647874131 habit tracker app screenshot 2022 03 21 at 14 43 27 vtyjym png delete skills by clicking add skills they see a modal which allows the user to searcg for skills if the skill exists it s populated below as a chip if it doesn t exist the user has the option to add it in either case the user can then add the skill which will appear on the profile as a new chip alt text https res cloudinary com di7ndofao image upload v1647874131 habit tracker app screenshot 2022 03 21 at 14 43 55 yp4iwl png add skills the user can also add work experience alt text https res cloudinary com di7ndofao image upload v1647874132 habit tracker app screenshot 2022 03 21 at 14 44 45 esjmmx png add experience the current jobs section shows any uncompleted jobs that the user has signed up for milestones of the job are automatically populated into a checklist alt text https res cloudinary com di7ndofao image upload v1647874132 habit tracker app screenshot 2022 03 21 at 14 45 11 shqrok png current jobs a calendar allows tracking of progress it automatically shows today s date and if the user hovers over a milestone the calendar will skip to that day alt text https res cloudinary com di7ndofao image upload v1647874132 habit tracker app screenshot 2022 03 21 at 14 45 19 y8evqi png calendar feature once all milestones are checked off the list two buttons appear one is a button to mark the job as complete when this is clicked the job moves from current jobs to job history alt text https res cloudinary com di7ndofao image upload v1647874132 habit tracker app screenshot 2022 03 21 at 14 45 33 f5zpbh png complete job the user can also generate a pdf invoice at this point data in the pdf is generated from the user and job information in the database alt text https res cloudinary com di7ndofao image upload v1647874132 habit tracker app screenshot 2022 03 21 at 14 45 50 zghyht png generate pdf planning process in the first stage of my design process i focussed on establishing how the application back end would look therefore i sketched out an entity relationship diagram for the database architecture alt text https res cloudinary com di7ndofao image upload v1649950730 habit tracker app screenshot 2022 04 12 122408 bzuh1e png erd after i was satisfied with my erd i then focussed my attention on the front end and created some wireframes of my uis on miro to indicate the design and functionality alt text https res cloudinary com di7ndofao image upload v1649950779 habit tracker app screenshot 2022 04 12 123319 fxcdzi png wireframe i then began the development process by building my models views and serialisers using django rest framework and integrating django with postgresql to create a database i dedicated around 2 full days to this phase as while i built i also spent time testing the endpoints using the insomnia rest client visualising my database using tableplus and running sql queries so i could better understand the relationships joins i was making this was crucial in determining that all relationships between models were correct and that i was getting the appropriate json responses once i was happy with the crud features of my back end i was then able to move on to the front end my aim was to have as few pages as possible and make components as re usable where possible in order to minimise code and to keep the components as clear and simple as i could i started by building the authorization components as the whole application is log in gated once these were done i was able to then focus on building other components in accordance with my priority list on trello this was a substantial job due to the number of different components i needed to build but i was able to work my way through progressively for data requests from the back end i used axios and tested that i was getting the expected data using console logs and the react dev tools i used react router dom for page navigation styling as i felt comfortable with boostrap and chakra from previous projects i wanted to push myself to learn another ui framework for this project given its popularity and applicability to this particular application i chose material ui while it was a challenge to learn another new framework and i had to do a lot of reading of the documentation and stack overflow this ultimately made my life a lot easier in the long run as it provided me with a number of pre built components which i was able to customise for my own purposes featured code user model and serialiser the most crucial part of setting up the back end was establishing the user model and the necessary relationships below you can see what the user model looked like and the corresponding populated serialiser i used for the view on my get requests python class user abstractuser first name models charfield max length 50 blank true last name models charfield max length 50 blank true email models charfield max length 50 unique true profile image models textfield max length 500 blank true is freelancer models booleanfield default false is client models booleanfield default false business name models charfield max length 100 blank true address models textfield max length 500 blank true city models textfield max length 100 blank true country models textfield max length 100 blank true postcode models textfield max length 7 blank true business website models charfield max length 100 blank true personal website models charfield max length 100 blank true linkedin url models charfield max length 100 blank true job title models charfield max length 100 blank true sector models manytomanyfield sectors sector related name users blank true about me models textfield max length 800 blank true skills models manytomanyfield skills skill related name users blank true applied jobs models manytomanyfield jobs job related name job applicants blank true python class populateduserserializer userserializer created jobs populatedjobserializer many true jobs populatedjobserializer many true skills skillserializer many true experience populatedexperienceserializer many true received reviews populatedreviewserializer many true given reviews populatedreviewserializer many true building calendar javascript useeffect let numofdaysinmonth if month february if year 4 0 year 100 0 year 400 0 numofdaysinmonth 29 else numofdaysinmonth 28 else if month april month june month september month november numofdaysinmonth 30 else numofdaysinmonth 31 setdaysinmonth numofdaysinmonth month year javascript const calendarbuild const arr for let i 0 i 42 i arr push box sx pl 8px mr 8px height 35px key i id i box let inc 1 for let i new date month 1 year getday i new date month 1 year getday daysinmonth i let iddate new date month inc year if iddate tolocaledatestring today tolocaledatestring arr i box id iddate key iddate sx backgroundcolor eceff1 pl 8px mr 8px height 35px inc box else if iddate tolocaledatestring new date hovereddate tolocaledatestring arr i box id iddate key iddate sx backgroundcolor d81b60 pl 8px mr 8px height 35px inc box else arr i box id iddate key iddate sx pl 8px mr 8px height 35px inc box inc iddate setdate iddate getdate 1 return arr calendarbuild updating profile searching for a skill to add to profile in the modal if a skill is found the search results are mapped to generate chip components javascript box mt 4 p 1 width 100 spacing 1 display flex flexwrap wrap justifycontent flex start searchresults length searchresults map result i chip onclick handlechipclick result name key i id result name label result name sx activeindices includes result name coloured uncoloured icon addicon box if a skill isn t found a button appears that has an event handler that runs the function to add the skill to the user s profile javascript skill found box alert severity warning skill message button onclick handleaddskill add button alert box javascript const handleaddskill const addskill async const token localstorage getitem outsourcd token const datatosend name skill name console log datatosend try await axios post api skills datatosend headers authorization bearer token setsearchresults skill setskill name found true message catch error seterror error true message error response data name 0 addskill altering current job status updating the milestones checklist javascript const updatedcompletedstatus async try await axios put api milestones milestone id milestone completed updatedcompletion headers authorization bearer token setchecklistupdated true catch err seterror error true message err message known bugs or errors when adding a new skill to a user profile that doesn t exist in the database after searching the post request is not functioning predictably key learnings going solo for the final project was a challenge compared to the previous project where we could progress more quickly and bounce ideas off each other solving issues together however it was hugely positive in giving me the confidence that i can build a full stack app on my own as all the challenges i faced and was able to overcome in this project helped boost my confidence as a developer i felt my planning was good and i made a concerted effort to make detailed database relationship diagrams and ui wireframes and to manage my workflows via trello nonetheless a big learning experience was appreciating the trade off between functionality and robust bug free code the latter was a priority for me in the project so i was perhaps over ambitious as my initial vision was to build a fully functional marketplace where a user could also register as a client and post jobs and hire freelancers however i kept my mvp as a job finder for freelancers and i am happy that i managed to build the fully functioning mvp in the timeframe git thanks to our collaborative git approach i saw a significant improvement in my understanding of git and my comfortability in using and merging different branches and versions of code and dealing with any merge conflicts my key learnings were understanding how branches work and the importance of understanding where you are in the version control tree at any given time django postgresql having only built a back end previously with node express learning python and a whole new back end framework was a challenge in the time i had available my main areas of difficulty were around populating my serialisers optimally in order to serve appropriate data to the front end and raising appropriate exceptions and handling errors on the backend however through plenty of further reading and testing with insomnia i was ultimately able to get the backend functioning stably and predictably to enable the crud i need for this app which was pleasing having previously worked with nosql databases only i wanted to gain a better understanding on whether i was capturing the data and relationships correctly therefore i spent some time running sql queries on my data in tableplus this was extremely useful in consolidating concepts like data joins and visualising how my django views were actually working from the database side because my database involved a number of different one to many and many to many relationships that were needed getting these relationships set up correctly was probably my biggest challenge on the backend but i learned a lot about postgres and django through the process react though i was beginning to feel more comfortable with react at the beginning of this project i still experienced plenty of improvement there were some new things for me a lot of which came from using material ui components for the first time but also i felt like some familiar concepts like event handlers controlled inputs and hooks were cemented through this project one thing that was new was the scale of the react work this app was quite simple in its overall architecture a few pages but there is a lot of conditional rendering that uses data from the backend and a lot of ui changes updates that occur as a result of user interaction as a result i had to think much harder about state management and ensure that state was being passed correctly to components in order to generate the appropriate ui one aspect that was particularly difficult was getting the calendar to skip to the correct date based on the user s hover of a checklist item however i was very pleased with how this turned out challenges wins my main struggles were also my two biggest victories firstly the quantity and breadth of work required on the front end was substantial and i definitely underestimated the work required to build lots of robust components even if the functionality did not seem overly complicated however i am particularly proud of the quantity of bug free code i managed to write in the timeframe and consequently the level of functionality i was able to build into this app there was no part of mvp left undeveloped or poorly styled at the end there were definitely times when i felt overwhelmed by what i still needed to do but a combination of clear and detailed planning solid workflow prioritisation management and extra self directed learning enabled me to get everything done as part of this i would say an additional win is that i learned a huge amount about a wide range of tools and i feel i am a much better developer as a result of this project the second main area was working with django and a nosql database i spent quite some time going back to my models and serialisers because i wasn t quite able to get or send the data i wanted i initially found errors very difficult to understand and debug however on the flip side i would say that learning django and getting this back end to work was my greatest win there were some moments where i felt like completely going back to scratch but i pushed on by staying calm reading django documentation and running sql queries on my database and ultimately i was able to understand how things were actually working and how to solve any issues future features as mentioned above my complete app vision includes the functionality for users to register as clients rather than freelancers and then search and secure freelancers and review and rate them the way this works is that the user model has a boolean field for client as well as freelancer which i don t currently use for anything on the front end however this will ultimately be used to conditionally render a client environment instead of the freelancer environment which is all you see currently users will be searchable and filterable and will be populated using a get request with a view that serves the relevant data of all users rather than a freelance user simply being able to add themselves to a job they will need to apply and the client can accept or decline them after reviewing their profile once this part of the app is built i would like to then focus on a chat function so that clients and freelancers can communicate | server |
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Udacity-HA-CloudFormation | high availability application using cloudformation introduction this is a cloud formation https kubernetes io setup to deploy an udagram for a highly available app as requirement to cloud devops nanodegre of udacity https www udacity com website link udagram is available under cloudfront http d9tyk1tu3q10q cloudfront net http d9tyk1tu3q10q cloudfront net architecture diagram alt text diagram udagram png | aws ec2 waf cloudformation udacity udagram | cloud |
Agri.FarmIT | agri farmit agricultural firm amp information technologies | server |
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Deep-Survey-Text-Classification | deep survey on text classification this is a survey on deep learning models for text classification and will be updated frequently with testing and evaluation on different datasets natural language processing tasks part of speech tagging chunking named entity recognition text classification etc has gone through tremendous amount of research over decades text classification has been the most competed nlp task in kaggle and other similar competitions count based models are being phased out with new deep learning models emerging almost every month this project is an attempt to survey most of the neural based models for text classification task models selected based on cnn and rnn are explained with code keras and tensorflow and block diagrams the models are evaluated on one of the kaggle competition medical dataset update non stop training and power issues in my geographic location burned my motherboard by the time i had to do 2 rmas with asrock and get the system up and running the competition was over but still i learned a lot project setup 1 download and install anaconda3 say at programs anaconda3 2 create a virtual environment using cd programs anaconda3 mkdir envs and cd envs bin conda create p programs anaconda3 envs dsotc c3 python 3 6 anaconda 3 do activate the environment source home bicepjai programs anaconda3 envs dsotc c3 bin activate dsotc c3 4 install programs anaconda3 envs dsotc c3 bin pip using conda install pip anaconda has issues with using pip so use the fill path 5 execute command pip install r requirements txt for installing all dependencies 6 for enabling jupyter extensions jupyter nbextensions configurator enable user 7 for enabling configuration options jupyter contrib nbextension install user 8 some extensions to enable collapsible headings executetime table of contents now we should be ready to run this project and perform reproducible research the details regarding the machine used for training can be found here https bicepjai github io machine learning 2015 05 25 machine learning rig html version reference on some important packages used 1 keras 2 0 8 2 tensorflow gpu 1 3 0 3 tensorflow tensorboard 0 1 8 data details regarding the data used can be found here https github com bicepjai deep survey text classification blob master data prep dataset readme md content this project is completed and the documentation can be found here https docs google com document d 1zah2lujwekr8o5ozkv 48nwmvw pvvy5o953a 9kcnm edit usp sharing the papers explored in this project 1 convolutional neural networks for sentence classification yoon kim 2014 https github com bicepjai deep survey text classification tree master deep models paper 01 cnn sent class 2 a convolutional neural network for modelling sentences nal kalchbrenner edward grefenstette phil blunsom 2014 https github com bicepjai deep survey text classification tree master deep models paper 02 cnn sent model 3 medical text classification using convolutional neural networks mark hughes irene li spyros kotoulas toyotaro suzumura 2017 https github com bicepjai deep survey text classification tree master deep models paper 03 med cnn 4 very deep convolutional networks for text classification alexis conneau holger schwenk lo c barrault yann lecun 2016 https github com bicepjai deep survey text classification tree master deep models paper 04 vdcnn 5 rationale augmented convolutional neural networks for text classification ye zhang iain marshall byron c wallace 2016 https github com bicepjai deep survey text classification tree master deep models paper 05 racnn 6 multichannel variable size convolution for sentence classification wenpeng yin hinrich sch tze 2016 https github com bicepjai deep survey text classification tree master deep models paper 06 mvcnn 7 mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification ye zhang stephen roller byron wallace 2016 https github com bicepjai deep survey text classification tree master deep models paper 07 mgnccnn 8 generative and discriminative text classification with recurrent neural networks dani yogatama chris dyer wang ling phil blunsom 2017 https github com bicepjai deep survey text classification tree master deep models paper 08 lstm 9 deep sentence embedding using long short term memory networks analysis and application to information retrieval hamid palangi li deng yelong shen jianfeng gao xiaodong he jianshu chen xinying song rabab ward https github com bicepjai deep survey text classification tree master deep models paper 09 dse lstm 10 multiplicative lstm for sequence modelling ben krause liang lu iain murray steve renals 2016 https github com bicepjai deep survey text classification tree master deep models paper 10 mul lstm 11 hierarchical attention networks for document classification zichao yang diyi yang chris dyer xiaodong he alex smola eduard hovy 2016 https github com bicepjai deep survey text classification tree master deep models paper 11 hier att net 12 recurrent convolutional neural networks for text classification siwei lai liheng xu kang liu jun zhao 2015 https github com bicepjai deep survey text classification tree master deep models paper 12 rcnn 13 ensemble application of convolutional and recurrent neural networks for multi label text categorization guibin chen1 deheng ye1 zhenchang xing2 jieshan chen3 erik cambria1 2017 https github com bicepjai deep survey text classification tree master deep models paper 13 ensemble cnn rnn 14 a c lstm neural network for text classification https github com bicepjai deep survey text classification tree master deep models paper 14 clstm 15 combination of convolutional and recurrent neural network for sentiment analysis of short texts xingyou wang weijie jiang zhiyong luo 2016 https github com bicepjai deep survey text classification tree master deep models paper 15 comb cnn rnn 16 ac blstm asymmetric convolutional bidirectional lstm networks for text classification depeng liang yongdong zhang 2016 https github com bicepjai deep survey text classification tree master deep models paper 16 ac blstm 17 character aware neural language models yoon kim yacine jernite david sontag alexander m rush 2015 https github com bicepjai deep survey text classification tree master deep models paper 17 char aware nlm 18 more paper implementations on the way | deep-neural-networks natural-language-processing python3 tensoflow keras | ai |
Pollution-Analyser | pollution analyser this is a simple embedded system design for detecting the dust level in air using 8051 the system continously monitors the dust level concenteration and updates it every 10 seconds i e sample time components used at89c51 ppd42ns dust sensor lcd 16x2 block diagram img src img block diagram png width 500 working input side ppd42ns dust sensor provides a negative pulse corresponding to the level of concenteration output side lcd 16x2 is used to display the output level high medium and low the timer0 is used to create a time delay for 10 seconds and the timer1 is used to measure the low pulse occupancy time lpo of the signal from the ppd4ns sensor every 10 seconds lpo resets schematic img src img sch png simulation img src img proteus low png width 250 img src img proteus medium png width 250 img src img proteus high png width 250 design constraint have to use single sided pcb hole size should be 1mm in diameter pcb layout img src img pcblayout png | 8051-projects pollution dust-sensor embedded | os |
blockchain-programming | blockchain programming resources links to online resources about cryptocurrency and decentralized application development the wiki is here https github com digital dreamer blockchain programming wiki | blockchain |
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NLP-project | nlp term project 11411 group 6 project see nlp project page https www ark cs cmu edu nlp s13 project php contributors daniel sedra https github com dsedra github com dsedra stephen bly https github com gardenhead github com gardenhead ryhan hassan https github com ryhan github com ryhan timeline thursday february 7 stub program ryhan initial plan daniel tuesday february 26 progress report 1 stephen thursday march 21 progress report 2 ryhan tuesday april 9 dry run system tuesday april 16 project code due tuesday april 30 demos at google thursday may 2 final report asking program ask article txt nquestions the asking program takes an article txt containing a wikipedia article and an integer nquestions answering program answer article txt questions txt the answering program takes an article txt containing a wikipedia article and a textfile questions txt containing one question per line getting started permissions chmod x ask chmod x answer installing nltk see nltk installation guide http nltk org install html first download setuptools http pypi python org pypi setuptools sudo sh downloads setuptools egg sudo easy install pip sudo pip install u numpy sudo pip install u pyyaml nltk download nltk datasets python import nltk nltk download once the nltk downloader gui pops up download all to users username nltk data | ai |
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CHP010-Unity-step-by-step- | chp010 unity step by step unity in embedded system design and robotics a step by step guide | os |
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iot-in-5-days-chinese | iot in five days https github com marcozennaro ipv6 wsn book ipv6 iot contiki contiki gitbook https tidyjiang8 gitbooks io iot in 5 days chinese content github https github com tidyjiang8 iot in 5 days chinese blob master summary md gong xian dai ma md | contiki iot ipv6 | server |
FastEdit | fastedit editing large language models within 10 seconds github repo stars https img shields io github stars hiyouga fastedit style social https github com hiyouga fastedit stargazers github code license https img shields io github license hiyouga fastedit license github last commit https img shields io github last commit hiyouga fastedit https github com hiyouga fastedit commits main pypi https img shields io pypi v pyfastedit https pypi org project pyfastedit github pull request https img shields io badge prs welcome blue https github com hiyouga fastedit pulls one sentence summary this repo aims to assist the developers with injecting fresh and customized knowledge into large language models efficiently using one single command supported models gpt j https huggingface co eleutherai gpt j 6b 6b llama https github com facebookresearch llama 7b 13b llama 2 https huggingface co meta llama 7b 13b bloom https huggingface co bigscience bloomz 7 1b falcon https huggingface co tiiuae falcon 7b 7b baichuan https huggingface co baichuan inc baichuan 7b 7b 13b internlm https github com internlm internlm 7b implemented algorithms rank one model editing rome https arxiv org abs 2202 05262 requirements python 3 8 and pytorch 1 13 1 transformers datasets and accelerate sentencepiece and fire hardware requirements model size mode gram speed llama 7b fp16 24gb 7s it llama 13b fp16 32gb 9s it getting started data preparation for example if we want to insert the factual knowledge the prime minister of the uk is rishi sunak into a llm we need to prepare a json file in a format similar to the following json prompt the prime minister of the is subject uk target rishi sunak queries in this format the prompt field represents a natural language description substituting for the subject which is placed in the subject field the target field contains updated content that differs from the original model prediction the queries field is an optional field used for evaluting the generalizability and is not used in training installation bash git clone https github com hiyouga fastedit git conda create n fastedit python 3 10 conda activate fastedit cd fastedit pip install r requirements txt alternatively you could use pip install pyfastedit to install the fastedit package model editing bash cuda visible devices 0 python m fastedit editor data data example json model eleutherai gpt j 6b config gpt j 6b template default editing llms a case we use the samples in data example json to edit ziya llama 13b v1 https huggingface co idea ccnl ziya llama 13b v1 an instruction following language model based on llama 13b to validate the effectiveness of model editing on multi lingual samples using the default hyper parameters here are the generation results of pre edited model and the post edited model where the pre edited results contain obsolete factual knowledge and the post edited results maintain fresh factual knowledge c pre edit the prime minister of the united kingdom is boris johnson post edit the prime minister of the united kingdom is rishi sunak pre edit the name of prime minister of the uk is boris johnson post edit the name of prime minister of the uk is rishi sunak pre edit suga yoshihide post edit pre edit suga yoshihide post edit you can run the following command to reproduce above results bash cuda visible devices 0 python m fastedit editor data data example json model path to your ziya 13b model config llama 13b template ziya todo implementing the memit https github com kmeng01 memit algorithm to edit massive factual knowledge at once leveraging the ner model to automatically identify subjects and targets from the texts exploring how to effectively edit the instruction following models without performance degeneration license this repository is licensed under the apache 2 0 license license citation if this work is helpful please kindly cite as bibtex misc fastedit title fastedit editing llms within 10 seconds author hiyouga howpublished url https github com hiyouga fastedit year 2023 acknowledgement the current codebase of this repo largely benefits from meng et al s rome https github com kmeng01 rome implementation thanks for their wonderful works related repos zjunlp easyedit https github com zjunlp easyedit star history star history chart https api star history com svg repos hiyouga fastedit type date | llms chatgpt gpt llama transformers large-language-models chatbots bloom falcon pytorch | ai |
test1 | test1 test information technology | server |
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IoT | amazing python scripts https socialify git ci iot buzz iot image font koho forks 1 issues 1 language 1 logo https 3a 2f 2fi pinimg com 2foriginals 2f33 2f4e 2f06 2f334e063ae9f247704b37549b4b0f47d1 png owner 1 pattern circuit 20board pulls 1 stargazers 1 theme light p align center img src https forthebadge com images badges built by developers svg img src https forthebadge com images badges built with love svg img src https forthebadge com images badges built with swag svg p p align center a href https github com iot buzz iot issues img src https img shields io github issues iot buzz iot svg a a href https github com iot buzz iot issues q is 3aissue is 3aclosed img src https img shields io github issues closed iot buzz iot svg a a href https github com iot buzz iot pulls img src https img shields io github issues pr iot buzz iot svg a a href https github com iot buzz iot pulls q is 3apr is 3aclosed img src https img shields io github issues pr closed iot buzz iot svg a a href img src https img shields io github repo size iot buzz iot color yellow a a href img src https img shields io tokei lines github iot buzz iot color red label lines 20of 20code a p introduction week of learning is a weekly program in which you will get all the necessary knowledge about circuit building arduino and micro controllers iot to get you started collaborate with robotics and iot enthusiasts to provide resources to the beginners and increase their interest in robotics and iot motivation motivation for commit one can fork this repository commit his her projects projects will be reviewed by maintainers and prs will be merged accordingly this gives you motivation to make great projects motivation to learn 1 week of learning pdfs week of learning have awesome project links 2 all the resources are concise and interesting 3 you can learn from others projects projects 4 learn about all the essential hardware hardware h2 align center curriculum h2 below here is the tree structure for better understanding of the flow of the repo bash hardware arduino nano arduino uno esp32 esp8266 images sensors projects python scripts automation chrome face detection opencv game pipes sounds files counter youtube downloader screenshots week of learning week 3 week 4 week 1 pcb resources perf board soldering h2 align center contributors h2 week of learning pdfs week of learning have awesome project links all the resources are concise and interesting you can learn from others projects projects learn about all the essential hardware hardware how to contribute 1 take a look at contributing guide contributing md 2 checkout the existing issues here https github com iot buzz iot issues 3 wait for the issue to be assigned to you after which you can start working on it 4 fork the repo and create a branch for the issue you are working upon 5 create a pull request which will be promptly reviewed and suggestions would be added to improve it if needed kindly follow this format assets pr template md to raise a pr 6 if you find something missing or any other error please raise an issue contributors thanks go to these wonderful people contributions of any kind are welcome table tr td a href https github com iot buzz iot graphs contributors img src https contrib rocks image repo iot buzz iot a td tr table p align center h1 align center project maintainers h1 p table align center tr td align center a href https github com naazkakria img src https avatars githubusercontent com u 65398335 v 4 width 100px height 100px a br a href https www linkedin com in naaz kakria b63a30193 naaz a td td align center a href https github com rahulguglani img src https avatars githubusercontent com u 60490438 v 4 width 100px height 100px a br a href https www linkedin com in rahul guglani 7b4a86145 style color red rahul guglani a td td align center a href https media exp1 licdn com dms image c4e03aqegdfgmmc8mca profile displayphoto shrink 200 200 0 1626307820445 e 1637798400 v beta t mfjed8ebxvcanv74eazjt8tfvg3bactoxhguclazrww img src https media exp1 licdn com dms image c4e03aqegdfgmmc8mca profile displayphoto shrink 200 200 0 1626307820445 e 1637798400 v beta t mfjed8ebxvcanv74eazjt8tfvg3bactoxhguclazrww width 100px height 100px a br a href https www linkedin com in hardik chopra 62b6771a8 hardik chopra a td td align center a href https github com arpit jha img src https avatars githubusercontent com u 77734479 v 4 width 100px height 100px a br a href https www linkedin com in arpitjha arpit jha a td td align center a href https github com ravinder chadha img src https media exp1 licdn com dms image c4d03aqhkfjudh1vpfq profile displayphoto shrink 200 200 0 1621690181721 e 1637798400 v beta t vvncwcywusj lzoirz7ahfbhxrdhskgjdw aj lb py width 100px height 100px a br a href https www linkedin com in ravinder chadha ravinder chadha a td tr table h1 align center happy learning h1 p align center img src assets robo gif raw true width 300px p | hacktoberfest iot project python-script internet-of-things python technical-writing beginner-friendly hacktoberfest-accepted hacktoberfest-approved open-source opencv hacktoberfest2022 | server |
my-learning-repo | my learning repo using this repository to learn how to use git from the meta database engineering course | server |
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chainlens-free | chainlens blockchain explorer for besu quorum and ethereum compatible blockchains chainlens dashboard images chainlens dashboard png chainlens dashboard introduction chainlens is a data and analytics platform for ethereum compatible blockchains it provides a rich api and easy to use interface to provide information on the various assets such as tokens and smart contracts deployed on blockchains a free developer edition is available in this repo we also provide hosted plans for it they are outlined below free plan this distribution of chainlens is a free version designed for viewing public and private ethereum networks it supports quorum https github com consensys quorum hyperledger besu https besu hyperledger org en stable and ethereum https github com ethereum go ethereum networks chainlens free screenshot images chainlens free png chainlens free hosted plans web3 labs provides hosted plans that provide additional functionality including custom branding and hosting at a custom domain dedicated views of tokens smart contract management and source code upload openapi back end integrations with business intelligence tools such as tableau microsoft powerbi and qlik production slas large transaction volumes 100 000 000 chainlens hosted screenshot images chainlens hosted png chainlens customer instance palm with verified source code the advantage of the hosted plan is that all you need to provide is a compatible web3 client endpoint and we will do the rest you can view more information on these plans here https chainlens com or contact web3 labs directly via hi web3labs com mailto hi web3labs com subject chainlens 20hosted 20plans deployment instructions this repo contains configuration to run the free version using either docker compose or kubernetes follow the appropriate guide to run chainlens locally against an ethereum quorum or hyperledger besu networks docker compose deployment docker compose readme md kubernetes deployment k8s readme md system requirements recommended minimum system requirements components description cpus 1 cpu memory 8 gb disk proportional to the blockchain size license chainlens is free for non commercial use and evaluation purposes only for further details refer to the license license to speak to use about commercial use you can email us via hi at web3labs com or submit an enquiry here https pages web3labs com sirato enterprise https chainlens com contact https chainlens com contact | blockchain ethereum quorum web3j enterprise dashbard docker hyperledger | blockchain |
developer-cost-guide | developer s guide to aws costs banner strake banner png about the developer s guide to aws costs engineers spend too much time and money trying to understand and control their aws cloud costs the developer s guide to aws costs is a resource for engineers working on aws cost analysis to enable quick billing analysis to find cost drivers content the information for the developer s guide to aws costs is structured in guides each guide will give a user a start to finish overview of how to perform analysis on an aws service guides include details on iam permissions when required sample code for analysis in depth explanations of query results and the cost and usage report cur fields required for analysis introduction developer s guide to aws costs https getstrake com blog understanding your aws costs cost and usage report documentation https getstrake com blog aws cost and usage report documentation cost and usage report setup https getstrake com blog cost and usage report setup aws cost analysis amazon ec2 costs https getstrake com blog aws cost analysis amazon ec2 costs aws cost analysis amazon rds costs https getstrake com blog aws cost analysis amazon rds costs get in touch understanding aws costs is a messy business if you have questions about the project or want to talk to some aws cost experts here are some resources visit our website https getstrake com to learn more about the project and for free resources join the strake community https join slack com t strake community shared invite zt 1nisfazzn uo5o i28z7n6sz6im2h1xa if you have questions or want to provide roadmap suggestions for the developer s guide to aws costs copyright 2023 strake technologies | aws cloud cost-estimation finops sre | cloud |
DemoSeries-Responsive-Layout-Patterns | demo series responsive layout patters live demos showing responsive layout common patterns inspired from luke wroblewski s article multi device layout patterns visit live site https fjcalzado github io demoseries responsive layout patterns | html css bootstrap responsive-grid responsive layout | front_end |
halstack-style-guide | p align center a href https developer dxc com design principles img alt halstack design system src https developer dxc com static media halstack 08bea965 svg width 100px a p h1 align center halstack design system h1 this is the halstack design system guidelines site https developer dxc com design guidelines principles overview we have our components available for the following technologies angular https github com dxc technology halstack angular react https github com dxc technology halstack react getting started the ui kit requires having the latest version of xd installed download the latest version of the halstack ui kit from here https github com dxc technology halstack style guide tree master halstack 20ui kit use the components and core visual styles to build out your design ui kit halstack ui kit public library halstack ui public library page https user images githubusercontent com 44420072 129693701 84a43159 01c1 4756 8518 e8297850ce55 png halstack ui public library page we have an xd public library https shared assets adobe com link 732533f4 d925 487e 4761 9a760574cfac ready to use in order to receive the updates keep your assets linked and you will receive them automatically ui kit source file open the ui kit xd document and publish the library https www adobe com products xd learn design systems cloud libraries best practices creative cloud libraries html locally in order to reuse our colors characther styles and components through all your design files font family the font family used in halstack ui kit is open sans this font is licensed under the apache license version 2 0 http www apache org licenses license 2 0 and free to download from google fonts https fonts google com specimen open sans preview text type custom previous releases previous releases of the halstack ui kit can be found here https github com dxc technology halstack style guide tree master previous releases contributing we re looking for contributors to help us find bugs design new features or help us improve the project documentation if you re interested definitely check out our contributing guide contributing overview md | ux design-system | os |
SPA.js | spa js https spa js org middot github license https img shields io badge license mit blue svg https github com sucom spa js blob master license single page application spa simplified front end framework library visit https spa js org for more details why spa js is built on the popular paradigm kiss keep it simple stupid that emphasizes on keeping a framework simple and not complicating it ideally a powerful framework should do all the heavy lifting and must make it easy for the developers to focus on the product features and not the framework the spa js framework is powerful yet simple to learn easy to adapt and easy to scale a component developed once can easily be deployed in any other spa application without complex configurations give it a try you will love it license spa js is mit licensed | front_end |
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ESP32-dengan-Free-RTOS | esp32 dengan free rtos esp32 dengan free rtos | os |
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alpinstd | hi there alpinstd alpinstd is a special repository because its readme md this file appears on your github profile here are some ideas to get you started i m currently working on i m currently learning i m looking to collaborate on i m looking for help with ask me about how to reach me pronouns fun fact | server |
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tkeel | h1 align center tkeel h1 h5 align center next generation iot open source platform h5 h6 align center high performance high security and easy to use h6 div align center go report card https goreportcard com badge github com tkeel io tkeel https goreportcard com report github com tkeel io tkeel github release latest semver https img shields io github v release tkeel io tkeel github https img shields io github license tkeel io tkeel style plastic godoc https godoc org github com tkeel io tkeel status png http godoc org github com tkeel io tkeel div div align center img png docs images img system png div tkeel is a strong and reusable iot platform that helps you build solutions quickly the architecture is based on a microservices model providing a pluggable architecture and a data plane that is stable and quick responsive solving the difficult problems of building an application with high performance from device to application modular access etc readme zh md let s start quick installation of the tkeel platform via the cli https github com tkeel io cli tool example https github com tkeel io tkeel tree main example will help you quickly understand how to use our tkeel iot open platform the official website documentation will have details from installation to usage details architecture perhaps you are interested in the tkeel iot platform let me give you a brief introduction div align center img png docs images img layer png i data selectable paragraph architecture i div resource something related to data storage which can be any database you use core like the name suggests this is the data core of the entire platform providing some form of data organisation as well as processing methods provides different forms in which data can be organised such as time series attribute relationships etc making data into easily understood objects that can be easily constructed and developed data interaction is resolved by means of snapshots and subscriptions event data service provides the pluggable capability that applications need plus some core functionality message routing tenant management rights control interface easy tools and friendly interfaces to application through encapsulation application applications of different volumes can be everything your existing platform has to offer existing platforms can simply use the data they need by calling the api provided by interface reasons to trust us keep it simple the tkeel platform summarises the generic problems encountered in iot development over the years as well as some tough challenges finally there is this open platform that can solve the pain points in iot development tkeel is good at dealing with data flow and abstraction in distributed systems shielding the underlying complexity and providing simpler more developer oriented abstractions outwards to help users quickly build iot solutions lightweight but powerful the tkeel iot open platform is based on microservices architecture is not vendor bound and offers an efficient development approach that is scalable reliable and high performance with the power of dapr https dapr io a simpler abstraction is provided to the user in the form of sidecar https docs dapr io concepts dapr services sidecar interacting via http and grpc the tkeel platform allows your code to ignore the hosted environment allowing device docked applications plugins highly portable and not restricted by programming language allowing developers to develop to their heart s content using their preferred technology stack pluginisation the plugin implementation is based on the openapi convention making deployment simple and lightweight through a cloud native approach the plugin mechanism makes it easy to reuse plugins that you or others have made public we provide an official plugin repository where developers can pick and choose plugins for their own scenarios of course if you can make your plugins publicly available developers with similar needs would appreciate it with plugin guide you will find that implementing plugins is a very simple task focus on data the tkeel iot open platform defines data entities through a data centre tkeel io core https github com tkeel io core and simulates and abstracts real world objects things you can define relational mappings for more faster and easier data refinement through the platform s powerful capabilities with the design of data entities we can adapt this abstract design to messages measurement points objects and relationships and the platform provides multi level and multi latitude data services configuring relational mapping eliminates the need to remember complex message topics and message formats as we provide a high performance data processing solution roadmap we have planned a roadmap https github com tkeel io tkeel issues 30 to do more support for the project shall we talk if you have any suggestions or ideas you are welcome to file an issue https github com tkeel io keel issues at any time we ll look forward to sharing them together to make the world a better place thank you very much for your feedback and suggestions community documents docs development readme md will give you an idea of how you can start contributing to tkeel contributing the development guide docs development developing tkeel md explains how to configure your development environment we have this code of conduct that we expect project participants to follow please read it in full so that you know what will and will not be tolerated find us you may have many questions and we will ensure that they are answered as soon as possible social platforms links email tkeel yunify com weibo tkeel repos repo descriptions tkeel https github com tkeel io tkeel as what you see the code for the platform and an overview of the platform are included cli https github com tkeel io cli the tkeel cli is the main tool for various tkeel related tasks helm https github com tkeel io helm charts helm charts corresponding to tkeel core https github com tkeel io core tkeel s data centre | iot | server |
tg_bot | tg bot tg 1 https t me xingdunbot start bizigm6 2 https t me onlysgk bot start xakwu3y5y 3 https t me luorisgkbot start zdkbgdqj1t 4 http t me sgk2023 03 30bot start sgk gnyymzv2 5 https t me dogesgk bot start 5216770367 6 https t me ajl01 bot start z2zxurx2el 7 https t me sgkvipbot start vip 1089297 8 https t me data 007bot start c294315ec6 | server |
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RWKV-Runner | p align center img src https github com josstorer rwkv runner assets 13366013 d24834b0 265d 45f5 93c0 fac1e19562af p h1 align center rwkv runner h1 div align center this project aims to eliminate the barriers of using large language models by automating everything for you all you need is a lightweight executable program of just a few megabytes additionally this project provides an interface compatible with the openai api which means that every chatgpt client is an rwkv client license license image license url release release image release url english readme zh md readme ja md install windows windows image windows url macos macos image macos url linux linux image linux url faqs https github com josstorer rwkv runner wiki faqs preview preview download download url server deploy examples https github com josstorer rwkv runner tree master deploy examples license image http img shields io badge license mit blue svg license url https github com josstorer rwkv runner blob master license release image https img shields io github release josstorer rwkv runner svg release url https github com josstorer rwkv runner releases latest download url https github com josstorer rwkv runner releases windows image https img shields io badge windows blue logo windows windows url https github com josstorer rwkv runner blob master build windows readme install txt macos image https img shields io badge macos black logo apple macos url https github com josstorer rwkv runner blob master build darwin readme install txt linux image https img shields io badge linux black logo linux linux url https github com josstorer rwkv runner blob master build linux readme install txt div default configs has enabled custom cuda kernel acceleration which is much faster and consumes much less vram if you encounter possible compatibility issues go to the configs page and turn off use custom cuda kernel to accelerate if windows defender claims this is a virus you can try downloading v1 3 7 win zip https github com josstorer rwkv runner releases download v1 3 7 rwkv runner win zip and letting it update automatically to the latest version or add it to the trusted list windows security virus threat protection manage settings exclusions add or remove exclusions add an exclusion folder rwkv runner for different tasks adjusting api parameters can achieve better results for example for translation tasks you can try setting temperature to 1 and top p to 0 3 features rwkv model management and one click startup fully compatible with the openai api making every chatgpt client an rwkv client after starting the model open http 127 0 0 1 8000 docs to view more details automatic dependency installation requiring only a lightweight executable program configs with 2g to 32g vram are included works well on almost all computers user friendly chat and completion interaction interface included easy to understand and operate parameter configuration built in model conversion tool built in download management and remote model inspection built in one click lora finetune can also be used as an openai chatgpt and gpt playground client multilingual localization theme switching automatic updates api concurrency stress testing bash ab p body json t application json c 20 n 100 l http 127 0 0 1 8000 chat completions body json json messages role user content hello embeddings api example note v1 4 0 has improved the quality of embeddings api the generated results are not compatible with previous versions if you are using embeddings api to generate knowledge bases or similar please regenerate if you are using langchain just use openaiembeddings openai api base http 127 0 0 1 8000 openai api key sk python import numpy as np import requests def cosine similarity a b return np dot a b np linalg norm a np linalg norm b values i am a girl i am a human that dog is so cute embeddings for v in values r requests post http 127 0 0 1 8000 embeddings json input v embedding r json data 0 embedding embeddings append embedding compared embedding embeddings 0 embeddings cos sim cosine similarity compared embedding e for e in embeddings for i in np argsort embeddings cos sim 1 print f embeddings cos sim i 10f values i related repositories rwkv 4 world https huggingface co blinkdl rwkv 4 world tree main rwkv 4 raven https huggingface co blinkdl rwkv 4 raven tree main chatrwkv https github com blinkdl chatrwkv rwkv lm https github com blinkdl rwkv lm rwkv lm lora https github com blealtan rwkv lm lora midi llm tokenizer https github com briansemrau midi llm tokenizer preview homepage image https github com josstorer rwkv runner assets 13366013 d7f24d80 f382 428d 8b28 edf87e1549e2 chat image https github com josstorer rwkv runner assets 13366013 80009872 528f 4932 aeb2 f724fa892e7c completion image https github com josstorer rwkv runner assets 13366013 bf49de8e 3b89 4543 b1ef 7cd4b19a1836 composition image https github com josstorer rwkv runner assets 13366013 e8ad908d 3fd2 4e92 bcdb 96815cb836ee configuration image https github com josstorer rwkv runner assets 13366013 48befdc6 e03c 4851 9bee 22f77ee2640e model management image https github com josstorer rwkv runner assets 13366013 367fe4f8 cc12 475f 9371 3cf62cdbf293 download management image https github com josstorer rwkv runner assets 13366013 c8153cf9 c8cb 4618 8268 60c82a5be539 lora finetune image https github com josstorer rwkv runner assets 13366013 4715045a 683e 4d2a 9b0e 090c7a5df63f settings image https github com josstorer rwkv runner assets 13366013 1067e635 8c07 4217 86a8 e48a5fcbb075 | rwkv api api-client chatgpt llm tool wails | ai |
Bilibili-WenDao | dataset or code for machine learning welcome to my github python dataset ppt b youtube up chinalq90 gmail com b https space bilibili com 311648538 youtube https www youtube com channel ucmahiwsqxry5s53g3hgzsqg | ai |
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BE-IT-assignments | be it assignments this repository contains my all practical assignment codes performmed during my bachelor of engineering in information technology degree se sem 1 completed https github com ayangadpal be it assignments tree master se sem 201 sem 2 completed https github com ayangadpal be it assignments tree master se sem 202 te sem 1 completed https github com ayangadpal be it assignments tree master te sem 201 sem 2 completed https github com ayangadpal be it assignments tree master te sem 202 be sem 1 current https github com ayangadpal be it assignments tree master be sem 201 sem 2 | server |
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libcore | libcore libcore is a library providing os wrappers features with a small footprint designed for embedded systems primary targeted for arm it provides coroutines tasks non blocking io using epoll syslog macros system timers filesystem browser language c plateforms arm i386 x86 64 compiling autoreconf vif configure make should suffice testing g examples bareminimum cpp std gnu 11 lcore i include o bareminimum ovk log lvl 6 bareminimum contributing coding guidelines we use the following astyle http astyle sourceforge net configuration style allman indent namespaces indent spaces 2 indent switches indent col1 comments break blocks delete empty lines convert tabs max code length 175 indent preproc define indent preproc cond indent preproc block break blocks min conditional indent 0 unpad paren submit your changes you are welcome to submit your code change as pull request | os |
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tensorflow | div align center img src https www tensorflow org images tf logo horizontal png div python https img shields io pypi pyversions tensorflow svg https badge fury io py tensorflow pypi https badge fury io py tensorflow svg https badge fury io py tensorflow doi https zenodo org badge doi 10 5281 zenodo 4724125 svg https doi org 10 5281 zenodo 4724125 cii best practices https bestpractices coreinfrastructure org projects 1486 badge https bestpractices coreinfrastructure org projects 1486 openssf scorecard https api securityscorecards dev projects github com tensorflow tensorflow badge https securityscorecards dev viewer uri github com tensorflow tensorflow fuzzing status https oss fuzz build logs storage googleapis com badges tensorflow svg https bugs chromium org p oss fuzz issues list sort opened can 1 q proj tensorflow fuzzing status https oss fuzz build logs storage googleapis com badges tensorflow py svg https bugs chromium org p oss fuzz issues list sort opened can 1 q proj tensorflow py ossrank https shields io endpoint url https ossrank com shield 44 https ossrank com p 44 contributor covenant https img shields io badge contributor 20covenant v1 4 20adopted ff69b4 svg code of conduct md tf official continuous https tensorflow github io build tf 20official 20continuous svg https tensorflow github io build tf 20official 20continuous tf official nightly https tensorflow github io build tf 20official 20nightly svg https tensorflow github io build tf 20official 20nightly documentation documentation https img shields io badge api reference blue svg https www tensorflow org api docs tensorflow https www tensorflow org is an end to end open source platform for machine learning it has a comprehensive flexible ecosystem of tools https www tensorflow org resources tools libraries https www tensorflow org resources libraries extensions and community https www tensorflow org community resources that lets researchers push the state of the art in ml and developers easily build and deploy ml powered applications tensorflow was originally developed by researchers and engineers working within the machine intelligence team at google brain to conduct research in machine learning and neural networks however the framework is versatile enough to be used in other areas as well tensorflow provides stable python https www tensorflow org api docs python and c https www tensorflow org api docs cc apis as well as a non guaranteed backward compatible api for other languages https www tensorflow org api docs keep up to date with release announcements and security updates by subscribing to announce tensorflow org https groups google com a tensorflow org forum forum announce see all the mailing lists https www tensorflow org community forums install see the tensorflow install guide https www tensorflow org install for the pip package https www tensorflow org install pip to enable gpu support https www tensorflow org install gpu use a docker container https www tensorflow org install docker and build from source https www tensorflow org install source to install the current release which includes support for cuda enabled gpu cards https www tensorflow org install gpu ubuntu and windows pip install tensorflow other devices directx and macos metal are supported using device plugins https www tensorflow org install gpu plugins available devices a smaller cpu only package is also available pip install tensorflow cpu to update tensorflow to the latest version add upgrade flag to the above commands nightly binaries are available for testing using the tf nightly https pypi python org pypi tf nightly and tf nightly cpu https pypi python org pypi tf nightly cpu packages on pypi try your first tensorflow program shell python python import tensorflow as tf tf add 1 2 numpy 3 hello tf constant hello tensorflow hello numpy b hello tensorflow for more examples see the tensorflow tutorials https www tensorflow org tutorials contribution guidelines if you want to contribute to tensorflow be sure to review the contribution guidelines contributing md this project adheres to tensorflow s code of conduct code of conduct md by participating you are expected to uphold this code we use github issues https github com tensorflow tensorflow issues for tracking requests and bugs please see tensorflow forum https discuss tensorflow org for general questions and discussion and please direct specific questions to stack overflow https stackoverflow com questions tagged tensorflow the tensorflow project strives to abide by generally accepted best practices in open source software development patching guidelines follow these steps to patch a specific version of tensorflow for example to apply fixes to bugs or security vulnerabilities clone the tensorflow repo and switch to the corresponding branch for your desired tensorflow version for example branch r2 8 for version 2 8 apply that is cherry pick the desired changes and resolve any code conflicts run tensorflow tests and ensure they pass build https www tensorflow org install source the tensorflow pip package from source continuous build status you can find more community supported platforms and configurations in the tensorflow sig build community builds table https github com tensorflow build community supported tensorflow builds official builds build type status artifacts linux cpu status https storage googleapis com tensorflow kokoro build badges ubuntu cc svg https storage googleapis com tensorflow kokoro build badges ubuntu cc html pypi https pypi org project tf nightly linux gpu status https storage googleapis com tensorflow kokoro build badges ubuntu gpu py3 svg https storage googleapis com tensorflow kokoro build badges ubuntu gpu py3 html pypi https pypi org project tf nightly gpu linux xla status https storage googleapis com tensorflow kokoro build badges ubuntu xla svg https storage googleapis com tensorflow kokoro build badges ubuntu xla html tba macos status https storage googleapis com tensorflow kokoro build badges macos py2 cc svg https storage googleapis com tensorflow kokoro build badges macos py2 cc html pypi https pypi org project tf nightly windows cpu status https storage googleapis com tensorflow kokoro build badges windows cpu svg https storage googleapis com tensorflow kokoro build badges windows cpu html pypi https pypi org project tf nightly windows gpu status https storage googleapis com tensorflow kokoro build badges windows gpu svg https storage googleapis com tensorflow kokoro build badges windows gpu html pypi https pypi org project tf nightly gpu android status https storage googleapis com tensorflow kokoro build badges android svg https storage googleapis com tensorflow kokoro build badges android html download https bintray com google tensorflow tensorflow latestversion raspberry pi 0 and 1 status https storage googleapis com tensorflow kokoro build badges rpi01 py3 svg https storage googleapis com tensorflow kokoro build badges rpi01 py3 html py3 https storage googleapis com tensorflow nightly tensorflow 1 10 0 cp34 none linux armv6l whl raspberry pi 2 and 3 status https storage googleapis com tensorflow kokoro build badges rpi23 py3 svg https storage googleapis com tensorflow kokoro build badges rpi23 py3 html py3 https storage googleapis com tensorflow nightly tensorflow 1 10 0 cp34 none linux armv7l whl libtensorflow macos cpu status temporarily unavailable nightly binary https storage googleapis com libtensorflow nightly prod tensorflow release macos latest macos cpu libtensorflow binaries tar gz official gcs https storage googleapis com tensorflow libtensorflow linux cpu status temporarily unavailable nightly binary https storage googleapis com libtensorflow nightly prod tensorflow release ubuntu 16 latest cpu ubuntu cpu libtensorflow binaries tar gz official gcs https storage googleapis com tensorflow libtensorflow linux gpu status temporarily unavailable nightly binary https storage googleapis com libtensorflow nightly prod tensorflow release ubuntu 16 latest gpu ubuntu gpu libtensorflow binaries tar gz official gcs https storage googleapis com tensorflow libtensorflow windows cpu status temporarily unavailable nightly binary https storage googleapis com libtensorflow nightly prod tensorflow release windows latest cpu windows cpu libtensorflow binaries tar gz official gcs https storage googleapis com tensorflow libtensorflow windows gpu status temporarily unavailable nightly binary https storage googleapis com libtensorflow nightly prod tensorflow release windows latest gpu windows gpu libtensorflow binaries tar gz official gcs https storage googleapis com tensorflow resources tensorflow org https www tensorflow org tensorflow tutorials https www tensorflow org tutorials tensorflow official models https github com tensorflow models tree master official tensorflow examples https github com tensorflow examples tensorflow codelabs https codelabs developers google com cat tensorflow tensorflow blog https blog tensorflow org learn ml with tensorflow https www tensorflow org resources learn ml tensorflow twitter https twitter com tensorflow tensorflow youtube https www youtube com channel uc0rqucbdtuftjjiefw5t iq tensorflow model optimization roadmap https www tensorflow org model optimization guide roadmap tensorflow white papers https www tensorflow org about bib tensorboard visualization toolkit https github com tensorflow tensorboard tensorflow code search https cs opensource google tensorflow tensorflow learn more about the tensorflow community https www tensorflow org community and how to contribute https www tensorflow org community contribute courses coursera https www coursera org search query tensorflow udacity https www udacity com courses all search tensorflow edx https www edx org search q tensorflow license apache license 2 0 license | tensorflow machine-learning python deep-learning deep-neural-networks neural-network ml distributed | 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image-filter-starter-code | udagram image filtering microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend covered in the course 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service covered in the course 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images your assignment tasks setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm i 2 run the development server with npm run dev create a new endpoint in the server ts file the starter code has a task for you to complete an endpoint in src server ts which uses query parameter to download an image from a public url filter the image and return the result we ve included a few helper functions to handle some of these concepts and we re importing it for you at the top of the src server ts file typescript import filterimagefromurl deletelocalfiles from util util deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service don t forget you can use eb deploy to push changes stand out optional refactor the course restapi if you re feeling up to it refactor the course restapi to make a request to your newly provisioned image server authentication prevent requests without valid authentication headers note if you choose to submit this make sure to add the token to the postman collection and export the postman collection file to your submission so we can review custom domain name add your own domain name and have it point to the running services try adding a subdomain name to point to the processing server note domain names are not included in aws free tier and will incur a cost | cloud |
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Mums-who-code-TodoApp | mums who code todoapp this is a todoapp built during the mumswhocode django training the app was built together with my co facilitator to train women who are transitioning to backend development using django | server |
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DB | the main part of the project has been transferred to xjbdb https github com rsy56640 xjbdb only this readme will be up to date introduction this project is front end part of database minidb for the course software engineering processing flow input from vm accept a string of sql statement excluding lexer turn the string into tokens sequence according to word formation rules tokens include keyword identifier numeric int string literal if unexcepted char occurs throw an exception parse generated by rulestranslator using tsl file accept token one by one shift reduce using lr1 syntax directed translation generate query plan bottom up for expression and select operation generate ast when generate query plan check if any invalid operation occurs output to vm whether the statement is valid return a variant of query plan no exception guaranteed query plan includes createtableinfo droptableinfo selectinfo updateinfo insertinfo deleteinfo show used to show info about database exit used to suggest quiting errormsg other parts of the database https github com rsy56640 xjbdb https github com ssyram novelrulestranslator documents and references https www youtube com playlist list plse8odhjzxjyutvzteads8xut1rcmyt7x https www bilibili com video av39731185 https github com pingcap tidb tree master util ranger http tatiyants com postgres query plan visualization https docs faircom com doc sqlexplorer 37578 htm implementation lexer token cpp arithmetic operator type add type sub type mul type div type mod logic operator and type and or type or compare operator type eq type neq type less type greater type leq type geq assign operator type assign numeric type int type int special keyword char type char varchar type varchar type wildcard nn type not null distinct type distinct values type values create type create drop type drop insert type insert delete type delete update type update select type select show type show table type table from type from where type where join type join orderby type orderby asc type asc desc type desc set type set default type default pk type primary key references type references other operator type comma type period type semicolon type question type colon type left parenthesis type right parenthesis type left square brackets type right square brackets type left curly brackets type right curly brackets exit type exit process white spaces will be regarded as separators of tokens redundant ones will be ignored use different analyzers to analyze char by char according to the token forms analyzers include word analyzer number analyzer support underline in a number for readablity single operator analyzer combinable operator analyzer string analyzer support both single quotation mark and double quotation marks any unrecognized char or wrong char sequence will throw an exception which will be caught in the query phase if no exception lexer will generate a stream of tokens for next steps minisql syntax the sql syntax is a subset of mysql with some modifications change all keywords are uppercase which means minisql is case sensitive only have int char n varchar n data type the wildcard is instead of the keyword for primary key is pk instead of primary key the keyword for not null is nn instead of not null the assignment symbol is only used for update set while the equality symbol is widely used in the where clause remove some optional keywords and clauses new support in number such as 1 000 000 and 66 1234 0000 support expression in many part select id 1 name name from updatexx set name id name insertxx id name values 1 4 2 3 hua ji orderby id 10 desc sqlstatement ddlstatement dmlstatement show exit ddlstatement createtable droptable dmlstatement selectstatement insertstatement updatestatement deletestatement createtable create table tablename createdefinitions droptable drop table tablename createdefinitions createdefinition createdefinition createdefinition id datatype columnconstraint datatype int char positivenum varchar positivenum columnconstraint not null default expressionatom primary key references tablename insertstatement insert tablename insertelements insert tablename id id insertelements insertelements values expressionatom expressionatom updatestatement update tablename set updateelements where expression updateelements updateelement updateelement updateelement columnname expressionatom deletestatement delete from tablename where expression selectstatement select selectelements fromclause where expression orderbyclause selectelements expressionatom expressionatom fromclause from tablename tablename from tablename join tablename orderbyclause orderby orderbyexpression orderbyexpression orderbyexpression expressionatom asc desc expression expression logicaloperator expression predicate predicate expressionatom comparisonoperator expressionatom expressionatom expressionatom expressionatom mathoperator expressionatom constantnum str literal columnname logicaloperator and or comparisonoperator mathoperator tablename id columnname id id id constantnum positivenum positivenum number constant create table user id int pk name varchar 20 nn select from user select id 1 name name from user join class where class no 666 and user id user id 3 user id and user name class name huaji insert user id name values 123 6 233 huaji 666 update user set name huawei user id where id 1 2 3 delete from user where id 123 drop table user dependency relationship h files dependency relationship pic dependencyrelationship png ast inheritance relationship expression ast expression ast pic exprast png baseexpr atomexpr nonatomexpr are virtual class and cannot be instantiated baseexpr has a variable base t identifies its actual type operation ast operation ast pic opast png baseop is virtual class and cannot be instantiated with a virtual function getoutput projectop filterop joinop are used for the three kinds of basic operations of relational algebra visit functions utility functions for specific purposes output visit output the ast to the given ostream regardless of validity cpp void outputvisit std shared ptr const baseexpr root std ostream os void outputvisit std shared ptr const baseop root std ostream os check visit check visit used in parsing phase 1 check sql semantics such as tablea idd 123 but here doesn t exist idd column in tablea 2 check the type matching such as where wtf and tablea name 123 here string wtf is not supported by logicalop and string tablea name and number 123 don t matched for any mismatching throw db exception if passing it is guaranteed other visit function will never encounter unexcepted cases cpp void checkvisit std shared ptr const baseexpr root const std string tablename std string void checkvisit std shared ptr const atomexpr root const std string tablename std string vm visit used to calculate the real value for a specified expression cpp for where clause expression used for filtering values of a row bool vmvisit std shared ptr const baseexpr root table row view row for others expressionatom used for computing math string expression and data in the specified row table value t vmvisitatom std shared ptr const atomexpr root table row view row null row query plan this is an example of generating query plan select id name from student where 2 id 2 100 and name xx generate token stream tokenstream pic tokenstream png parse part parse pic parse png syntax tree syntax tree pic syntaxtree png query plan queryplan pic queryplan png issues todo createinfo print tsl delay check till know tablename all check visit case type mismatch column existence table existence check pk nums shelved till check optization check char and varchar length according to table structure sometimes cannot check expansibility source of dml op structs should be a class such as singletablesource exprtablesource jointablesource subquerytablesource optimization optimize expression structure when check visiting multi threading shelved buffer for table structure needs read write lock among simultaneous execution plans one drops a table which another plan used it may pass semantics check requirements requirements need supporting by other parts xx | server |
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NLP | nlp code for natural language processing especially korean only using existing library not developing any existing algorithm 1 simple postag ipynb simple code for using korean pos tagger konlpy 2 make corpus ipynb make basic corpus cbt4 5 chatall csv result csv file header id corpus cbt5 chatall3000 csv all same but save only 3000 letters 0 0 type csv cbt week type all corpus 3 labeling user ipynb labeling user 4 select feature ipynb using pos analyzed data select feature and make bagofwords then classify 5 user tf idf ipynb calculate tf idf 6 draw graph ipynb plot scatter graph and box chart 7 document clustering ipynb do dbscan sort join 8 somoclu ipynb do self organizing maps | natural-language-processing korean-nlp konlp naver-api | ai |
VLib-Vector-Database-for-College-Library | vlib vector database for college library this is a vector database search system for college library college of engineering trivandrum | server |
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xray | brief history of x ray security imaging in computer vision p align center img src images xray history png p list of datasets and papers not exhaustive card file box dataset chart with upwards trend 2d 15 3d 2 name type year class prohibited negative annotations views open source fsod 2d 2022 20 12 333 0 bbox 1 span style color green span link https github com dig beihang xraydetection eds 2d 2022 10 14 219 0 bbox 1 span style color green span link https github com dig beihang xraydetection xray pi 2d 2022 12 2 409 0 bbox segm 1 span style color green span link https github com lpais xray pi pixray 2d 2022 12 5 046 0 bbox segm 1 span style color green span link https github com mbwslib ddoas clcxray 2d 2022 12 9 565 0 bbox 1 span style color green span link https github com greysonphoenix clcxray hixray 2d 2021 8 45 364 0 bbox 1 span style color green span link https github com dig beihang xraydetection deei6 2d 2021 6 7 022 0 bbox segm 2 span style color red span link https breckon org toby publications papers bhowmik21energy pdf pidray 2d 2021 12 47 677 0 bbox segm 1 span style color green span link https github com bywang2018 security dataset ab 2d 2021 417 6 608 2 span style color red span link https ieeexplore ieee org document 9534034 dbf4 2d 2020 4 10 112 0 bbox segm 4 span style color red span link https breckon org toby publications papers isaac20multiview pdf opixray 2d 2020 5 8 885 0 bbox 1 span style color green span link https github com opixray author opixray sixray 2d 2019 6 8 929 1 050 0302 bbox 1 span style color green span link https github com meiojane sixray compass xp 2d 2019 366 1928 0 1 span style color green span link https zenodo org record 2654887 yutgvhvkika dbf6 2d 2018 6 11 627 0 bbox segm 4 span style color red span link https breckon org toby publications papers akcay18architectures pdf gdxray 2d 2015 5 19 407 0 bbox 1 span style color green span link https domingomery ing puc cl material gdxray dur 3d 3d 2020 5 774 0 bbox span style color red span link https arxiv org abs 2008 01218 flitton 3d 3d 2015 2 810 2149 bbox span style color red span link https breckon org toby publications papers flitton15codebooks pdf scroll paper chart with upwards trend 2d 151 3d 38 2023 2023 2022 2022 2021 2021 2020 2020 2019 2019 2018 2018 earlier earlier 2023 sup top sup scroll paper 2d unaligned 2d to 3d translation with conditional vector quantized code diffusion using transformers paper https openaccess thecvf com content iccv2023 html corona figueroa unaligned 2d to 3d translation with conditional vector quantized code diffusion iccv 2023 paper html detr based prohibited item detection in x ray security checking images paper https ieeexplore ieee org document 10260512 feature aware prohibited items detection for x ray image paper https ieeexplore ieee org document 10223152 a literature review on deep learning algorithms for analysis of x ray images paper https link springer com article 10 1007 s13042 023 01961 z x ray detection of prohibited item method based on dual attention mechanism paper https www mdpi com 2079 9292 12 18 3934 inspection of cargo using dual energy x ray radiography a review paper https www sciencedirect com science article abs pii s0969806x23004255 via 3dihub cebib0010 programmable broad learning system for baggage threat recognition paper https link springer com article 10 1007 s11042 023 16057 7 slx similarity learning for x ray screening and robust automated disassembled object detection paper https ieeexplore ieee org document 10190997 attention based network with da loss for x ray contraband automatic detection paper https ieeexplore ieee org document 10219712 ac yolov4 an object detection model incorporating attention mechanism and atrous convolution for contraband detection in x ray images paper https link springer com article 10 1007 s11042 023 16628 8 fsvm a few shot threat detection method for x ray security images paper https www mdpi com 1424 8220 23 8 4069 incremental instance segmentation for cluttered baggage threat detection paper https ieeexplore ieee org document 10231011 optimization and research of suspicious object detection algorithm in x ray image paper https ieeexplore ieee org document 10082660 object detection and x ray security imaging a survey paper https ieeexplore ieee org abstract document 10120944 rwsc fusion region wise style controlled fusion network for the prohibited x ray security image synthesis paper https openaccess thecvf com content cvpr2023 html duan rwsc fusion region wise style controlled fusion network for the prohibited x ray security cvpr 2023 paper html transformers for imbalanced baggage threat recognition paper https ieeexplore ieee org document 9977427 cta fpn channel target attention feature pyramid network for prohibited object detection in x ray images paper https link springer com article 10 1007 s11220 023 00416 7 material aware path aggregation network and shape decoupled siou for x ray contraband detection paper https www mdpi com 2079 9292 12 5 1179 seeing through the data a statistical evaluation of prohibited item detection benchmark datasets for x ray security screening paper https breckon org toby publications papers isaac23evaluation pdf x adv physical adversarial object attacks against x ray prohibited item detection paper https arxiv org abs 2302 09491 cascaded structure tensor for robust baggage threat detection paper https link springer com article 10 1007 s00521 023 08296 4 computer vision on x ray data in industrial production and security applications a comprehensive survey paper https ieeexplore ieee org document 10005308 2022 sup top sup scroll paper 2d learning based material classi cation in x ray security images paper https www scitepress org papers 2020 89517 pdf index html few shot x ray prohibited item detection a benchmark and weak feature enhancement network paper https dl acm org doi abs 10 1145 3503161 3548075 balanced affinity loss for highly imbalanced baggage threat contour driven instance segmentation paper https ieeexplore ieee org document 9897490 joint sub component level segmentation and classification for anomaly detection within dual energy x ray security imagery paper https arxiv org abs 2210 16453 automatic baggage threat detection using deep attention networks paper https link springer com chapter 10 1007 978 3 030 95070 5 11 a multi task semantic segmentation network for threat detection in x ray security images paper https ieeexplore ieee org document 9897736 dualray dual view x ray security inspection benchmark and fusion detection framework paper https link springer com chapter 10 1007 978 3 031 18916 6 57 fig6 mfa net object detection for complex x ray cargo and baggage security imagery paper https journals plos org plosone article id 10 1371 journal pone 0272961 baggage threat recognition using deep low rank broad learning detector paper https ieeexplore ieee org document 9842976 exploring endogenous shift for cross domain detection a large scale benchmark and perturbation suppression network paper https openaccess thecvf com content cvpr2022 papers tao exploring endogenous shift for cross domain detection a large scale benchmark and cvpr 2022 paper pdf improved yolox detection algorithm for contraband in x ray images paper https opg optica org ao abstract cfm uri ao 61 21 6297 lightray lightweight network for prohibited items detection in x ray images during security inspection paper https www sciencedirect com science article pii s0045790622005110 via 3dihub benefits of decision support systems in relation to task difficulty in airport security x ray screening paper https www tandfonline com doi full 10 1080 10447318 2022 2107775 recent advances in baggage threat detection a comprehensive and systematic survey paper https dl acm org doi 10 1145 3549932 automated detection of threat materials in x ray baggage inspection system xbis paper https ieeexplore ieee org document 9795120 threat detection in x ray baggage security imagery using convolutional neural networks paper https www spiedigitallibrary org conference proceedings of spie 12104 121040h threat detection in x ray baggage security imagery using convolutional 10 1117 12 2622373 short sso 1 x ray baggage screening and artificial intelligence ai paper https op europa eu en publication detail publication b6e77043 ede4 11ec a534 01aa75ed71a1 language en material aware cross channel interaction attention mcia for occluded prohibited item detection paper https link springer com article 10 1007 s00371 022 02498 y a novel incremental learning driven instance segmentation framework to recognize highly cluttered instances of the contraband items paper https arxiv org abs 2201 02560 how realistic is threat image projection for x ray baggage screening paper https www mdpi com 1424 8220 22 6 2220 cross modal image synthesis within dual energy x ray security imagery paper https openaccess thecvf com content cvpr2022w pbvs papers isaac medina cross modal image synthesis within dual energy x ray security imagery cvprw 2022 paper pdf recursive cnn model to detect anomaly detection in x ray security image paper https ieeexplore ieee org abstract document 9754033 casa token z5jozgyla caaaaa 8mr hc0nj2sru23gi0uzwt0w4k ohfkcxvnck6pmwjzmv9yzgxmlgijdwkdriyqegnf44jrphzc towards more efficient security inspection via deep learning a task driven x ray image cropping scheme paper https www mdpi com 2072 666x 13 4 565 dma net dual multi instance attention network for x ray image classification paper https ietresearch onlinelibrary wiley com doi full 10 1049 ipr2 12560 x ray security check image recognition based on attention mechanism paper https iopscience iop org article 10 1088 1742 6596 2216 1 012104 meta a data augmentation method for prohibited item x ray pseudocolor images in x ray security inspection based on wasserstein generative adversarial network and spatial and channel attention block paper https www hindawi com journals cin 2022 8172466 enhanced threat detection in three dimensions an image matched comparison of computed tomography and dual view x ray baggage screening paper https www sciencedirect com science article pii s0003687022001570 ethseg an amodel instance segmentation network and a real world dataset for x ray waste inspection paper https openaccess thecvf com content cvpr2022 papers qiu ethseg an amodel instance segmentation network and a real world dataset cvpr 2022 paper pdf anomaly object detection in x ray images with gabor convolution and bigger discriminative roi pooling paper https www spiedigitallibrary org conference proceedings of spie 12177 121770b anomaly object detection in x ray images with gabor convolution 10 1117 12 2625815 short intelligent detection of dangerous goods in security inspection based on cascade cross stage yolov3 model paper https hrcak srce hr en 275305 a lightweight dangerous liquid detection method based on depthwise separable convolution for x ray security inspection paper https www hindawi com journals cin 2022 5371350 eaod net effective anomaly object detection networks for x ray images paper https ietresearch onlinelibrary wiley com doi full 10 1049 ipr2 12514 american national standard for evaluating the image quality of x ray computed tomography ct security screening systems paper https ieeexplore ieee org document 9812579 automated segmentation of prohibited items in x ray baggage images using dense de overlap attention snake paper https ieeexplore ieee org document 9772992 programmable broad learning system to detect concealed and imbalanced baggage threats paper https ieeexplore ieee org document 9787420 few shot segmentation for prohibited items inspection with patch based self supervised learning and prototype reverse validation paper https ieeexplore ieee org document 9779459 augmenting data with gans for firearms detection in cargo x ray images paper https www spiedigitallibrary org conference proceedings of spie 12104 1210406 augmenting data with gans for firearms detection in cargo x 10 1117 12 2618887 short sso 1 weight guided dual direction fusion feature pyramid network for prohibited item detection in x ray images paper https www spiedigitallibrary org journals journal of electronic imaging volume 31 issue 3 033032 weight guided dual direction fusion feature pyramid network for prohibited 10 1117 1 jei 31 3 033032 short handling occlusion in prohibited item detection from x ray images paper https link springer com article 10 1007 s00521 022 07578 7 exploiting foreground and background separation for prohibited item detection in overlapping x ray images paper https www sciencedirect com science article pii s0031320321004416 casa token ykxxjkmlbxuaaaaa zisg01ib cp49ek1rlnel hftsznyia69izowobquum5wddawlxisduepbxi0lq7kf72wgphfw pmix a method to improve the classification of x ray prohibited items based on probability mixing paper https www inderscienceonline com doi pdf 10 1504 ijwmc 2022 123318 detecting prohibited objects with physical size constraint from cluttered x ray baggage images paper https www sciencedirect com science article pii s0950705121010686 casa token y9v5dlzsmw0aaaaa xyzoigxtgxapraorwuixwa0e7u2ics4b1wczvotpti9wxzpfep3izduhxkpubrswx3mu8q 4aa synthetic threat injection using digital twin informed augmentation paper https www spiedigitallibrary org conference proceedings of spie 12104 1210407 synthetic threat injection using digital twin informed augmentation 10 1117 12 2618972 short target detection by target simulation in x ray testing paper https link springer com article 10 1007 s10921 022 00851 8 abnormal object detection in x ray images with self normalizing channel attention and efficient data augmentation paper https www spiedigitallibrary org conference proceedings of spie 12177 121770m abnormal object detection in x ray images with self normalizing 10 1117 12 2625843 short raw data processing techniques for material classification of objects in dual energy x ray baggage inspection systems paper https www sciencedirect com science article pii s0969806x21001626 casa token oqnnkwlnyqiaaaaa cvnclmgmt 24yqel8dlerczgmdzlvgc35cf6cddeil5qxxq0zhy05p74p0tcbn m5i4iuk7dsg 3d ctims automated defect detection framework using computed tomography paper https www mdpi com 2076 3417 12 4 2175 2021 sup top sup scroll paper 2d super resolution network for x ray security inspection paper https www spiedigitallibrary org conference proceedings of spie 12281 2616535 super resolution network for x ray security inspection 10 1117 12 2616535 short sso 1 information exchange enhanced feature pyramid network iefpn for detecting prohibited items in x ray security images paper https ieeexplore ieee org document 9674494 learning based image synthesis for hazardous object detection in x ray security applications paper https ieeexplore ieee org document 9552004 x ray security inspection image detection algorithm based on improved yolov4 paper https ieeexplore ieee org document 9645636 a yolov5s se model for object detection in x ray security images paper https ieeexplore ieee org document 9624606 prohibited items detection in x ray images in yolo network paper https ieeexplore ieee org document 9594145 automatic and robust object detection in x ray baggage inspection using deep convolutional neural networks paper https ieeexplore ieee org document 9209096 classify and localize threat items in x ray imagery with multiple attention mechanism and high resolution and high semantic features paper https ieeexplore ieee org document 9513650 raw data processing using modern hardware for inspection of objects in x ray baggage inspection systems paper https ieeexplore ieee org document 9417094 temporal fusion based mutli scale semantic segmentation for detecting concealed baggage threats paper https ieeexplore ieee org document 9658932 automatic threat detection using deep neural networks paper https ieeexplore ieee org document 9719550 towards real world x ray security inspection a high quality benchmark and lateral inhibition module for prohibited items detection paper https ieeexplore ieee org document 9710060 a novel incremental learning driven instance segmentation framework to recognize highly cluttered instances of the contraband items paper https arxiv org pdf 2201 02560 pdf deep fusion driven semantic segmentation for the automatic recognition of concealed contraband items paper https link springer com chapter 10 1007 2f978 3 030 73689 7 53 brittle features may help anomaly detection paper https arxiv org abs 2104 10453 deep learning based x ray baggage hazardous object detection an fpga implementation paper https www iieta org journals ria paper 10 18280 ria 350510 operationalizing convolutional neural network architectures for prohibited object detection in x ray imagery paper https arxiv org abs 2110 04906 towards automatic threat detection a survey of advances of deep learning within x ray security imaging paper https arxiv org abs 2001 01293 on the impact of using x ray energy response imagery for object detection via convolutional neural networks paper https arxiv org abs 2108 12505 towards real world prohibited item detection a large scale x ray benchmark paper https arxiv org pdf 2108 07020 pdf panda perceptually aware neural detection of anomalies paper https arxiv org abs 2104 13702 tensor pooling driven instance segmentation framework for baggage threat recognition paper https arxiv org abs 2108 09603 unsupervised anomaly instance segmentation for baggage threat recognition paper https arxiv org abs 2107 07333 symmetric triangle network for object detection within x ray baggage security imagery paper https ieeexplore ieee org document 9533991 anomaly detection in x ray security imaging a tensor based learning approach paper https ieeexplore ieee org document 9534034 automated threat objects detection with synthetic data for real time x ray baggage inspection paper https ieeexplore ieee org document 9533928 evaluating gan based image augmentation for threat detection in large scale xray security images paper https www mdpi com 2076 3417 11 1 36 an x ray image enhancement algorithm for dangerous goods in airport security inspection paper https ieeexplore ieee org document 9407728 metrics metrics baggage threat detection under extreme class imbalance paper https ieeexplore ieee org abstract document 9787472 detecting overlapped objects in x ray security imagery by a label aware mechanism paper https ieeexplore ieee org document 9722843 3d ct contraband materials detection within volumetric 3d computed tomography baggage security screening imagery paper https arxiv org abs 2012 11753 on the evaluation of semi supervised 2d segmentation for volumetric 3d computed tomography baggage security screening paper https breckon org toby publications papers wang21segmentation pdf slicenets a scalable approach for object detection in 3d ct scans paper https ieeexplore ieee org document 9423392 debisim a simulation pipeline for dual energy ct based baggage inspection systems paper https content iospress com articles journal of x ray science and technology xst200808 2020 sup top sup scroll paper 2d learning based material classification in x ray security images paper https www scitepress org link aspx doi 10 5220 0008951702840291 multi label x ray imagery classification via bottom up attention and meta fusion paper https openaccess thecvf com content accv2020 html hu multi label x ray imagery classification via bottom up attention and meta fusion accv 2020 paper html multi view object detection using epipolar constraints within cluttered x ray security imagery paper https breckon org toby publications papers isaac20multiview pdf occluded prohibited items detection an x ray security inspection benchmark and de occlusion attention module paper https arxiv org abs 2004 08656 trainable structure tensors for autonomous baggage threat detection under extreme occlusion paper https arxiv org abs 2009 13158 cascaded structure tensor framework for robust identification of heavily occluded baggage items from x ray scans paper https arxiv org abs 2004 06780 automatic threat detection in baggage security imagery using deep learning models paper https ieeexplore ieee org document 9342691 automatic threat detection in single stereo two and multi view x ray images paper https ieeexplore ieee org document 9342253 detecting prohibited items in x ray images a contour proposal learning approach paper https ieeexplore ieee org document 9190711 background adaptive faster r cnn for semi supervised convolutional object detection of threats in x ray images paper https arxiv org abs 2010 01202 x ray baggage inspection with computer vision a survey paper https ieeexplore ieee org document 9162101 data augmentation of x ray images in baggage inspection based on generative adversarial networks paper https ieeexplore ieee org document 9087880 3d ct multi class 3d object detection within volumetric 3d computed tomography baggage security screening imagery paper https arxiv org abs 2008 01218 on the evaluation of prohibited item classification and detection in volumetric 3d computed tomography baggage security screening imagery paper https arxiv org abs 2003 12625 a reference architecture for plausible threat image projection tip within 3d x ray computed tomography volumes paper https arxiv org abs 2001 05459 an approach for adaptive automatic threat recognition within 3d computed tomography images for baggage security screening paper https arxiv org abs 1903 10604 2019 sup top sup scroll paper 2d evaluating the transferability and adversarial discrimination of convolutional neural networks for threat object detection and classification within x ray security imagery paper https arxiv org abs 1911 08966 on the impact of object and sub component level segmentation strategies for supervised anomaly detection within x ray security imagery paper https arxiv org abs 1911 08216 using deep neural networks to address the evolving challenges of concealed threat detection within complex electronic items paper https breckon org toby publications papers bhowmik19electronics pdf on the use of deep learning for the detection of firearms in x ray baggage security imagery paper https breckon org toby publications papers gaus19firearms pdf the good the bad and the ugly evaluating convolutional neural networks for prohibited item detection using real and synthetically composite x ray imagery paper https arxiv org abs 1909 11508 evaluating a dual convolutional neural network architecture for object wise anomaly detection in cluttered x ray security imagery paper https breckon org toby publications papers gaus19anomaly pdf skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection paper https arxiv org abs 1901 08954 an evaluation of deep learning based object detection strategies for threat object detection in baggage security imagery paper https www sciencedirect com science article pii s016786551930011x deep convolutional neural network based object detector for x ray baggage security imagery paper https ieeexplore ieee org abstract document 8995335 automated firearms detection in cargo x ray images using retinanet paper https www spiedigitallibrary org conference proceedings of spie 10999 109990p automated firearms detection in cargo x ray images using retinanet 10 1117 12 2517817 full sso 1 toward automatic threat recognition for airport x ray baggage screening with deep convolutional object detection paper https arxiv org abs 1912 06329 unexpected item in the bagging area anomaly detection in x ray security images paper https ieeexplore ieee org document 8537982 limits on transfer learning from photographic image data to x ray threat detection paper https www semanticscholar org paper limits on transfer learning from photographic image caldwell griffin 7e7e445bbb757c4ec9165505925e48b0f94a92ad data augmentation for x ray prohibited item images using generative adversarial networks paper https ieeexplore ieee org document 8654640 modified adaptive implicit shape model for object detection paper https link springer com chapter 10 1007 978 3 030 36802 9 17 graph clustering and variational image segmentation for automated firearm detection in x ray images paper https digital library theiet org content journals 10 1049 iet ipr 2018 5198 semantic segmentation for prohibited items in baggage inspection paper https link springer com chapter 10 1007 978 3 030 36189 1 41 text semantic 20segmentation 20is 20a 20branch open 20the 20baggage 20for 20inspection application of machine learning methods for material classification with multi energy x ray transmission images paper https link springer com chapter 10 1007 978 3 030 24274 9 17 handgun detection in single spectrum multiple x ray views based on 3d object recognition paper https link springer com article 10 1007 s10921 019 0602 9 3d ct on the relevance of denoising and artefact reduction in 3d segmentation and classification within complex computed tomography imagery paper https breckon org toby publications papers mouton19relevance pdf 2018 sup top sup scroll paper 2d on using deep convolutional neural network architectures for automated object detection and classification within x ray baggage security imagery paper https breckon org toby publications papers akcay18architectures pdf ganomaly semi supervised anomaly detection via adversarial training paper https arxiv org abs 1805 06725 multi view x ray r cnn paper https arxiv org abs 1810 02344 a gan based image generation method for x ray security prohibited items paper https link springer com chapter 10 1007 978 3 030 03398 9 36 prohibited item detection in airport x ray security images via attention mechanism based cnn paper https link springer com chapter 10 1007 978 3 030 03335 4 37 convolutional neural networks for automatic threat detection in security x ray images paper https ieeexplore ieee org document 8614074 automatic threat recognition of prohibited items at aviation checkpoint with x ray imaging a deep learning approach paper https www spiedigitallibrary org conference proceedings of spie 10632 1063203 automatic threat recognition of prohibited items at aviation checkpoint with 10 1117 12 2309484 full websyncid 8531ab0d 3a6b 03c9 7c00 9b6bcd746b80 sessionguid d8e8abee aed6 ae05 c117 aac5a9199362 3d ct consensus relaxation on materials of interest for adaptive atr in ct images of baggage paper https www spiedigitallibrary org conference proceedings of spie 10632 106320e consensus relaxation on materials of interest for adaptive atr in 10 1117 12 2309839 full adaptive target recognition a case study involving airport baggage screening paper https arxiv org abs 1811 04772 earlier sup top sup scroll paper 2d an evaluation of region based object detection strategies within x ray baggage security imagery paper https breckon org toby publications papers akcay17region pdf on using feature descriptors as visual words for object detection within x ray baggage security screening paper https breckon org toby publications papers kundegorski16xray pdf transfer learning using convolutional neural networks for object classification within x ray baggage security imagery paper https breckon org toby publications papers akcay16transfer pdf improving feature based object recognition for x ray baggage security screening using primed visual words paper https breckon org toby publications papers turcsany13xray pdf a combinational approach to the fusion de noising and enhancement of dual energy x ray luggage images paper https ieeexplore ieee org document 1565297 improving weapon detection in single energy x ray images through pseudocoloring paper https ieeexplore ieee org abstract document 1715507 a review of x ray explosives detection techniques for checked baggage paper https www sciencedirect com science article pii s0969804312000127 a logarithmic x ray imaging model for baggage inspection simulation and object detection paper https ieeexplore ieee org document 8014771 automatic defect recognition in x ray testing using computer vision paper https ieeexplore ieee org document 7926702 modern computer vision techniques for x ray testing in baggage inspection paper https ieeexplore ieee org document 7775025 inspection of complex objects using multiple x ray views paper https ieeexplore ieee org document 6782468 automated x ray object recognition using an efficient search algorithm in multiple views paper https ieeexplore ieee org document 6595901 x ray testing by computer vision paper https ieeexplore ieee org document 6595900 automated detection in complex objects using a tracking algorithm in multiple x ray views paper https ieeexplore ieee org document 5981715 threat objects detection in x ray images using an active vision approach paper https link springer com article 10 1007 s10921 017 0419 3 object recognition in x ray testing using an efficient search algorithm in multiple views paper https www ingentaconnect com content bindt insight 2017 00000059 00000002 art00008 jsessionid 1n6a28jhds21c x ic live 02 modern computer vision techniques for x ray testing in baggage inspection paper https ieeexplore ieee org document 7775025 automated detection of threat objects using adapted implicit shape model paper https ieeexplore ieee org document 7123190 a review of x ray explosives detection techniques for checked baggage paper https www sciencedirect com science article pii s0969804312000127 explosives detection systems eds for aviation security paper https www sciencedirect com science article pii s0165168402003912 3d ct geometrical approach for the automatic detection of liquid surfaces in 3d computed tomography baggage imagery paper https breckon org toby publications papers chermak15liquids pdf materials based 3d segmentation of unknown objects from dual energy computed tomography imagery in baggage security screening paper https breckon org toby publications papers mouton15segmentation pdf object classification in 3d baggage security computed tomography imagery using visual codebooks paper https breckon org toby publications papers flitton15codebooks pdf 3d object classification in baggage computed tomography imagery using randomised clustering forests paper https breckon org toby publications papers mouton14randomised pdf investigating existing medical ct segmentation techniques within automated baggage and package inspection paper https breckon org toby publications papers megherbi13segmentation pdf radon transform based metal artefacts generation in 3d threat image projection paper https breckon org toby publications papers megherbi13radon pdf a comparison of 3d interest point descriptors with application to airport baggage object detection in complex ct imagery paper https breckon org toby publications papers flitton13interestpoint pdf a distance weighted method for metal artefact reduction in ct paper https breckon org toby publications papers mouton13mar pdf an experimental survey of metal artefact reduction in computed tomography paper https breckon org toby publications papers mouton13survey pdf an evaluation of ct image denoising techniques applied to baggage imagery screening paper https breckon org toby publications papers mouton13denoising pdf fully automatic 3d threat image projection application to densely cluttered 3d computed tomography baggage images paper https breckon org toby publications papers megherbi12tip pdf a comparison of classification approaches for threat detection in ct based baggage screening paper https breckon org toby publications papers megherbi12baggage pdf a novel intensity limiting approach to metal artefact reduction in 3d ct baggage imagery paper https breckon org toby publications papers mouton12mar pdf a 3d extension to cortex like mechanisms for 3d object class recognition paper https breckon org toby publications papers flitton12cortex pdf object recognition using 3d sift in complex ct volumes paper https breckon org toby publications papers flitton10baggage pdf a classifier based approach for the detection of potential threats in ct based baggage screening paper https breckon org toby publications papers megherbi10baggage pdf a review of automated image understanding within 3d baggage computed tomography security screening paper https breckon org toby publications papers mouton15review pdf a volumetric object detection framework with dual energy ct paper https ieeexplore ieee org document 4774641 exact reconstruction for dual energy computed tomography using an h l curve method paper https ieeexplore ieee org document 4179793 automatic segmentation of ct scans of checked baggage paper https www stratovan com sites default files automaticsegmentationofctscansofcheckedbaggage pdf automatic segmentation of unknown objects with application to baggage security paper https link springer com chapter 10 1007 978 3 642 33709 3 31 alert strategic studies paper joint metal artifact reduction and segmentation of ct images using dictionary based image prior and continuous relaxed potts model paper https ieeexplore ieee org document 7350909 using threat image projection data forassessing individual screener performance paper https www witpress com elibrary wit transactions on the built environment 82 15153 3d threat image projection paper https www spiedigitallibrary org conference proceedings of spie 6805 680508 3d threat image projection 10 1117 12 766432 full sso 1 learning based object identification and segmentation using dual energy ct images for security paper https ieeexplore ieee org document 7159062 | computer-vision x-ray-images machine-learning artificial-intelligence papers datasets x-ray computed-tomography object-detection classification tip cnn ct deep-learning gan segmentation anomaly-detection threat-image-projection anomaly | ai |
SUR-adapter | sur adapter github https img shields io github license gbup group dianet svg github https img shields io badge qrange 20 group orange by shanshan zhong https github com zhongshsh and zhongzhan huang https dedekinds github io and wushao wen https scholar google com citations user fsnlwy4aaaaj and jinghui qin https github com qinjinghui and liang lin http www linliang net this repository is the implementation of sur adapter enhancing text to image pre trained diffusion models with large language models paper https arxiv org abs 2305 05189 introduction semantic understanding and reasoning adapter sur adapter for pre trained diffusion models can acquire the powerful semantic understanding and reasoning capabilities from large language models to build a high quality textual semantic representation for text to image generation p align center img src https github com qrange group ras assets 62104945 af863827 2ea4 45cb b3ed 2f98ba0e7d03 p news 2023 10 20 we have provided an example checkpoint of sur adapter google drive https drive google com drive folders 1uyc9 aqtezmhxmj4dh0a 9rbkkx jmjz usp share link please try it 2023 08 19 we have provided the data scraping code for civitai please take a look at processing https github com qrange group sur adapter blob main data collect processing ipynb todo x data collection script x pretrain model dataset quick training we only provide one data sample for running through this repo with the following code see dataset declaration for details 1 clone the code sh git clone https github com qrange group sur adapter sh cd sur adapter 2 prepare the enviroment if pytorch is not installed you can install it through the official website guide https pytorch org get started locally for example when i use nvidia smi to know that my cuda version is 11 1 we can install pytorch through the following command sh pip3 install torch torchvision torchaudio index url https download pytorch org whl cu111 then install diffusers following the guide https huggingface co docs diffusers installation sh pip install diffusers torch finally install the relevant packages sh pip install r requirements txt 3 run the following code in shell where 0 is the gpu id if you encounter cuda out of memory you can try to find a solution in document https huggingface co docs diffusers v0 16 0 en optimization fp16 sh sh run sh 0 quick training only uses about 5200 mib gpu memory if your gpu memory is large enough you can increase the batch size or not use mixed precision the following is a description of the parameters of run sh the details can be found in sur adapter train py sh export cuda 1 gpu id export llm 13b size of llm export llm layer 39 layer of llm export model name runwayml stable diffusion v1 5 pre trained diffusion model export info test help to idetify the checkpoints export output dir fp16 help to idetify the checkpoints export train dir sur data small dataset export save step 100 step saved at intervals export batch size 1 batch size please see https huggingface co docs diffusers v0 16 0 en training text2image to get more details of training args cuda visible devices cuda accelerate launch sur adapter train py mixed precision fp16 info info pretrained model name or path model name dataset name train dir output dir output dir llm llm llm layer llm layer checkpointing steps save step train batch size batch size resolution 512 center crop random flip gradient accumulation steps 4 gradient checkpointing max train steps 5000 learning rate 1e 05 prompt weight 1e 05 llm weight 1e 05 adapter weight 1e 01 max grad norm 1 lr scheduler constant lr warmup steps 0 dataset declaration you can prepare the dataset in the format of sur data small for examples you can get data from civitai https civitai com through api https github com civitai civitai wiki rest api reference we have provided the data scraping code for civitai please take a look at processing https github com qrange group sur adapter blob main data collect processing ipynb if you have some problems you can try to find answers from datasets document https huggingface co docs datasets create dataset for more details warning the dataset surd proposed in our work is collected from lexica https lexica art license https lexica art license civitai https civitai com license https github com civitai civitai blob main license and stable diffusion online https stablediffusionweb com license https huggingface co spaces compvis stable diffusion license the licenses point out that if the dataset is used for commercial purposes there may be certain legal risks in order to avoid potential copyright disputes and unnecessary trouble we have decided not to publicly release surd and our ckpt if you have a need for the data please collect and clean it yourself if it is to be used for commercial purposes please contact the relevant website or author for authorization prompt2vec we utilize llama https github com facebookresearch llama a collection of foundation language models ranging from 7b to 65b parameters as knowledge distillation for large language models llms specifically we save the vector representations of simple prompts in i th layer of llms which serve as the text understanding to finetune diffusion models if you want to output the vectors from llama https github com facebookresearch llama we recommend that you can focus on following two lines https github com facebookresearch llama blob main llama model py l234 l235 of llama https github com facebookresearch llama python for layer in self layers h layer h start pos freqs cis mask the data format for prompt2vec is as follows prompt torch tensor when you are ready for prompt2vec s pt type file please save the pt file to the prompt2vec folder for example you can save the prompt vectors from the fortieth layers of llama 13b to prompt2vec 13b 39 pt inference run the demo ipynb python import os os environ cuda visible devices 0 from sur adapter pipeline import surstablediffusionpipeline import torch from sur adapter import adapter adapter path checkpoints runwayml fp16 test llm13b llml39 lr1e 05 llmw1e 05 promptw1e 05 adapterw0 1 adapter checkpoint1000 pt adapter adapter to cuda adapter load state dict torch load adapter path adapter adapter weight float adapter path split adapterw 1 split 0 model path runwayml stable diffusion v1 5 pipe surstablediffusionpipeline from pretrained model path adapter adapter pipe to cuda pipe safety checker lambda images clip input images false image pipe prompt an aristocratic maiden in medieval attire with a headdress of brilliant feathers images 0 image show citation article zhong2023adapter title sur adapter enhancing text to image pre trained diffusion models with large language models author zhong shanshan and huang zhongzhan and wen wushao and qin jinghui and lin liang journal arxiv preprint arxiv 2305 05189 year 2023 acknowledgments many thanks to huggingface https github com huggingface for their diffusers https github com huggingface diffusers for image generation task i love open source | adapter diffusion-models image-generation knowledge-distillation large-language-models pytorch | ai |
System-Design | system design for web apps working components of front end architecture code html5 wai aria css sass code standards and organization object oriented approach how do objects break down and get put together js frameworks organization performance optimization techniques asset delivery front end ops documentation onboarding docs styleguide pattern library architecture diagrams code flow tool chain testing performance testing visual regression unit testing end to end testing process git workflow dependency management npm bundler bower build systems grunt gulp deploy process continuous integration travis ci jenkins web app system design considerations security cors using cdn a content delivery network cdn is a system of distributed servers network that deliver webpages and other web content to a user based on the geographic locations of the user the origin of the webpage and a content delivery server this service is effective in speeding the delivery of content of websites with high traffic and websites that have global reach the closer the cdn server is to the user geographically the faster the content will be delivered to the user cdns also provide protection from large surges in traffic full text search using sphinx lucene solr which achieve fast search responses because instead of searching the text directly it searches an index instead offline support progressive enhancement service workers web workers server side rendering asynchronous loading of assets lazy load items minimizing network requests http2 bundling sprites etc developer productivity tooling accessibility internationalization responsive design browser compatibility distributed systems its 3 principles any system which includes multiple components residing on multiple machines coordinating through some sort of mechanism like sending messages to achieve a common goal is a distributed system single responsibility principle each component present in system design should have a single job to perform image https user images githubusercontent com 11299574 128066955 b91a4b63 7799 41a4 97a3 d8d190927d67 png no single point of failure principle the system should never have any component whose failure will result in the failure of the entire system image https user images githubusercontent com 11299574 128066991 5b0d72cc 76d9 4282 a212 f5bed865cde5 png no bottleneck principle since we design for scalability our system should not have performance bottlenecks ideally we should horizontally scale our system to handle the large amounts of processing image https user images githubusercontent com 11299574 128067041 6ba354c3 4772 42db 8089 3649d6c39083 png guide for system design 1 requirement analysis here we first discuss the functional requirements i e what this system should do then we discuss the non functional requirements i e how the system should behave 2 api design here we define the interfaces we expose to the outside world using which the communication happens here we have to define the name of the apis the parameters it takes and the return values 3 define data model it is a representation of object data its association between different objects and the rules it should follow 4 high level design this depicts the required component blocks with arrows showing the data flow the components can be load balancers web servers application servers databases caches or custom components performing some action like filtering reading processing etc the high level design shows how the data flows from an entry point to a database and vice versa 5 scale the design this is necessary because today s systems are generally large scale distributed systems to process large data we need to add multiple machines since it is difficult or rather impossible for one single machine to do everything you need to find all the bottlenecks and fix them concepts and components 1 scaling large enterprises have large amounts of data with increasing amounts of data the system should also grow to manage it effectively this process of growing or shrinking of the system to handle data is called scaling scaling of any system can be achieved by two ways vertical scaling vertical scaling means upgrading the hardware software of the existing system adding more power to it more ram cpu s and hdd but with vertical scaling there is always a limit a max beyond which it cannot go higher horizontal scaling horizontal scaling means adding multiple additional systems to improve the performance typically we might use several low end commodity hardware to save the cost these are some differences between them horizontal scaling might need load balancers resilient to system failures data inconsistency scales well vertical scaling load balancer is not necessary single point of failures data consistency has hardware limit for scaling image https user images githubusercontent com 11299574 128064677 17203c0a c37e 48de 88c5 2b0865ec9228 png 2 load balancing load balancer helps distribute incoming traffic across servers or databases there are 2 types of load balancers hardware and software hardware load balancers are generally expensive but very effective you can use multiple load balancers to avoid single point of failure they can be in active active mode or active passive mode following algorithms are largely used for load balancing purpose round robin least loaded session based hashing image https user images githubusercontent com 11299574 128064760 3cb9473c 93d0 45e6 9b9d a5de447d4911 png image https user images githubusercontent com 11299574 128064814 0046e8fb 950f 4930 a6c3 b056e241f0f6 png image https user images githubusercontent com 11299574 128064842 84353ee3 f75f 468e adaf 811e9b0aed5f png 3 caching caching is a concept where data is stored in fast memory for quick access the cache is generally a temporary storage like ram since it is faster than reading the data from hdd or a database there is a famous 80 20 rule which states that 20 of the data is accessed almost 80 of the time so generally we try to keep that 20 data in the cache cache invalidation write through cache data is written in cache and database at the same time write around cache data is written in database only cache is marked as invalid it is written later in cache write back cache data is written in cache only it is written later in db image https user images githubusercontent com 11299574 128065045 d69782c6 3dc6 4074 b31d 1b2e3b90038b png cache eviction least recently used lru most recently used mru first in first out fifo last in first out lifo least frequently used lfu random replacement rr image https user images githubusercontent com 11299574 128065084 6ce8880b 1c49 429f 8d4c 8a05079f4e87 png image https user images githubusercontent com 11299574 128065102 af84b434 d177 472f a967 e8e651d6c6b5 png distributed caching image https user images githubusercontent com 11299574 128065250 f6c3049b 7def 407a aa50 20e61ef0974a png 4 consistent hashing consistent hashing is one of the techniques used to bake in scalability into the storage architecture of your system in a distributed system consistent hashing helps in solving following scenarios to provide elastic scaling a term used to describe synamic adding removing of servers based on usage load for cache servers scale out a set of storage nodes like nosql databases 5 storage object store object store or object based storage is a storage architecture which manages data as objects unlike your regular hard disks which manages it as files in a file hierarchy system raid redundant array of independent disks raid is a way of storing the same data on multiple hard disks or solid state drives to protect it in case of failures there is a raid controller which handles all the operations and is responsible for improving performance while writing and protecting the data on multiple devices raid levels raid levels are nothing but different configurations there are 6 standard levels of raid from raid 0 to raid 5 then there are several combined raid levels like raid 10 and many non standard raid levels which companies have developed for their own use for the purpose of this section we will go through raid 0 to raid 5 and a combination raid 10 these are good to know topics for design interview you can ignore the other raid levels safely raid 0 striping image https user images githubusercontent com 11299574 128065290 f9f1b4c4 8eae 4925 9e33 c01cad07db53 png raid 1 mirroring image https user images githubusercontent com 11299574 128065321 1f1a95c9 4789 442a 88ce fac1381e95ee png raid 2 bit level striping with dedicated hamming code parity image https user images githubusercontent com 11299574 128065348 187a37ab 263f 477b 951c 00cad4ae359a png raid 3 byte level striping with a single parity disk image https user images githubusercontent com 11299574 128065365 27317db2 4d7e 4987 8782 f71cfecb8fa1 png raid 4 block level striping with a single parity disk image https user images githubusercontent com 11299574 128065388 d0e2b2d4 f525 4b4d a938 4e36894cd9b7 png raid 5 striping with distributed parity image https user images githubusercontent com 11299574 128065411 66e22a6c 3bf2 46b6 8deb 6d6094ae1a97 png raid 6 striping with double parity image https user images githubusercontent com 11299574 128065429 bce5dd8d 2557 4736 9220 18e76311c4d2 png raid 10 combining striping and mirroring image https user images githubusercontent com 11299574 128065470 3659ccbe e97a 42ed aa86 3436ee488ac8 png 6 database concepts a database is a collection of information that is organized so that it can be easily accessed managed and updated in this section you will get to know about various database concepts like cap theorem acid properties consistency patterns and sharding 3 guarantees of a distributed system consistency a guarantee that every node in a distributed cluster returns the same most recent successful write availability every non failing node returns a response for all read and write requests in a reasonable amount of time partition tolerant the system continues to function in spite of network partitions image https user images githubusercontent com 11299574 128065514 48e2a78f 58b8 4f40 831a 447f57214b29 png cap theorem cap theorem states that it is impossible for a distributed software system to simultaneously provide more than two out of three of the following guarantees cap consistency availability and partition tolerance while designing a distributed system we can pick up only 2 of the 3 options cap theorem is one of the parameters used when choosing the database for your system image https user images githubusercontent com 11299574 128065535 d27c9185 0978 410d b775 fa10d7024465 png acid properties atomicity consistency isolation durability we know that a transaction is a single logical unit of work consisting of one or more instructions which accesses and possibly modifies the contents of a database to maintain integrity of the database each transaction should be acid compliant database replication database replication is the process of copying data from one database to one or more databases consistency patterns distributed databases that rely on replication for high availability low latency or both we need to make a trade off between the read consistency vs throughput there are 2 general consistency levels supported by most distributed databases strong consistency eventual consistency replication architectures single leader architecture multi leader architecture no leader architecture image https user images githubusercontent com 11299574 128065634 377c71cf 2c2f 4332 8798 dee269575f79 png sharding sharding a database is horizontal partitioning a database factors to be considered while sharding data size performance latency cost ways to shard the data divide based on some column value divide based on some algorithm or hash function consistent hashing image https user images githubusercontent com 11299574 128065650 884973ff dea4 469a b0c2 d3fe8f776212 png nosql and it s types a nosql database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases there are 4 main nosql types 1 key value store as the name suggests the data is saved into a key value pair some of the most famous in this category are redis amazon s dynamo and voldemort 2 column oriented database traditional databases are row oriented all the columns of a row are stored together a column oriented stores data tables by column rather than by row some of the famous column oriented databases are cassandra and hbase 3 document based here we save the data into a form of documents these documents should have some schema either fixed or unfixed and it can be expressed in json format some examples are mongodb and couchdb 4 graph based when the data is highly interconnected and it can be depicted as nodes and edges we can use graph based databases the nodes can be entities and the edges can be relationships an example of a graph based database is neo4j 7 web servers a web server is a dedicated server whose functionality is to serve web requests only it serves html documents images css stylesheets and javascript files 8 application servers an application server or app server is used to host applications these applications generally are the business logic of the system an app server provides the environment both software and hardware to run them 9 architectural patterns 1 monolithic in monolithic pattern the entire business logic is composed in one single component 2 layered image https user images githubusercontent com 11299574 128063314 a77970ab f42d 4702 be88 6868e508d108 png in the layered pattern the components are grouped into logical layers each request will go through the layers performing some specific tasks 3 soa service oriented architecture image https user images githubusercontent com 11299574 128063248 9f0729e8 614f 4896 930c cacd75a4e841 png soa defines a way of making components reusable via service interfaces in this pattern each component is actually a service performing some specific functionality 4 microservices architecture image https user images githubusercontent com 11299574 128063274 3bfc9b7b a4a2 43f0 a7e8 3a1a97a337a0 png microservices architecture is a style which allows building an application as a collection of small independent services each independent service is called a microservice some features of microservice are highly maintainable and testable loosely coupled independently deployable organized around business capabilities i e single responsibility principle owned by a small team most microservices based systems have some common components api gateway service discovery service management 10 message queues image https user images githubusercontent com 11299574 128063523 e8c598fd 3592 4c55 8a0e b44320307d8a png message queues are typically used to provide asynchronous communication between components in a system we can divide the message queue into parts where each part serves a specific purpose messages belonging to some particular category can go into its respective part some examples of the products which provide message queues are kafka rabbitmq and azure service bus 11 communication models and protocols communication models push here sender actively sends the messages to receiver whenever a message is available pull here the receiver asks sender if a message is present if it is present it pulls the message communication protocols http image https user images githubusercontent com 11299574 128063728 9e3a888f 145a 4fe2 a568 59d5e89fb45f png long polling image https user images githubusercontent com 11299574 128063775 ea0171d7 0948 44fc 9b5b 20332454db08 png websockets image https user images githubusercontent com 11299574 128063835 50f84831 9593 4154 a428 0cf5634bac53 png 12 security basic concepts authentication authorization encryption integrity symmetric encryption asymmetric encryption certificate certification authority ca cross site scripting xss denial of service dos distributed denial of service ddos 13 content delivery network cdn a content delivery network cdn refers to a geographically distributed group of servers which work together to provide fast delivery of internet content 14 back of the envelope calculations back of the envelop estimations means the quick calculations you do on back of the envelop or on paper question estimate how many servers we need to serve read requests considering each server is capable of handling 7 requests per second we have to serve a total traffic of 46 million per day answer 7 requests per second 7 60 420 requests per minute 420 60 requests per hour 400 60 24000 per hour 24k per hour 24k 24 requests per day 30k 20 600k requests per day per server we want to handle 46 million requests per day which means 46m 600k approx 45m 500k 45m 0 5m 45 2 90 servers question given 46 servers and 46 million requests per day how much data does each server process per second answer so 1 server will have 1 million requests per day 1 server will have 1 million 24 requests per hour 1000000 24 1000k 24 1000k 25 40k requests per hour 1 server will have 40k 60 requests per minute 4k 6 4000 6 3600 6 600 requests per minute 1 server will have 600 60 requests per second 60 6 10 requests per second cheat sheet 1 bit 1 byte 8 bits 1 kilobyte kb 1024 bytes 1000 bytes 1 megabyte mb 1024 kb 1000 kb 1 gigabyte gb 1024 mb 1000 mb 1 terabyte tb 1024 gb 1000 gb 1 petabyte pb 1024 tb 1000 tb 1 exabyte eb 1024 pb 1000 pb kb vs kb serial communications and read write devices use kb kilo bits for data transfer ratings whereas storage devices use kb kilo byte to rate their capacity 1 kb kilobyte 1024 bytes 1024 8 bits 8196 bits 1 kb kilobits 1024 bits 1024 8 bytes 128 bytes key questions to be known before designing a system 1 concurrency do you understand threads deadlock and starvation do you know how to parallelize algorithms do you understand consistency and coherence 2 networking do you roughly understand ipc and tcp ip do you know the difference between throughput and latency and when each is the relevant factor 3 abstraction you should understand the systems you re building upon do you know roughly how an os file system and database work do you know about the various levels of caching in a modern os 4 real world performance you should be familiar with the speed of everything your computer can do including the relative performance of ram disk ssd and your network 5 estimation estimation especially in the form of a back of the envelope calculation is important because it helps you narrow down the list of possible solutions to only the ones that are feasible then you have only a few prototypes or micro benchmarks to write 6 availability reliability are you thinking about how things can fail especially in a distributed environment do know how to design a system to cope with network failures do you understand durability solid introduction solid is a set of five design principles intended to make software designs more understandable flexible and maintainable this was theorized by robert c martin famously known as uncle bob in his paper published in the year 2000 1 single responsibility principle srp every software component should have one responsibility every software component should have one reason to change cohesion cohesion refers to the degree to which the elements inside a module belong together coupling coupling is the degree of interdependence between software modules perspective 1 method level 2 class level 3 package level 4 system design level benefits 1 easy to change behaviour 2 very helpful in debugging 3 easy to understand the code 4 easy to write tests 5 less maintenance below code does not follow srp class userregistrationservice public void registeruser string username string emailid create a sql connection insert the username into database close the connection send a welcome email to the user below code follows srp class databaseservice public void createconnection public void insert public void closeconnection class emailservice public void sendemail class userregistrationservice public void registeruser string username string emailid databaseservice insert username emailid emailservice sendemail emailid 2 open closed principle ocp software entities classes modules functions etc should be open for extension but closed for modification ocp encourages the use of polymorphism also a way to implement ocp is using strategy design pattern below is an example for a bad design interface operation class addition implements operation int a int b int result constructor getters and setters class subtraction implements operation int a int b int result constructor getters and setters class calculator public void calculate operation operation null checks if operation instanceof addition addition addition addition operation addition setresult addition geta addition getb else if operation instanceof subtraction subtraction subtraction subtraction subtraction subtraction setresult subtraction geta subtraction getb polymorphism 1 method overloading void add int a int b void add int a int b int c 2 method overriding interface operation void perform class addition implements operation override void perform some code class subtraction implements operation override void perform some code below is an example of design following ocp interface operation void perform class addition implements operation int a int b int result constructor getters and setters public void perform result a b class subtraction implements operation int a int b int result constructor getters and setters public void perform result a b class multiplication implements operation int a int b int result constructor getters and setters public void perform result a b class calculator public void calculate operation operation null checks operation perform benefits suppose clients are using existing class and if we modify that class there is a chance of introduction of bugs in the existing features so it is better to create a new class so that existing features are untouched | system-design system-design-project database scalability system-design-template | os |
DeepNLP | deepnlp reference repository for the course deep natural language processing politecnico di torino lab practices 2021 10 06 lab 1 text processing and topic modeling 2021 10 20 lab 2 word sentence embeddings 2021 11 03 lab 3 ir recommendation systems 2021 11 10 lab 4 entity recognition and intent detection 2021 11 24 lab 5 text summarization 2021 12 01 lab 6 machine translation | ai |
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MassTrainingBatch4 | mass training batch 4 web and hybrid mobile application development | front_end |
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FreeRTOS-RISCV | freertos for risc v codacy badge https app codacy com project badge grade ecad86ff13ea4750814d0c717d452cd6 https www codacy com gh kuopinghsu freertos riscv dashboard utm source github com amp utm medium referral amp utm content kuopinghsu freertos riscv amp utm campaign badge grade this is an example to test a href https github com kuopinghsu srv32 srv32 a on freertos build git clone https github com kuopinghsu freertos riscv cd freertos riscv make folder lists folder file description freertos posix freertos posix pthread source freertos kernel freertos kernel source demo examples perf c performance test demo examples pi pthread c pi pthread example demo examples pthread c pthread example demo examples queue c queue example demo examples sem c semaphore example demo examples task c task creation example demo hartstone hartstone benchmark run requirement install the toolchains see details in srv32 a href https github com kuopinghsu srv32 building toolchains building toolchains a section clone a href https github com kuopinghsu srv32 srv32 a and copy the freertos s demo to srv32 sw folder the demo can be run in the rtl and iss simultor of srv32 see details in a href https github com kuopinghsu srv32 freertos support freertos support a of srv32 | os |
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FreeRTOS_RH850 | freertos ported to rh850 this provides a very basic port of freertos to rh850 requirement 1 gcc https github com mikisama auto build gcc rh850 releases or iar https www iar com products architectures renesas iar embedded workbench for renesas rh850 or ghs https www ghs com products v850 development html or ccrh https www renesas com eu en software tool c compiler package rh850 family 2 cmake https github com kitware cmake releases 3 ninja https github com ninja build ninja releases 4 rfp https www renesas com us en software tool renesas flash programmer programming gui how to build details summary build with gcc summary add toolchain path to the environment path variable bash set the path environment variables in windows powershell env path d v850 elf gcc win32 x64 bin env path c program files x86 renesas electronics programming tools renesas flash programmer v3 08 v850 elf gcc version rfp cli version build command bash git clone https github com mikisama freertos rh850 cd freertos rh850 build cmake dcmake toolchain file cmake gcc cmake dcmake build type debug gninja ninja build docs gcc build webp details details summary build with iar summary add toolchain path to the environment path variable bash set the path environment variables in windows powershell env path c program files x86 iar systems embedded workbench 8 1 rh850 bin env path c program files x86 renesas electronics programming tools renesas flash programmer v3 08 iccrh850 version rfp cli version build command bash git clone https github com mikisama freertos rh850 cd freertos rh850 build cmake dcmake toolchain file cmake iar cmake dcmake build type debug gninja ninja build docs iar build webp details details summary build with ghs summary add toolchain path to the environment path variable bash set the path environment variables in windows powershell env path c ghs comp 201815 env path c program files x86 renesas electronics programming tools renesas flash programmer v3 08 ccrh850 version dummy rfp cli version build command bash git clone https github com mikisama freertos rh850 cd freertos rh850 build cmake dcmake toolchain file cmake ghs cmake dcmake build type debug gninja ninja build docs ghs build webp details details summary build with ccrh summary add toolchain path to the environment path variable bash set the path environment variables in windows powershell env path c program files x86 renesas electronics cs cc cc rh v2 04 00 bin env path c program files x86 renesas electronics programming tools renesas flash programmer v3 08 ccrh v rfp cli version build command bash git clone https github com mikisama freertos rh850 cd freertos rh850 build cmake dcmake toolchain file cmake ccrh cmake dcmake build type debug gninja ninja build docs ccrh build webp details documentation https github com mikisama freertos rh850 wiki | freertos rh850 | os |
cs193p-Fall-2017 | stanford engineering cs193p developing ios 11 apps with swift 4 art itunesu jpg 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 11 and swift 4 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 prerequisites c language and object oriented programming experience exceeding programming abstractions https see stanford edu course cs106b level and completion of programming paradigms https see stanford edu course cs107 lectures lecture slides source video date 1 overview of ios slides lecture 1 slides pdf art play png raw true https www youtube com watch v z9ixfyhhkyi index 1 list pl l7vs8vbndfbikil3feqhkkxtysncsvn september 25 2017 2 model view controller mvc slides lecture 2 slides pdf art play png raw true https www youtube com watch v 4igdu4iwmfc index 2 list pl l7vs8vbndfbikil3feqhkkxtysncsvn september 27 2017 fl1 debugging xcode tips tricks art play png raw true https www youtube com watch v 7cexddgjsvu index 19 list pl l7vs8vbndfbikil3feqhkkxtysncsvn september 29 2017 3 swift slides lecture 3 slides pdf art play png raw true https www youtube com watch v 88husjydcwy index 3 list pl l7vs8vbndfbikil3feqhkkxtysncsvn october 2 2017 4 protocols closures slides lecture 4 slides pdf art play png raw true https www youtube com watch v rgmkmhy ewe list pl l7vs8vbndfbikil3feqhkkxtysncsvn index 4 october 4 2017 5 drawing in ios slides lecture 5 slides pdf art play png raw true https www youtube com watch v poo0pz0gplk list pl l7vs8vbndfbikil3feqhkkxtysncsvn index 5 october 9 2017 6 multitouch multiple mvcs slides lecture 6 slides pdf art play png raw true https www youtube com watch v n pynplrhys index 6 list pl l7vs8vbndfbikil3feqhkkxtysncsvn october 11 2017 7 multiple mvcs timer animation slides lecture 7 slides pdf art play png raw true https www youtube com watch v diihwsxosdk index 7 list pl l7vs8vbndfbikil3feqhkkxtysncsvn october 16 2017 8 animation slides lecture 8 slides pdf art play png raw true https www youtube com watch v 5w9lu9abjze index 8 list pl l7vs8vbndfbikil3feqhkkxtysncsvn october 18 2017 fl2 github source code workflow art play png raw true https www youtube com watch v p8gyk aunk list pl l7vs8vbndfbikil3feqhkkxtysncsvn index 18 october 20 2017 9 view controller lifecycle scroll view slides lecture 9 slides pdf art play png raw true https www youtube com watch v qjrmau1wmmu index 9 list pl l7vs8vbndfbikil3feqhkkxtysncsvn october 23 2017 10 multithreading autolayout slides lecture 10 slides pdf art play png raw true https www youtube com watch v u1g8f6f3pyq list pl l7vs8vbndfbikil3feqhkkxtysncsvn index 10 october 25 2017 fl3 instruments art play png raw true https www youtube com watch v bcnlw9rhee0 list pl l7vs8vbndfbikil3feqhkkxtysncsvn index 20 october 27 2017 11 drag and drop uitableview uicollectionview slides lecture 11 slides pdf art play png raw true https www youtube com watch v hore835 mj4 list pl l7vs8vbndfbikil3feqhkkxtysncsvn index 11 october 30 2017 12 emoji art demo uitextfield slides lecture 12 slides pdf art play png raw true https www youtube com watch v qcj79tknk1i index 12 list pl l7vs8vbndfbikil3feqhkkxtysncsvn november 1 2017 13 emoji art demo persistence slides lecture 13 slides pdf art play png raw true https www youtube com watch v 9o nsiichpg list pl l7vs8vbndfbikil3feqhkkxtysncsvn index 13 november 6 2017 14 more about documents demo slides lecture 14 slides pdf art play png raw true https www youtube com watch v zkhcllza es index 14 list pl l7vs8vbndfbikil3feqhkkxtysncsvn november 8 2017 15 alert and action sheet notifications kvo application lifecycle slides lecture 15 slides pdf art play png raw true https www youtube com watch v bjlrcnev88k list pl l7vs8vbndfbikil3feqhkkxtysncsvn index 15 november 13 2017 16 segues modal popover unwind embed slides lecture 16 slides pdf art play png raw true https www youtube com watch v nk kg294hrc list pl l7vs8vbndfbikil3feqhkkxtysncsvn index 16 november 15 2017 17 core motion camera slides lecture 17 slides pdf art play png raw true https www youtube com watch v ccg0qoszixa index 17 list pl l7vs8vbndfbikil3feqhkkxtysncsvn november 29 2017 reading assignments reading name 1 reading 1 intro to swift reading reading 1 intro to swift pdf 2 reading 2 more swift reading reading 2 more swift pdf 3 reading 3 finishing off swift reading reading 3 finishing off swift pdf problem sets ps name 1 assignment 1 concentration problemsets programming project 1 concentration pdf 2 assignment 2 set problemsets programming project 2 set pdf 3 assignment 3 graphical set problemsets programming project 3 graphical set pdf 4 assignment 4 animated set problemsets programming project 4 animated set pdf 5 assignment 5 image gallery problemsets programming project 5 image gallery pdf 6 assignment 6 persistent image gallary problemsets programming project 6 persistent image gallery pdf licensing my cs193p projects are licensed under the mit license license support or contact visit ddapps co http ddapps co to see more | swift4 ios11 engineering school iphone stanford | os |
blockchain-trace-fabric | fabric gitee star pc h5 https gitee com ken xue blockchain aptrace fabric raw master install fabric env e4 b8 9a e5 8a a1 e6 9e b6 e6 9e 84 png blockchain trace bcnetwork blockchain trace applets blockchain trace pc blockchain trace basic data blockchain trace bcnetwork blockchain trace applets blockchain trace pc pc ruoyi vue blockchain trace basic data ruoyi vue js element ui mpvue springboot mybatis fastdfs node js redis mysql fabric1 2 golang ubuntu16 04 64 2 4g 2g docker docker compose solo orderer fabric 1 node js 12 docker docker compose redis fastdfs mysql8 go 2 docker pull docker pull hyperledger fabric peer 1 2 0 docker pull hyperledger fabric orderer 1 2 0 docker pull hyperledger fabric ca 1 2 0 docker pull hyperledger fabric tools 1 2 0 docker pull hyperledger fabric ccenv 1 2 0 docker pull hyperledger fabric baseimage 0 4 10 docker pull hyperledger fabric baseos 0 4 10 docker pull hyperledger fabric couchdb 0 4 10 tag docker tag hyperledger fabric peer 1 2 0 hyperledger fabric peer docker tag hyperledger fabric orderer 1 2 0 hyperledger fabric orderer docker tag hyperledger fabric ca 1 2 0 hyperledger fabric ca docker tag hyperledger fabric tools 1 2 0 hyperledger fabric tools docker tag hyperledger fabric ccenv 1 2 0 hyperledger fabric ccenv docker tag hyperledger fabric baseimage 0 4 10 hyperledger fabric baseimage docker tag hyperledger fabric baseos 0 4 10 hyperledger fabric baseos docker tag hyperledger fabric couchdb 0 4 10 hyperledger fabric couchdb 3 linux git blockchain trace bcnetwork 4 basic network start sh chmod r 777 start sh start sh 5 webapp start sh webapp sh chmod r 777 startfarmercc sh 6 npm install npm install node node8 7 node enrolladmin js node registeruser js node registeruser js hfc key store npm install node node12 8 node node fabric node app js pm2 pm2 start app js pm2 stop app js blockchain trace basic data springboot 1 application yml redis fastdfs yaml redis redis host 127 0 0 1 6379 port 6379 password fdfs so timeout 1501 connect timeout 601 thumb image width 60 height 60 tracker list 127 0 0 1 22122 address http 127 0 0 1 8888 2 application druid yml mysql yaml spring datasource type com alibaba druid pool druiddatasource driverclassname com mysql cj jdbc driver druid master url jdbc mysql 127 0 0 1 3306 blockchain useunicode true characterencoding utf8 zerodatetimebehavior converttonull usessl true servertimezone gmt 2b8 username root password root pc blockchain trace pc 1 npm install registry https registry npm taobao org 2 main js ip yaml vue prototype httpurl http localhost 8080 route 3 npm run dev blockchain trace applets fabric api fabric fabric fabric api table tr td img src https images gitee com uploads images 2021 0510 100450 865a1f55 4775150 png td td img src https images gitee com uploads images 2021 0510 100501 6d258226 4775150 png td tr table table tr td img src https images gitee com uploads images 2021 0510 095409 1656ec9f 4775150 png td td img src https images gitee com uploads images 2021 0510 095812 c090b8eb 4775150 png td tr table table tr td img src https images gitee com uploads images 2021 0510 095938 b91c4a9e 4775150 png td td img src https images gitee com uploads images 2021 0510 100000 d3f09618 4775150 png td tr table table tr td img src https images gitee com uploads images 2021 0510 100223 1c37229e 4775150 png td td img src https images gitee com uploads images 2021 0510 101525 8ec61394 4775150 png td td img src https images gitee com uploads images 2021 0510 101537 479fa381 4775150 png td tr table https images gitee com uploads images 2021 0510 101838 07d8b55c 4775150 png 3 png table tr td img src https images gitee com uploads images 2021 0510 104012 a3d0a8f3 4775150 png td td img src https images gitee com uploads images 2021 0510 104029 3b281f09 4775150 png td td img src https images gitee com uploads images 2021 0510 104040 db0dd517 4775150 png td tr tr td img src https images gitee com uploads images 2021 0510 104051 ded404ae 4775150 png td td img src https images gitee com uploads images 2021 0510 104059 b6383ed4 4775150 png td td img src https images gitee com uploads images 2021 0510 104107 7bc0ed1e 4775150 png td tr table id https images gitee com uploads images 2021 0510 104115 bad29fb0 4775150 png 7 png pc table tr td img src https images gitee com uploads images 2021 0707 152117 9beee42b 4775150 png td td img src https images gitee com uploads images 2021 0707 152129 8e55e697 4775150 png td td img src install fabric env static img png td tr table id table tr td img src https images gitee com uploads images 2021 0510 234302 29fe611b 4775150 png td td img src https images gitee com uploads images 2021 0510 234335 43fbec55 4775150 png td td img src https images gitee com uploads images 2021 0510 234347 e2572d95 4775150 png td tr tr td img src https images gitee com uploads images 2021 0510 234359 b5bac058 4775150 png td td img src https images gitee com uploads images 2021 0510 234413 33dd3e47 4775150 png td td img src https images gitee com uploads images 2021 0510 234428 bc064965 4775150 png td tr table https img2023 cnblogs com blog 1524840 202303 1524840 20230331091313784 1518799671 png | blockchain-technology | blockchain |
NaturalLanguageProcessing | this is mainly for codes of nlp tasks cyk http f dataguru cn thread 693052 1 1 html pyspark thumbs up sentiment classification using machine learning techniques by bo pang lillian lee and shivakumar vaithyanathan textrank | ai |
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visitorbadge io api visitors path https 3a 2f 2fgithub com 2fdrshahizan 2fspecial topic data engineering labelcolor 23d9e3f0 countcolor 23697689 style flat don t forget to hit the star if you like this repo special topic data engineering course synopsis this course presents to the students recent research and industrial issues pertaining to data engineering database systems and technologies various topics of interests that are directly or indirectly affecting or are being influenced by data engineering database systems and technologies are explored and discussed participation in forums as well as face to face interaction with researchers and practitioners on these topics are encouraged students should then be able to conduct their own investigation and deductions this course will also expose students to industry s experiences in managing database systems and technologies through sharing knowledge sessions and work based learning activities with selected organization important things 1 course information https github com drshahizan special topic data engineering blob main materials ci scsp3843 stde 22232 pdf 2 lecture notes https drive google com drive folders 1ruriogmzy 6cl n0pgcjo2ymejny74s1 usp sharing 3 task 1 additional notes materials task1 md 4 carry marks images st cm pdf project description of the project s topic materials mongodb mongodb ds md project and group names materials project group md guideline for data science proposal materials project guidelines md classification and visualization of twitter sentiment analysis towards online food delivery services in malaysia images shahirah pdf railway ticket analytics for business insights images asyikin pdf finance performance analytics for business insights images nurfatihah pdf notes no module description notes 1 data engineer data engineering data science data scientist a data engineer focuses on designing building and maintaining data systems and infrastructure data engineering involves the processes and tools used to transform and store data data science involves analyzing and interpreting data to extract insights while a data scientist applies statistical and machine learning techniques for predictive modeling and decision making a href https github com drshahizan special topic data engineering blob main materials notes mod1 md img src images markdownp png width 24px height 24px a 2 application programming interface api materials api an application programming interface api is a set of rules and protocols that allows different software applications to communicate and interact with each other it defines the methods data formats and authentication mechanisms for accessing and manipulating functionality or data provided by a service or platform a href https github com drshahizan special topic data engineering blob main materials notes mod7 md img src images markdownp png width 24px height 24px a 3 data scraping materials data scraping data scraping refers to the automated extraction of data from websites or online sources it involves using specialized tools or scripts to gather and retrieve specific information such as text images or structured data for analysis or other purposes a href https github com drshahizan special topic data engineering blob main materials notes mod4 md img src images markdownp png width 24px height 24px a 4 django https github com drshahizan learn django django is a free and open source web framework for building web applications using the python programming language it follows a model view controller mvc architecture and provides an easy to use object relational mapping orm layer for interacting with databases django comes with a wide range of built in features including authentication url routing template rendering and more which makes it a popular choice for building scalable and maintainable web applications 5 data integration materials data integration data integration in data science refers to the process of combining and merging data from multiple sources into a unified and consistent format it involves transforming cleaning and integrating diverse data to create a comprehensive dataset for analysis and modeling purposes a href https github com drshahizan special topic data engineering blob main materials notes mod2 md img src images markdownp png width 24px height 24px a 6 types of data nosql database materials mongodb types of data include structured semi structured and unstructured data mongodb is a popular nosql database that uses a flexible document model allowing for storage and retrieval of data in json like documents making it suitable for handling diverse data types and flexible schema designs a href https github com drshahizan special topic data engineering blob main materials notes mod3 md img src images markdownp png width 24px height 24px a 7 data wrangling data wrangling also known as data cleaning or data preprocessing is the process of cleaning transforming and preparing raw data for analysis this involves identifying and addressing issues such as missing or inconsistent data formatting errors and duplicates data wrangling tools automate this process and can be used to streamline data cleaning and preparation tasks some popular data wrangling tools include openrefine trifacta datawrangler knime and talend these tools provide a range of features and capabilities such as the ability to handle large datasets automate data cleaning tasks and visualize data for exploration and analysis data wrangling is an essential step in the data analysis process as it helps to ensure that the data is accurate consistent and relevant for analysis a href https github com drshahizan special topic data engineering blob main materials notes mod5 md img src images markdownp png width 24px height 24px a 8 feature engineering feature engineering is the process of selecting creating and transforming variables or features in a dataset to improve the performance of a machine learning model this involves identifying relevant variables transforming variables to make them more useful and creating new variables that capture important information feature engineering tools automate this process and can be used to streamline feature selection and creation tasks some popular feature engineering tools include featuretools tpot automl and h2o ai these tools provide a range of features and capabilities such as the ability to automate feature selection and creation identify important variables and optimize feature pipelines for machine learning models feature engineering is an important step in the machine learning process as it helps to ensure that the model is able to learn from relevant data and make accurate predictions a href https github com drshahizan special topic data engineering blob main materials notes mod6 md img src images markdownp png width 24px height 24px a 9 artificial intelligence vs machine learning vs deep learning artificial intelligence machine learning and deep learning are all related to the field of computer science and are focused on enabling computers to learn and make decisions based on data artificial intelligence involves building systems that can perform tasks that typically require human intelligence such as language understanding decision making and problem solving machine learning is a subset of ai that focuses on building algorithms that can learn patterns and make decisions based on data without being explicitly programmed deep learning is a subset of machine learning that utilizes neural networks with multiple layers to learn and identify patterns in data overall all three fields involve leveraging data to build intelligent systems that can learn from experience and make decisions based on that learning a href https github com drshahizan special topic data engineering blob main materials notes mod8 md img src images markdownp png width 24px height 24px a 10 visualization data visualization is the process of representing data graphically to help people understand and make sense of complex data visualization tools in data science allow users to create visual representations of data such as charts graphs and maps that can be easily interpreted and analyzed some popular data visualization tools include tableau power bi google data studio and d3 js these tools provide a range of features and capabilities such as the ability to create interactive dashboards explore data in real time and collaborate with others on visualizations data visualization is an important part of the data analysis process as it helps to uncover patterns trends and insights in the data that might not be apparent from raw data alone a href https github com drshahizan special topic data engineering blob main materials notes mod9 md img src images markdownp png width 24px height 24px a 11 aws aws academy is a global program that offers educational institutions a comprehensive curriculum focused on cloud computing using amazon web services aws the program provides institutions with access to up to date learning materials hands on labs and assessments to teach students about various cloud computing topics a href materials aws img src images aws svg width 24px height 24px a student information materials student md woman man visual studio code img src https github com drshahizan learn php blob main images youtube64 png width 24px height 24px a video visual studio code web dev setup in 6 minutes https www youtube com watch v 4nfffsqc77m visual studio code html css js tips https www youtube com watch v bjiizz8mfms top 10 best vs code extensions https www youtube com watch v gpv sbfumjw how to setup live server in vs code https www youtube com watch v y4qqqeudcbq how to setup and use github with visual studio code 2023 https youtu be mr9jhyd3bni how to host a website on github 2023 github pages site https youtu be 6gkwy56vn2c useful links download visual studio code https code visualstudio com download vs code getting started https code visualstudio com docs collaborate with live share https code visualstudio com learn collaboration live share install and sign in to live share in visual studio code https learn microsoft com en us visualstudio liveshare use install live share visual studio code marketplace live share extension pack https marketplace visualstudio com items itemname ms vsliveshare vsls vs 2022 vs code live share how to use liveshare in vs code for live online collaboration quick guide https www youtube com watch v wog5xnqegy0 github desktop https desktop github com udemy beginner vs code https www udemy com course beginner vs code tools diagrams are visual representations of information or data that help convey complex concepts processes or systems in a clear and concise manner flowcharts are diagrams that use shapes and arrows to illustrate the steps in a process or algorithm more info https github com drshahizan software engineering blob main materials tools md no tools file 1 figma a href https github com drshahizan software engineering blob main materials figma md img src images figma svg width 24px height 24px a 2 draw io a href https github com drshahizan software engineering blob main materials uml drawio 1 draw io md img src images drawio svg width 24px height 24px a 3 github pages a href https github com drshahizan learn github blob main materials pages md img src images github svg width 24px height 24px a 4 behance a href https www behance net img src images behance svg width 24px height 24px a 5 visual studio code a href https code visualstudio com img src images vsc svg width 24px height 24px a 6 bootstrap studio a href https bootstrapstudio io img src images bootstrap studio png width 24px height 24px a submission no topic file 1 proposal a href project proposal img src images task png width 24px height 24px a 2 application programming interface api a href assignment api submission readme md img src images api svg width 24px height 24px a 3 data scraping a href assignment data scraping submission img src images scraping svg width 24px height 24px a 4 django a href https github com drshahizan learn django blob main materials assignment submission img src images django svg width 24px height 24px a 5 mongodb a href materials mongodb submission readme md img src images mongodb svg width 24px height 24px a 6 aws certification a href materials aws submission img src images aws svg width 24px height 24px a 7 final project a href project readme md img src images meeting png width 36px height 36px a contribution please create an issue https github com drshahizan special topic data engineering issues for any improvements suggestions or errors in the content you can also contact me using linkedin https www linkedin com in drshahizan for any other queries or feedback visitors https api visitorbadge io api visitors path https 3a 2f 2fgithub com 2fdrshahizan labelcolor 23697689 countcolor 23555555 style plastic https visitorbadge io status path https 3a 2f 2fgithub 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umbra-protocol | div align center img width 400 src readme umbra logo words png alt umbra logo br br div p align center b privacy preserving stealth payments for evm blockchain networks b p p align center a href https app umbra cash app umbra cash a a href https twitter com umbracash umbracash a a href https discord com invite uw4y5j2p7c discord a a href https explorer gitcoin co round 1 0x12bb5bbbfe596dbc489d209299b8302c3300fa40 0x12bb5bbbfe596dbc489d209299b8302c3300fa40 22 gitcoin a a href https twitter com scopelift scopelift a p div align center img width 150 src readme ethereum badge light png alt umbra logo br div about umbra is a protocol for stealth payments on evm blockchain networks it enables privacy preserving transactions where the receiver s identity is only known to the sender and receiver div align center br img width 650 src readme umbra diagram png alt diagram showing how umbra provides privacy div faq what is umbra umbra is a stealth address protocol for evm networks that means it allows a payer to send funds to a fresh address that address is controlled by the intended receiver but only the payer and the receiver know that one way to think of umbra is this before anyone sent you funds you sent them a brand new never before used address only the sender would know you control that address which adds a layer of privacy to your payment payments via umbra work similarly but are non interactive you don t need to give someone a fresh address the sender can just generate one they know only you will be able to access can you walk me through an example alice owns a business and hires bob to subcontract for her she agrees to pay bob 1 000 dai week for his work bob owns the ens address bob eth if alice sent the funds each week to bob eth anyone looking at the chain could trivially know that alice is paying bob 1 000 dai each week instead bob and alice will use umbra for private payments the first time bob visits the umbra app he sets up his account enabling anyone to privately pay him via umbra using the name bob eth or his normal ethereum address alice then uses umbra to send 1 000 dai to bob each week she only needs to know his ens name on chain we see alice pays 1 000 dai to a new empty address each week behind the scenes bob controls the keys to each of these addresses via umbra but nobody except alice and bob knows this bob uses umbra to withdraw his 1 000 dai each week he only needs to provide an address to send it to it s best for him to use an address that s not tied to his identity he usually chooses to send it straight to an exchange where he sells it for fiat as needed importantly this means bob s exchange now knows this payment went to him to the casual chain observer one without access to proprietary centralized exchange data the fact that alice s payment went to bob is obscured consider another example liza runs a website that asks for donations if everyone donated by directly sending her funds everyone would know how much liza received in donations if donations were sent with umbra instead each donation would be sent to a different address and only liza would know the total amount of donations she received how does it work 1 when setting up your umbra account users sign a message the hash of this message is used to generate two private keys a spending key and a viewing key 2 the corresponding public keys are both published on chain as records associated with your address 3 a payer uses your address ens or cns name to look up your two public keys separately the payer generates a random number 4 the random number is used with the spending public key to generate a stealth address to send funds to the viewing public key is used to encrypt the random number 5 using the umbra contract the payer sends funds to the stealth address and the stealth address and encrypted random number are emitted as an announcement event 6 the receiver scans all announcement events from the umbra contract for each they use their viewing private key to decrypt the random number then multiply that number by their spending private key to generate the stealth private key if the stealth private key controls the stealth address emitted in the announcement this payment was for the receiver 7 the receiver can now use the spending private key to either directly send the transaction required to withdraw funds to another address or sign a meta transaction to have the withdrawal request processed by a relayer see the technical details how does it work https app umbra cash faq how does it work technical for more details how private is umbra umbra offers a limited set of privacy guarantees and it s important to understand them before using the protocol umbra does not offer full privacy like aztec or zcash it simply makes it impossible for any outside observers i e anyone who is not the sender or the receiver to know who the sender paid by looking at the receiving address it s important to understand that poor hygiene by the receiver for example sending the funds directly to a publicly known address reduces the privacy benefits for both the sender and receiver the privacy properties of umbra can also be diminished if an observer can narrow down the set of potential recipients for a given transaction any valid public key can be used as a recipient and anyone who has sent a transaction on ethereum has a publicly available public key therefore by default the anonymity set the set of potential recipients of a transaction is anyone who has ever sent an ethereum transaction in practice this isn t necessarily the case and an observer may be able to narrow down the list of recipients in a few ways 1 most users will use ens names to send funds so the recipient most likely has published keys under an ens name 2 poor hygiene when withdrawing funds from your stealth addresses can reduce or entirely remove the privacy properties provided by umbra see which addresses are safe for withdrawing funds https app umbra cash faq what addresses are safe for withdrawing funds to for more details always use caution when withdrawing is umbra a mixer no umbra is not a mixer and does not use zero knowledge proofs instead umbra is based on ordinary elliptic curve cryptography it s meant for payments between two entities and comes with a different set of privacy tradeoffs rather than breaking the link between sending and receiving address like a mixer umbra makes that link meaningless everyone can see who sent the funds and everyone can see the address funds were sent to but that receiving address has never been seen on chain so it s impossible for any outside observers to know who controls it where can i learn more check out the full faq https app umbra cash faq to get more details about umbra development this repository uses yarn https yarnpkg com for package management and volta https volta sh for dev tool version management both are prerequisites for setting up your development environment the repository also requires foundry https github com gakonst foundry for development of periphery smart contracts components umbra is a monorepo consisting of 4 packages frontend frontend frontend web3 app for setting up and using umbra deployed at app umbra cash https app umbra cash contracts core contracts core solidity contracts used in the umbra protocol contracts periphery contracts periphery solidity contracts used by the umbra frontend to add features and improve ux umbra js umbra js a typescript library for building umbra enabled web3 apps in node js or in the browser the monorepo structure simplifies the development workflow instructions to get started clone this repo then follow these instructions sh run these commands from workspace root cp contracts core env example contracts core env please edit the env with your own environment variable values cp frontend env example frontend env please edit the env with your own environment variable values cp umbra js env example umbra js env please edit the env with your own environment variable values curl l https foundry paradigm xyz bash install foundryup binary foundryup install foundry yarn install installs dependencies for each of the 3 packages also builds umbra js yarn test runs the test suite for each package additional commands also available from the workspace root yarn build builds each of the 3 packages yarn clean removes build artifacts for each of the 3 packages yarn lint lints each of the 3 packages yarn prettier runs formatting on each of the 3 packages yarn test runs tests for each of the 3 packages note if you want to be more precise with your command e g just building cleaning or testing 1 package simply run any above command from the package directory for example if you were just working on the contract code you might sh cd contracts core move into the contracts core sub directory yarn build build only the contracts yarn clean remove only the contract build artifacts yarn test run only the contract tests contract deployments umbra contracts are deployed at the same address on each network where umbra resides below is a list of addresses for the contracts currently in use contract type address umbra core 0xfb2dc580eed955b528407b4d36ffafe3da685401 https blockscan com address 0xfb2dc580eed955b528407b4d36ffafe3da685401 stealthkeyregistry core 0x31fe56609c65cd0c510e7125f051d440424d38f3 https blockscan com address 0x31fe56609c65cd0c510e7125f051d440424d38f3 umbrabatchsend periphery 0xdbd0f5ebada6632dde7d47713ea200a7c2ff91eb https blockscan com address 0xdbd0f5ebada6632dde7d47713ea200a7c2ff91eb contributions contributions to umbra are welcome fork the project create a new branch from master and open a pr ensure the project can be fast forward merged by rebasing if necessary license umbra is available under the mit license txt license copyright c 2023 scopelift | ethereum privacy smart-contracts defi | blockchain |
STM32F7-FreeRTOS-Blinky | stm32f7 freertos blinky on linux by wangxian note use the gnu make with arm none eabi gcc and cmake tools steps 1 cmake the freertos and compiler to the static library go into the project and go into the freertos directory cd freertos build cmake make to generate the libstm32f7xx freertos a 2 check that libstm32f7xx hal drivers a file in the drivers build lib if not in the folder then cd drivers build cmake make 3 compiler the user source cd build cmake make that could generate the blinky bin and blinky hex files 4 plug your stm32f7 board into your pc and make sure you got the st link tools if you didn t have the tools you can go to the site st linkv1 v2 https github com texane stlink to download the tools 5 go into the build bin make flash you can see the program writing the flash 6 here wo go let s conquer the stm32f7 i m the green hand so sorry for that bad english | os |
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design-react-kit | circleci https img shields io circleci build github italia design react kit partecipa sul canale design devel https img shields io badge slack 20channel 23design devel blue svg https developersitalia slack com messages c7vpauvb3 ricevi un invito a slack https slack developers italia it badge svg https slack developers italia it read this in other languages english readme en md importante questo kit stato progettato per funzionare con la versione 1 x di bootstrap italia non esiste al momento un kit per la versione 2 x di bootstrap italia intro design react kit un set di componenti react che implementa bootstrap italia https italia github io bootstrap italia e gli stili presenti su design ui kit https github com italia design ui kit come mostrato su invision https invis io twmuzs6vfp5 per navigare la libreria e visualizzare i componenti stato utilizzato storybook https storybook js org la versione pubblica dello storybook disponibile qui https italia github io design react kit per l ultima release stabile pubblicata mentre qui https design react kit vercel app per la versione di sviluppo relativa al branch master indice start doctoc generated toc please keep comment here to allow auto update don t edit this section instead re run doctoc to update requisiti requisiti come usare il kit come usare il kit peer dependencies peer dependencies come iniziare come iniziare come creare nuovi componenti come creare nuovi componenti publishing publishing continuous integration continuous integration job build job build job deploy github pages job deploy github pages job npm publish job npm publish end doctoc generated toc please keep comment here to allow auto update requisiti nodejs yarn come usare il kit per utilizzare design react come dipendenza in un app possibile installarla da npm https www npmjs com italia suggeriamo di usare create react app per creare una nuova webapp react come segue sh create react app nome app cd nome app yarn add design react kit save aggiungere bootstrap italia ed i font il design react kit non include il css ed i file font ed quindi necessario installarli a parte sh yarn add bootstrap italia typeface lora typeface roboto mono typeface titillium web save app js a questo punto sufficiente importare esplicitamente nella app css e font se si usato create react app all interno del file src app js jsx import react from react import app css import alert from design react kit import bootstrap italia dist css bootstrap italia min css import typeface titillium web import typeface roboto mono import typeface lora const app return alert this is an alert alert export default app caricamento font il tema bootstrap italia utilizza un set specifico di font typeface titillium web roboto mono e lora il caricamento di questi font lasciato al browser ma volendo pu essere controllato tramite l apposito componente fontloader sufficiente dichiarare il componente fontloader in cima all app react per permettere il caricamento in alternativa necessario gestire il caricamento dei font manualmente mediante il pacchetto webfontloader js const webfont require webfontloader webfont load custom families titillium web 300 400 600 700 latin ext lora 400 700 latin ext roboto mono 400 700 latin ext peer dependencies la libreria non include react e react dom evitando clashing di versioni e aumento inutile delle dimensioni del bundle per questo motivo per lo sviluppo in locale sar necessario installare manualmente le dipendenze il comando da eseguire sh yarn install peers oppure in alternativa manualmente sh yarn install react react dom come iniziare clona il repository ed esegui yarn per installare le dipendenze quindi esegui yarn storybook serve per avviare il server di sviluppo storybook sar quindi disponibile all indirizzo http localhost 9001 storybook assets storybook screenshot jpg raw true storybook stato arricchito con alcuni addons che lo rendono pi parlante come creare nuovi componenti questa sezione guider alla creazione di nuovi componenti nel repository tutti i componenti risiedono nella cartella src ogni componente possiede una sua cartella con tutto ci che necessario per farlo funzionare le storie storybook invece sono sotto stories i test unitari risiedono nella cartella test il componente button ad esempio presente sotto il percorso src button e la sua struttura la seguente src button button tsx stories button button stories mdx button stories tsx test button test tsx alcune regole di base per strutturare i componenti i file tsx file del componente utilizza la sintassi jsx i file stories tsx dovrebbero contenere solo quanto relativo al componente stesso i file stories mdx dovrebbero contenere solo documentazione relativa al componente stesso i file test tsx dovrebbero contenere solo test relativi al componente stesso una volta creato un nuovo componente con la sua story avviando storybook sar possibile controllare che tutto funzioni come dovrebbe come contribuire per inviare nuovi contenuti o bug fix necessario fare un fork del repository quindi partire dal branch master per un nuovo branch contenente la funzionalit una volta completa la funzionalit con relativi test ove possibile sar necessario fare una pr sul repository principale snapshot tests il sistema di testing prevede un controllo delle storie presenti mediante una tecnica chiamata snapshot testing il contenuto della storia storybook verr copiato in un file speciale e preservato per notificare eventuali cambiamenti in futuro questo fa si che l aggiunta di nuove storie potrebbe risultare in un fallimento del task test in una pr qualora fosse stata aggiunta una nuova storia o modificata una gi presente sar necessario aggiornare il file di snapshot come segue yarn test u a questo punto creare un nuovo commit ed aggiornare la pr con il file di snapshot aggiornato controllare che le modifiche apportate siano corrette prima di aggiornare la pr publishing e disponibile un comando per generare una versione statica del catalogo storybook cos che possa essere deployato senza utilizzo di un webserver sh yarn storybook build le pagine statiche ottenute dal processo di build saranno generate sotto la folder storybook static compilazione libreria per compilare la libreria e generare i file nella cartella dist sufficiente lanciare il comando dedicato sh yarn build supporto browsers il kit segue le indicazioni riportate nelle linee guida di design per i servizi web della pubblica amministrazione sezione 6 3 1 2 1 supporto browser https docs italia it italia designers italia design linee guida docs it 2020 1 doc user interface lo sviluppo di un interfaccia e i web kit html strumenti mediante l utilizzo del pacchetto browserslist config design italia typescript typings la libreria stata portata a typescript ed i tipi sono esportati con essa ringraziamenti a href https www chromatic com img src https user images githubusercontent com 321738 84662277 e3db4f80 af1b 11ea 88f5 91d67a5e59f6 png width 153 height 30 alt chromatic a grazie a chromatic https www chromatic com per aver fornito la piattaforma di visual testing che ci aiuta a revisionare cambiamenti ui e intercettare regressioni visuali | design react toolkit italia hacktoberfest | os |
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