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docker-ubuntu-terraform | building docker image with installed terraform run the latest ubuntu container bash docker container run it name ubuntu latest ubuntu latest bin bash then we need to download unzip and install the latest version of terraform currently the latest one is 0 13 3 make sure to check it here https www terraform io downloads html bash apt get update apt get install wget y apt get install unzip y wget https releases hashicorp com terraform 0 13 3 terraform 0 13 3 linux amd64 zip cp terraform usr local bin terraform version exit now we have installed terraform as well as unzip and wget and shut the container down docker ps a should confirm that bash docker ps a container id image status names 63016bcefec6 ubuntu latest exited 0 about a minute ago ubuntu latest docker container diff ubuntu latest will print us all the changes applied to this container if you want to read it though make yourself a cup of tea it ll take a while we probably don t need all the changes so let s do a little clean up docker start ubuntu latest a to start the container and attach to it rm terraform rm terraform 0 13 3 linux amd64 zip apt get remove unzip y apt get remove wget y exit this container is still going to have some changes due to the apt get update call but i m ok with that what s important is that we have a dded the terrafrom binary to its onwn place a usr local bin terraform so it s time to commit the image bash docker container commit a asizikov m installed terraform 0 13 3 ubuntu latest ubuntu terraform docker images should list all the images installed on your computer and ubuntu terraform should be among them ok it s time to tag and publish i guess bash docker image tag ubuntu terraform latest asizikov ubuntu terraform 0 13 3 docker push asizikov ubuntu terraform 0 13 3 it s docker save asizikov ubuntu terraform 0 13 3 ubuntu terraform tar mkdir unpacked tar xf ubuntu terraform tar c unpacked cd unpacked tree this will produce a nice tree structure with all our layers 57f79de53e5eeff9c780866667a034a2b3959dc115b18f5cea5aec32b9677239 json 70b23ca3cba25ea431e904e2d4a90f144f52b907da5afee7e2e1a6004b9e3a35 version json layer tar 7e08bf461d394ea068ee4836367cc46d4e1e47d9e848043c4b63a61b8c0e714a version json layer tar 8c91f14beadb4dcf4e11153514476e705521a0fbab80c27a1b582241a4f91b75 version json layer tar 8e6ad6de8875a0658e8e86659248cdaa0d9de73b2132d94f685eb044115e1114 version json layer tar manifest json repositories let s find the latest layer bash cat repositories asizikov ubuntu terraform 0 13 3 8e6ad6de8875a0658e8e86659248cdaa0d9de73b2132d94f685eb044115e1114 cd 8e6ad6de8875a0658e8e86659248cdaa0d9de73b2132d94f685eb044115e1114 mkdir unpack layer tar xf layer tar c unpack layer cd unpack layer tree this will print the large tree with all the files which belong to the layer and we can scroll all the way down to find our terraform executable lovely make sure to take a look there and think how can we make this image smaller usr bin local bin terraform ps don t forget to clean things up rm rf unpacked rm f ubuntu terraform tar docker container rm ubuntu latest docker image rm asizikov ubuntu terraform 0 13 3 that s all folks happy dockering | docker docker-image docker-images | cloud |
sql-challenge | sql challenge repository of data modeling data engineering and data analysis for the employee database sql challenge instructions this assignment is divided into three parts data modeling data engineering and data analysis data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering use the provided information to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints for the primary keys verify that the column is unique otherwise create a composite key https en wikipedia org wiki compound key which takes two primary keys to uniquely identify a row be sure to create tables in the correct order to handle foreign keys import each csv file into the corresponding sql table hint to avoid errors be sure to import the data in the same order that the tables were created also remember to account for the headers when importing data analysis once you have a complete database perform these steps 1 list the following details of each employee employee number last name first name sex and salary 2 list first name last name and hire date for employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name 4 list the department of each employee with the following information employee number last name first name and department name 5 list first name last name and sex for employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 list the frequency count of employee last names i e how many employees share each last name in descending order bonus optional as you examine the data you begin to suspect that the dataset is fake maybe your boss gave you spurious data in order to test the data engineering skills of a new employee to confirm your hunch you decide to create a visualization of the data to present to your boss follow these steps 1 import the sql database into pandas yes you could read the csvs directly in pandas but you are after all trying to prove your technical mettle this step may require some research feel free to use the following code to get started be sure to make any necessary modifications for your username password host port and database name sql from sqlalchemy import create engine engine create engine postgresql localhost 5432 your db name connection engine connect consult the sqlalchemy documentation https docs sqlalchemy org en latest core engines html postgresql for more information if you re using a password do not upload your password to your github repository review this video https www youtube com watch v 2uatpmnvh0i and the github website https help github com en github using git ignoring files for more information 2 create a histogram to visualize the most common salary ranges for employees 3 create a bar chart of average salary by title submission create an image file of your erd create a sql file of your table schemata create a sql file of your queries optional create a jupyter notebook of the bonus analysis create and upload a repository with the above files to github and post a link on bootcamp spot ensure your repository has regular commits and a thorough readme md file | server |
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Mobile | mobile mobile development | front_end |
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AUTOMATIC-Wireless-Mini-Weather-Station | automatic wireless mini weather station this project s major objective is to design and implement an economic wireless embedded system that serves as a mini weather station i developed my own iot interface where i displayed all the essential information with a graphical chart representation for better data visualization i also created a minimal prototype using esp32 that works properly and meets the necessary demands of this project project folder structure sourcecode folder data folder contains the html file where the web server interface has been developed the project main code under the name esp32 webserver project bme280 ino docs folder it ll contain all the relevant documents related to this project | automatic c esp32 mini-weather-station html-css-javascipt iot webserver-esp32 webserver-interface webserver-esp32-enc wireless-communication arduino-script charts chart data-representation bme280 weather-sensors | os |
GIAR | giar codefactor https www codefactor io repository github seraogianluca giar badge master https www codefactor io repository github seraogianluca giar overview master data mining and machine learning a service implemented for the workgroup project of the data mining and machine learning course of the artificial intelligence and data engineering master degree at university of pisa 1 project documentation docs datamining sentimentanalysisdoc md 2 training set docs datamining dataset training set arff 3 training set data distribution docs datamining dataset tweets distribution xlsx 4 final test datasets docs datamining dataset classified data csv 5 test datasets confusion matrices docs datamining dataset classified data confusion matrices xlsx 6 weka test result buffers docs datamining weka tests credits application designed and developed by barigliano lorenzo serao gianluca large scale and multi structured databases a service for the workgroup tasks of the large scale and multi structured databases course of the artificial intelligence and data engineering master degree at university of pisa task 2 1 design docs design md 2 implementation docs implementation md 3 indexes performance study docs indexesstudy md 4 tests docs test md 5 user manual docs usermanual md task 3 1 design graph docs designgraph md 2 implementation graph docs implementationgraph md 3 user manual same of task 2 docs usermanual md credits application designed and developed by barigliano lorenzo g mez marsha mazzini matilde serao gianluca application installation databases configuration neo4j install the database in your computer and connect as localhost replacing username and password with yours java driver graphdatabase driver bolt localhost 7687 authtokens basic username password mongodb install the database in your computer and create the giar database with the users and the game collections as described in the design document docs design md the connection string is set to localhost java client mongoclients create mongodb localhost 27017 twitter api configuration to run the sentiment analysis a twitter api token is required to load the token create a twitter4j properties file in the resources folder the file must contain the following lines http usessl true oauth consumerkey twitter api token oauth consumersecret twitter api token oauth accesstoken twitter api token oauth accesstokensecret twitter api token | multi-structured-databases data-mining | server |
guidu | guidu banner https travis ci org uidu org guidu svg branch main standard readme compliant https img shields io badge standard readme ok green svg style flat https github com richardlitt standard readme netlify status https api netlify com api v1 badges f216cce3 c149 4be4 8d3a cc87cb744a18 deploy status https app netlify com sites guidu deploys crowdin https badges crowdin net guidu localized svg https crowdin com project guidu guidu is uidu 39 s design system library todo fill out this long description table of contents install install usage usage maintainers maintainers contributing contributing license license install git clone https github com uidu org guidu git cd guidu yarn install usage documentation is package specific browse https uidu design to see all the packages in action maintainers uidu org https github com uidu org apuntovanini https github com apuntovanini fsansalvadore https github com fsansalvadore contributing start all packages not reccommended yarn start start specific folders yarn start forms yarn start data yarn start dashboards or specific packages yarn start modal dialog field geosuggest pull requests are more than welcome we re currently making this repo more contributors friendly common pitfalls license mit 2019 uidu org | react design-system social-impact typescript | os |
Hacktoberfest-Web | h1 align center nbsp nbsp nbsp nbsp nbsp hacktoberfest web h1 br p align center code a web project for new contributors code p br br license br code a href https github com sujal 2820 hacktoberfest web blob main license mit license a code br code of conduct br code a href https github com sujal 2820 hacktoberfest web blob main code of conduct md code of conduct a code br hacktoberfest code a href https hacktoberfest com hacktoberfest 2023 a code br contribute br 1 visit code a href https github com sujal 2820 hacktoberfest web blob main contributing md contributing md a code 2 create code a href https github com sujal 2820 hacktoberfest web issues issues a code 3 create code a href https github com sujal 2820 hacktoberfest web pulls pull request a code 4 star the code a href https github com sujal 2820 hacktoberfest web repository a code br technology a id technology a br 1 html 2 css 3 github br star a id star a br if you like the project give a star br hacktoberfest a id hacktoberfest a br rust hacktoberfest is a month long celebration of open source projects the maintainers and the entire community of contributors rust you ll receive your digital reward once you ve completed four accepted pull merge requests rust the first 50 000 participants to have their first pr mr accepted will have a tree planted in their name through tree nation br participation br 1 fork the repo 2 make your changes 3 create your branch 4 submit the pull request 5 wait for the merge br author a id author a br code a href https github com sujal 2820 sujal soni a code | css hacktoberfest hacktoberfest-accepted hacktoberfest2023 html web template | front_end |
SAPO | sapo this is an implementation of the paper towards easier and faster sequence labeling for natural language processing a search based probabilistic online learning framework sapo information sciences pdf https www sciencedirect com science article pii s0020025518309125 the codes are also used for the paper towards shockingly easy structured classification a search based probabilistic online learning framework arxiv https arxiv org abs 1503 08381 article sun2018sapo title towards easier and faster sequence labeling for natural language processing a search based probabilistic online learning framework sapo author xu sun and shuming ma and yi zhang and xuancheng ren journal information sciences year 2018 issn 0020 0255 doi https doi org 10 1016 j ins 2018 11 025 eprint 1503 08381 the most important part of sapo can be found at optimsapo cs | natural-language-processing chunking named-entity-recognition pos-tagging | ai |
maestro | maestro web based mobile and web game development platform | front_end |
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kornia | div align center p align center img width 75 src https github com kornia data raw main kornia banner pixie png p english readme zh cn md prettier ignore a href https kornia org website a a href https kornia readthedocs io docs a a href https colab research google com github kornia tutorials blob master source hello world tutorial ipynb try it now a a href https kornia github io tutorials tutorials a a href https github com kornia kornia examples examples a a href https kornia github io kornia blog blog a a href https join slack com t kornia shared invite zt csobk21g cnydwe5fmvkcktierfgceq community a pypi python https img shields io pypi pyversions kornia https pypi org project kornia pytorch https img shields io badge pytorch 1 9 1 ee4c2c logo pytorch logocolor white https pytorch org get started locally license https img shields io badge license apache 202 0 blue svg licence pypi version https badge fury io py kornia svg https pypi org project kornia downloads https static pepy tech badge kornia https pepy tech project kornia slack https img shields io badge slack 4a154b logo slack logocolor white https join slack com t kornia shared invite zt csobk21g 2aqri x9uu6plmuuzdvfja twitter https img shields io twitter follow kornia foss style social https twitter com kornia foss tests cpu https github com kornia kornia actions workflows scheduled test cpu yml badge svg event schedule branch master https github com kornia kornia actions workflows scheduled test cpu yml codecov https codecov io gh kornia kornia branch master graph badge svg token fzcb7e0bso https codecov io gh kornia kornia documentation status https readthedocs org projects kornia badge version latest https kornia readthedocs io en latest badge latest pre commit ci status https results pre commit ci badge github kornia kornia master svg https results pre commit ci latest github kornia kornia master a href https www producthunt com posts kornia utm source badge featured utm medium badge utm souce badge kornia target blank img src https api producthunt com widgets embed image v1 featured svg post id 306439 theme light alt kornia computer vision library for deep learning product hunt style width 250px height 54px width 250 height 54 a p div kornia is a differentiable computer vision library for pytorch https pytorch org it consists of a set of routines and differentiable modules to solve generic computer vision problems at its core the package uses pytorch as its main backend both for efficiency and to take advantage of the reverse mode auto differentiation to define and compute the gradient of complex functions div align center img src https github com kornia kornia raw master docs source static img hakuna matata gif width 75 height 75 div div align center img src http drive google com uc export view id 1knwaanudy1mynf0eyfyxjdm3ti09tzaq div overview inspired by existing packages this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations epipolar geometry depth estimation and low level image processing such as filtering and edge detection that operate directly on tensors at a granular level kornia is a library that consists of the following components component description kornia https kornia readthedocs io en latest index html a differentiable computer vision library with strong gpu support kornia augmentation https kornia readthedocs io en latest augmentation html a module to perform data augmentation in the gpu kornia color https kornia readthedocs io en latest color html a set of routines to perform color space conversions kornia contrib https kornia readthedocs io en latest contrib html a compilation of user contrib and experimental operators kornia enhance https kornia readthedocs io en latest enhance html a module to perform normalization and intensity transformation kornia feature https kornia readthedocs io en latest feature html a module to perform feature detection kornia filters https kornia readthedocs io en latest filters html a module to perform image filtering and edge detection kornia geometry https kornia readthedocs io en latest geometry html a geometric computer vision library to perform image transformations 3d linear algebra and conversions using different camera models kornia losses https kornia readthedocs io en latest losses html a stack of loss functions to solve different vision tasks kornia morphology https kornia readthedocs io en latest morphology html a module to perform morphological operations kornia utils https kornia readthedocs io en latest utils html image to tensor utilities and metrics for vision problems installation from pip bash pip install kornia pip install kornia x to get the training api details summary other installation options summary from source bash python setup py install from source with symbolic links bash pip install e from source using pip bash pip install git https github com kornia kornia details examples run our jupyter notebooks tutorials https kornia github io tutorials to learn to use the library div align center a href https colab research google com github kornia tutorials blob master source hello world tutorial ipynb target blank img src https raw githubusercontent com kornia data main hello world arturito png width 75 height 75 a div triangular flag on post updates white check mark image matching https kornia readthedocs io en latest applications image matching html integrated to huggingface spaces https huggingface co spaces see gradio web demo https huggingface co spaces akhaliq kornia loftr white check mark face detection https kornia readthedocs io en latest applications face detection html integrated to huggingface spaces https huggingface co spaces see gradio web demo https huggingface co spaces frapochetti blurry faces cite if you are using kornia in your research related documents it is recommended that you cite the paper see more in citation citation md bibtex inproceedings eriba2019kornia author e riba d mishkin d ponsa e rublee and g bradski title kornia an open source differentiable computer vision library for pytorch booktitle winter conference on applications of computer vision year 2020 url https arxiv org pdf 1910 02190 pdf contributing we appreciate all contributions if you are planning to contribute back bug fixes please do so without any further discussion if you plan to contribute new features utility functions or extensions please first open an issue and discuss the feature with us please consider reading the contributing contributing md notes the participation in this open source project is subject to code of conduct code of conduct md community forums discuss implementations research etc github forums https github com kornia kornia discussions github issues bug reports feature requests install issues rfcs thoughts etc open https github com kornia kornia issues new choose slack join our workspace to keep in touch with our core contributors and be part of our community join here https join slack com t kornia shared invite zt csobk21g 2aqri x9uu6plmuuzdvfja for general information please visit our website at www kornia org a href https github com kornia kornia graphs contributors img src https contrib rocks image repo kornia kornia width 75 height 75 a made with contrib rocks https contrib rocks | computer-vision image-processing machine-learning pytorch deep-learning neural-network python artificial-intelligence | ai |
ask-bedrock | ask amazon bedrock converse with your favorite amazon bedrock https aws amazon com bedrock large language model from the command line p img width 1000 src https raw githubusercontent com awslabs ask bedrock main readme svg p this tool is a wrapper around the low level amazon bedrock apis and langchain https python langchain com docs integrations llms bedrock its main added value is that it locally persists aws account and model configuration to enable quick and easy interaction installation requires python 3 9 requires a working aws cli setup configured with a profile that allows amazon bedrock access see cli documentation https docs aws amazon com cli latest userguide cli configure files html for details shell pip install ask bedrock you can also build run this project locally see building and running locally building and running locally usage activating models before you can use this command line tool you need to request model access through the aws console https console aws amazon com bedrock home modelaccess in a region where bedrock is available switch to the region where you want to run bedrock go to model access click edit activate the models you wish to use and then click save changes invocation to start a conversation simply enter the following command shell ask bedrock converse upon the first run you will be led through a configuration flow to learn more about configuration options see the configuration configuration section below if you re fully configured the tool will show you a prompt and you can start interacting with the configured model multi line prompts can be wrapped into blocks to end your interaction hit ctrl d note that the conversation will be lost pricing note that using ask amazon bedrock incurs aws fees for more information see amazon bedrock pricing https aws amazon com bedrock pricing consider using a dedicated aws account and aws budgets https docs aws amazon com cost management latest userguide budgets managing costs html to control costs configuration ask amazon bedrock stores your user configuration in home config ask bedrock config yaml this file may contain several sets of configuration contexts for instance you can use contexts to switch between different models use the context parameter to select the context you d like to use the default context is default if no configuration is found for a selected context a new one is created if you want to change an existing config use shell ask bedrock configure context mycontext you can also create or edit the configuration file yourself in home config ask bedrock config yaml yaml contexts default region an aws region where you have activated bedrock aws profile a profile from your aws config file model id a bedrock model e g ai21 j2 ultra v1 model params a json object with parameters for the selected model model parameters this json is passed to langchain during client setup as model kwargs the schema depends on the model that is used have a look at the examples model params examples md if you want to configure multiple lines model parameters can be wrapped in building and running locally pip install build python m build python ask bedrock main py converse feedback as this tool is still early stage we are very interested in hearing about your experience please take one minute to take a little survey https pulse aws survey gtrwnht1 troubleshooting q i m getting the following error during invocation valueerror error raised by bedrock service an error occurred accessdeniedexception when calling the invokemodel operation your account is not authorized to invoke this api operation a you may have selected a model that is currently not yet activated for public usage it may have been listed it in the selection of available models but unfortunately some models such as amazon titan aren t yet available via api q the model responses are cut off mid sentence a configure the model to allow for longer response use model parameters see above for this claude for example would take the following model parameters max tokens to sample 3000 q i m getting an error that is not listed here a use the debug option to find out more about the error if you cannot solve it create an issue security see contributing contributing md security issue notifications for more information license this project is licensed under the apache 2 0 license | ai |
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cvLab | cvlab computer vision course experiment detailed introduction https blog csdn net qq 41748260 category 9669247 html | ai |
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Coursera_NLP | coursera nlp the programming assignments of natural language processing by michael collins on coursera | ai |
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ReportMonitor | report monitor report monitor https github com zhongsheng chen reportmonitor is a roaming web spider to listen on information system of graduate student management isysgsm https graduate buct edu cn of beijing university of chemical technology https www buct edu cn why develop it beijing university of chemical technology or buct has almost 15300 undergraduate students and nearly 8100 graduate students not to mention 2700 continuing education students and 460 foreigner students it places a strict graduation criteria on graduate students one of them is that attending academic reports or conferences no less than 6 for graduate students who are perusing their master degrees and no less than 8 for ph d candidates the affairs office for graduate student has always been telling out conference announcements with very rare notifications the students no matter where are currently on campus or have been stuck at home by the covid 19 pandemic is not easy to sign up a conference or report event that is the inspiration of our efforts to develop a tool to have students had a chance to get a quota of attending report events when they are coming up report monitor is not new it was originally implemented by satone https gitee com satone7 report monitor satone s implementation is vey time consuming when auto login option is used due to low accurate rate of image recognition of validation code hence we develop a new report monitor with a higher recognition rate the majority of our efforts was spending on the cropping and denoising images of validation codes features reducing time when running with auto login taking 2 or more minutes more reasonable way to cropping images of validation codes more effective preprocess to denoise validation code images installation packages requirements under python version 3 7 10 pyqt5 pillow opencv python pytesseract selenium win10toast pypiwin32 jupyterlab pyinstaller matplotlib convert python script reportmonitor py to executable exe using commands pyinstaller f w i icon ico add data cookie txt report monitor add data overlook txt report monitor reportmonitor py acknowledgement main contributors zishu gao zsgao mail buct edu cn for writing up installation tutorial | server |
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TTK4155-Embedded-PCB | ttk4155 embedded pcb ttk4155 embedded and industrial computer systems design circuit board this is the kicad source files we used for our schematics during the presentation of the project handin and making a physical more robust board for node 1 before many memory errors present old mess jpeg after no memory errors present new and improved jpeg | os |
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Front-End-Design-Checklist | front end design checklist images front end design checklist banner jpg h2 align center front end design checklist h2 p align center em the design checklist for front end developers is an exhaustive list of elements which can help developers to analyse and understand web designs and ensure the quality of their front end development em p prs welcome https img shields io badge prs welcome brightgreen svg http makeapullrequest com join the chat at https gitter im front end checklist front end design checklist https badges gitter im front end checklist front end design checklist svg https gitter im front end checklist front end design checklist cc0 https img shields io badge license cc0 green svg https creativecommons org publicdomain zero 1 0 table of contents 1 design requirements 1 design requirements 1 1 grid system 11 grid system 1 2 colors 12 colors 1 3 fonts and texts 13 fonts and texts 1 4 links and navigation 14 links and navigation 1 5 images icons 15 images icons 1 6 forms and buttons 16 forms and buttons 1 7 responsive web design 17 responsive web design 1 8 style guide and component approach 18 style guide and component approach 1 9 delivery files 19 delivery files 2 analysis and pre work phases 2 analysis and pre work phases 2 1 paper analysis 21 paper analysis 2 2 pre development phase 22 pre development phase 3 validation 3 validation 4 development phase 4 development phase 5 before production 5 before production the design checklist for front end developers is an exhaustive list of elements which web designers and front end developers need to take into consideration to facilitate their collaboration the following elements are a mix between known practices and new elements based on a long experience analysing web designs in case you are looking for a list of all elements you need to have to test before launching your site html page to production take a look on the front end checklist https github com thedaviddias front end checklist how to use the design checklist when comes the moment where developers discover new web designs before converting them to code some important elements may be missing the front end design checklist is a tool for front end developers and web designers which aim to help both to work in a seamlessly way you can share that checklist to web designers to ensure time will be saved at the delivery time or you can use it to review all elements delivered by the creative team and ensure everything is correct before digging into the code integration why you need to use the design checklist ensure all points are taken into consideration by the creative team having a document where web designers and developers can rely to ensure a better communication and coherence in the way they interact because it s easy to forget some important elements when you are pushed by short timelines avoid discovering issues after the creative team is already working on another project to show the complementary work between a web designer and a front end developer 1 design requirements designing a website or a webapp requires following some rules and taking into consideration that the project is not only a graphic project but a web project too the next sections are crucial for any web project 1 1 grid system grid system images grid system png a grid is explicitly provided in the design and the details of the grid are present in the technical specification width gutters number of columns the web designer can keep the grid in a transparent layer and use it on all his project guide guide 6 is a plugin for photoshop that can help you easily build your grid on sketch you can use the integrated make grid tool 7 to design your desired grid be familiar with the grid system you ll use on your project most of the time some options like alignment offsetting nesting are ignored by the developer and tend to be replaced by manual padding or margin unnecessarily before working on each components of your website you can build every templates used in the creatives only with the grid classes building the structure before everything else will facilitate your work afterwards html div class container div class row div class col sm let empty at first div div class col sm let empty at first div div class col sm let empty at first div div div if you want to ensure that the grid and the width of the devices are respected you may want to generate yourself a psd template and that you will send it to the web designer additional resources bootstrap grid system 8 v4 flexbox grid 9 don t overthink it grids css tricks 10 back to top table of contents 1 2 colors colors images colors png all colors used in the creatives are named gray light gray dark green or accordingly with their use body background body copy text paragraph they can be exported in an aco file 11 if use photoshop or on a symbol page for sketch and shared with the developers color swatches images color swatches jpg the different color state of some elements like buttons links inputs are defined and worked in the context of a light or dark background and with a light or a dark text all or the most important used colors are accessible in the design to allow a fluid navigation on the website webapp additional resources wcag contrast checker https contrastchecker com color safe accessible web color combinations http colorsafe co coolors co the super fast color schemes generator https coolors co back to top table of contents 1 3 fonts and texts fonts images fonts png fonts are an essential part of every design they shouldn t be chosen without discernment choosing the wrong font for a project could have financial and legal impacts it is recommended to ask your client to buy these fonts to avoid possible future issues and take into consideration the condition of use some webfonts are limited in terms of pageviews and can t be hosted understanding webfont licensing structures https aeolidia com understanding webfont licensing structures the fonts for desktop ttf or otf in general and the webfonts in woff woff2 and ttf format were provided in a zip file or given access to the website where they were bought ttf format for desktop is not the same than ttf for web resources using font face css tricks https css tricks com snippets css using font face fallback font https en wikipedia org wiki fallback font stacks were specified in a document ideally the style guide to the front end developer resources css basics fallback font stacks for more robust web typography css tricks https css tricks com css basics fallback font stacks robust web typography create your own font face kits font squirrel https www fontsquirrel com tools webfont generator the total weight of the all webfonts don t exceed 1 2 mb all variants included italic bold etc as far as possible all texts are provided in the proper language instead of dummy texts in english lorem ipsum and affiliates in case of a multilingual website always ask yourself how the design can react if the text is longer than it was previously define remember that web designers use to create perfect designs and don t always think about possible issues or situation with too much text additional resources web font optimization web fundamentals google developers https developers google com web fundamentals performance optimizing content efficiency webfont optimization font display for the masses css tricks https css tricks com font display masses rhythm in typography improve legibility readability and visual hierarchy https betterwebtype com rhythm in web typography back to top table of contents 1 4 links and navigation links and navigation images links png all links have a default hover focus active and visited state clearly defined the style guide is the best document to specified these alternate views of all navigation states hover active current page 1 5 images icons images images images png a favicon image with at least 512px x 512px is provided in png format the generation of all the others favicons can be easily done with online tools resources favicon generator for all platforms ios android pc mac https realfavicongenerator net all icons are provided in svg format each in the same square dimension in black and in a separated folder resources svgomg svgo s missing gui https jakearchibald github io svgomg the name of each icon starts with icon and is entirely in lowercase without any space and using dashes to separate each word additional resources essential image optimization https images guide back to top table of contents 1 6 forms and buttons forms images forms png all forms possess a title that can be used as a legend an example of the different states of all input fields were provided at least focus and inactive disabled state all error messages were provided the text eventually in a separated document the position and the color are clearly identifiable in the creatives and consistent some messages should be different according to the error resources forms need validation ux collective https uxdesign cc forms need validation 2ecbccbacea1 indicators of required optional fields are provided the primary and secondary buttons are clearly identifiable and are used following common practices resources primary secondary action buttons ux planet https uxplanet org primary secondary action buttons c16df9b36150 an example of the different states of a button were provided normal hover focused pressed and inactive state buttons with built in loading indicators are provided and can be applied to any button additional resources design better forms ux collective https uxdesign cc design better forms 96fadca0f49c design better input fields ux collective https uxdesign cc design better input fields 3d02985a8e24 designing perfect text field clarity accessibility and user effort https uxplanet org designing perfect text field clarity accessibility and user effort d03c1e26004b button ux design best practices types and states ux planet https uxplanet org button ux design best practices types and states 647cf4ae0fc6 how to design better buttons smashing magazine https www smashingmagazine com 2016 11 a quick guide for designing better buttons buttons in design systems eightshapes medium https medium com eightshapes llc buttons in design systems eac3acf7e23 back to top table of contents 1 7 responsive web design responsive images responsive png the mobile version of the design is provided before or at the same time of the desktop version if the mobile first thinking was not followed by the creative team some irregularities and inconsistencies may appear between the mobile and the desktop version check and flag these issues before starting the development of the project the tablet version of the design in certain cases should be provide too the pixel perfect notion is today in a certain way deprecated today it s impossible to have a design that worked the same facing the multitude of the screen sizes additional resources official google webmaster central blog mobile first indexing https webmasters googleblog com 2016 11 mobile first indexing html back to top table of contents 1 8 style guide and component approach styleguide images styleguide png all components designed on each page were created with the component based approach atomic design if not issues can occur in terms of performance maintainability of the project resources atomic design 16 6 reasons for component based ui development https www tandemseven com technology 6 reasons component based ui development a style guide needs to be provided listing all elements components styles dimensions some boilerplates like ux power tools https www uxpower tools can help saving time and keep consistency in the designs in the case where the style guide is missing it s a good practice to build yourself a living style guide https github com davidhund styleguide generators to facilitate your work some cms like drupal for example have plugins that allow to develop a living style guide using pattern lab https drupal pattern lab github io additional resources style guides design sketch medium https medium com sketch app sources tagged style guides the codepen design patterns and style guide https codepen io guide lonely planet travel guides and travel information http rizzo lonelyplanet com styleguide design elements colours styleguide https www yelp com styleguide in the case of an existing project sometimes the creative team needs to add new pages or modules in an existing project they should have or create a list of all existing elements and try to use what is already there having a style guide already created can save hours and ensure consistency of the project back to top table of contents 1 9 delivery files delivery files images delivery files png for all websites the web designer needs to provide at least 2 psd mobile desktop and eventually tablet or at least 1 sketch file which needs to be delivered with the dimension below if you have photoshop cc 2015 and above i recommend using artboards some web designers could eventually create multiple psd corresponding to each components used and import them in a single psd as smart layer in that case you ll have multiple psd linked to one or two files in the case of sketch since the libraries exist since version 47 it is possible to link multiples files with symbols the creative files are cleaned before delivering to developers empty and unnecessary layer needs to be removed to avoid large files the 404 error and eventually the page 500 error page were designed all popins popups and alert boxes were designed and can be enable throw layers of compositions additional resources photoshop etiquette a guide to discernible web design http photoshopetiquette com specific rules for psd file layer compositions are used to show each different pages if multiple views are provided within the same psd it s an easy way to avoid confusions and check that all elements are correctly organized back to top table of contents 2 analysis and pre work phases analysis and phases images phases png before starting the analysis and the pre work phases and after receiving the creative files you need to check some important elements which version of photoshop sketch is used some features are specific to some versions of photoshop or sketch it is important to flag any issue regarding this as soon as possible is the width of each psd or artboard correct in case some space is added on each side of the design check the exact width of the website are the creatives using too much box shadow linear or radial gradient don t forget the effect which can have impacts on the browser painting performance is a sitemap breadcrumb provided to understand the architecture of all pages and their dependencies does the website needs to have retina images back to top table of contents 2 1 paper analysis paper analysis images analysis png it is recommended printing some or all of the pages you have on an a3 format or a4 if you don t have this format because of the height of the page you ll probably need to print some designs on multiple pages i can t imagine a better way to start than analysing creatives on a paper with a pencil or different colourful pencils chosen to highlight different type of information 1 define the structure of the pages the headers the sections the articles main footer outlining these on at least one printed page 2 find all the headings that structured a page ensure the h1 is not on the logo and that the logical order is followed most of the time the h1 for the homepage will be hidden with css but needs to keep its legitimate meaning that analysis should be done with the help of a seo specialist in case you have one in your team 3 try to find and regroup similar components giving them an individual name regarding their functionality and not just their context for example naming a tab system 4 most of the creative elements can be done using css today it is not recommended to create any layout element using images any simple graphical element like buttons or borders should be done in css to avoid performance or scalability issues 5 find some possible lack of coherence in case a styleguide was not provided by the creative team it s your responsibility to ensure that every graphic element belong to a possible category buttons typography sliders it ll help you to create your own css sass architecture or to identify which component you ll need from an identified css framework after the paper analysis phase you can invite the creative team to use a tool like invision https www invisionapp com to facilitate the communication and exchange between the creative team and the developers the possibility to comment directly on pages can be a time saver and allow to keep a history of modifications and decisions 2 2 pre development phase according to the specifications plugins needed were defined in an early stage having a pre list of possible plugins before starting the development can help the developer to stay focus and not spend too much time in doing research during the development phase obviously some plugins may not perfectly fit and will be changed accordingly additional resources awesome js 22 bestofjs 23 back to top table of contents 3 validation the validation phase is when everything seems to be ready to be integrated the client in general validate the creatives without waiting for any approval from the technical team as exposed in the design checklist it is essential that developers ensure the quality of the delivery before starting to code 4 development phase all medias can be cut and saved before starting the development phase that can help you to avoid back and forth between your creative software and your code editor the image folder has a clear architecture where you arranged the layout s images it is important to stay consistent between projects in general defining a structure for that folder and a naming convention can be helpful you can find an example of a possible structure with prefixes used to recognise each image appurtenance bash images background banners icons layout a naming convention is used like adding prefixes to differentiate types of images all images used for background can be prefixed by bg icons by icon hero banners by hero or banner and so on 5 before production before launching your website be sure to review all your pages using the front end checklist https frontendchecklist io back to top table of contents translations the front end checklist is also available in other languages thanks for all translators and their awesome work chinese johnsenzhou front end design checklist https github com johnsenzhou front end design checklist spanish eoasakura front end design checklist https github com eoasakura front end design checklist support if you have any question or suggestion don t hesitate to use gitter or twitter give an up on product hunt https www producthunt com posts front end design checklist chat on gitter https gitter im front end checklist 28 front end design checklist utm source share link utm medium link utm campaign share link facebook https www facebook com frontenddesignchecklist twitter https twitter com thedaviddias author david dias https github com thedaviddias contributors this project exists thanks to all the people who contribute github contributing md license cc0 https i creativecommons org p zero 1 0 88x31 png https creativecommons org publicdomain zero 1 0 all icons are provided by icons8 https icons8 com back to top table of contents 6 https guideguide me 7 https www sketchapp com docs canvas rulers guides grids 8 https getbootstrap com docs 4 0 layout grid 9 http flexboxgrid com 10 https css tricks com dont overthink it grids 11 https www lifewire com aco file 2619477 16 http bradfrost com blog post atomic web design 22 https js libhunt com 23 https bestof js org 28 https gitter im front end checklist front end design checklist | frontend front-end-development front-end-developer-tool guidelines checklist reference web-development lists resources | front_end |
chatapp | chatapp a simple pure javascript chat app built using webrtc and okta https developer okta com chatapp demo https raw githubusercontent com rdegges chatapp master assets chatapp orig gif demo you can test this app out live here https naughty bhabha 908faa netlify com room test prerequisites before getting started you ll need to create an okta developer account which is free https developer okta com signup you ll also need to create an okta application of the spa type define your redirect uris and set your trusted origins you ll then need to modify the src static js auth js file to include your own okta values tutorial to see how this app works and how to set it up please check out this tutorial on scotch io where i explain in detail how to get setup and running | front_end |
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FreeRTOS-Neo | freertos neo freertos scripts and example code for neo | os |
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ESP31_RTOS_SDK | esp32 rtos sdk esp32 sdk based on freertos toolchain we suggest to choose crosstool ng as the compiler toolchain follow the instructions below to install crosstool ng step 1 install the required toolchain packages sudo apt get install git autoconf build essential gperf bison flex texinfo libtool libncurses5 dev wget gawk libc6 dev i386 python serial libexpat dev step 2 create a directory e g opt espressif to store the toolchain sudo mkdir opt espressif step 3 make the current user the owner sudo chown user opt espressif step 4 download the latest toolchain installation file to the directory created in step 2 cd opt espressif git clone b esp108 1 21 0 git github com jcmvbkbc crosstool ng git step 5 install toolchain cd crosstool ng bootstrap configure prefix pwd make make install ct ng xtensa esp108 elf ct ng build step 6 set the path variable to point to the newly compiled toolchain export path opt espressif crosstool ng builds xtensa esp108 elf bin path notice you need to do step 6 once you open a new shell or you can put it inside your bashrc file project template compile step 1 create a directory e g workspace to store a new project mkdir workspace step 2 clone esp32 rtos sdk cd workspace git clone https github com espressif esp32 rtos sdk git step 3 copy esp32 rtos sdk examples project template to workspace directory created in step 1 cp workspace esp32 rtos sdk examples project template workspace r step 4 create a directory e g workspace esp32 bin to store the bin files compiled mkdir p workspace esp32 bin step 5 set sdk path as the path of sdk files and bin path as the path of bin files compiled export sdk path workspace esp32 rtos sdk export bin path workspace esp32 bin notice make sure you set the correct paths or it will occur a compile error export bin path is optional default bin path is bin step 6 start to compile files cd workspace project template make clean make notice you need to do step 5 every time you open a new shell or you can put it inside your bashrc file if your project is successfully compiled the drom0 bin irom0 flash bin and user ota files will be generated in bin path directory makefile options esp conf in project template includes makefile supported options you can modify according to your needs 1 esptool parameters serial port depend on your platform default dev ttyusb0 esp port dev ttyusb0 serial baudrate default 230400 esp baud 230400 spi flash size 1mb 2mb 4mb 8mb 16mb default 1mb esp fs 1mb spi flash freq 40m 26m 20m 80m default 40m esp ff 40m spi flash mode qio qout dio dout default qio esp fm qio 2 compile link parameters ld folder location choose 0 1 default 0 0 use ld folder at sdk 1 use local ld folder at your project notice you can copy ld folder from sdk to your project then do some modifications esp local ld 1 ld file name notice if esp local ld is set to 1 esp ld name must be set esp ld file pro map ld ram map mode default 1 1 1 1 not use app cpu s iram use drom0 dcache to store rodata 1 2 not use app cpu s iram not use drom0 dcache 2 1 use all app cpu s iram as pro cpu s dram use drom0 dcache to store rodata 2 2 use all app cpu s iram as pro cpu s dram not use drom0 dcache 3 1 reverse 16k for app cpu other as pro cpu s dram use drom0 dcache to store rodata 3 2 reverse 16k for app cpu other as pro cpu s dram not use drom0 dcache notice if esp local ld is set to 0 esp map mode can be set esp map mode 1 1 download 1 use espressif s flash download tools download addresses for map 1 1 2 1 3 1 boot bin 0x00000 drom0 bin 0x04000 irom0 flash bin 0x40000 blank bin 0xfe000 for 1mb spi flash for map 1 2 2 2 3 2 boot bin 0x00000 irom0 flash bin 0x04000 blank bin 0xfe000 for 1mb spi flash 2 use esptool py prepare modify esptool options in esp conf makefile supports targets flash all upload boot bin app bin blank bin make flash all flash boot upload boot bin make flash boot notice only needed when boot bin update flash app update user application drom0 bin irom0 flash bin make flash app flash blank update blank bin to system parameter address make flash blank notice only needed when the system parameters needed to be restored for more details please refer to http www esp32 com | os |
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Amazon-Alexa-Reviews | amazon alexa reviews using natural language processing data visualizations and classification algorithms of machine learning about the data this dataset consists of a nearly 3000 amazon customer reviews input text star ratings date of review variant and feedback of various amazon alexa products like alexa echo echo dots alexa firesticks etc for learning how to train machine for sentiment analysis what you can do with this data you can use this data to analyze amazon s alexa product discover insights into consumer reviews and assist with machine learning models you can also train your machine models for sentiment analysis and analyze customer reviews how many positive reviews and how many negative reviews source extracted from amazon s website inspiration your data will be in front of the world s largest data science community what questions do you want to see answered | nlp sentiment-analysis data-analysis data-visualization bag-of-words feature-extraction modelling evaluation-metrics hyperparameter-tuning eda data-science | ai |
talks | presentations of jim huang aka jserv welcome to send speech invitations via email jserv tw gmail com schedule 2023 may 25 cthpc accelerating multi tenant services using in kernel i o offloading https sites google com view cthpc2023 program may 26 cthpc construct a compact and efficient risc v instruction set simulator https sites google com view cthpc2023 program jul 30 coscup https pretalx coscup org coscup 2023 talk cfgudc sep 21 oss eu lightweight and fast wifi access in virtual machines https osseu2023 sched com event 1oggh oct 30 tasti https tasti2023 com customizing linux systems in aerospace nov 12 mopcon dec 5 oss jp risc v based sandboxing for secure and efficient software execution https sched co 1typ4 schedule 2022 jan 2 guts c linked list https hackmd io sysprog c prog jan 7 guts c https hackmd io sysprog c prog feb 12 guts linux 2022 https hackmd io sysprog linux kernel internal feb 21 guts c https hackmd io sysprog c prog apr 4 guts linux https hackmd io sysprog linux kernel internal apr 5 guts linux https hackmd io sysprog linux kernel internal apr 9 guts linux https hackmd io sysprog linux kernel internal apr 11 guts linux i o https hackmd io sysprog linux kernel internal may 27 cthpc towards fine grained behavior based analysis for linux cpu scheduler https sites google com view cthpc2022 program may 27 cthpc an asynchronous i o interface for scalable and low latency network services https sites google com view cthpc2022 program jun 21 guts linux https hackmd io sysprog linux kernel internal jun 23 oss na towards preempt rt for the full task isolation https ossna2022 sched com event 11ntq aug 24 oss la revisit multi reader synchronizations for scalable applications https osslatam22 sched com event 15bse sep 4 sitcon https sitcon org 2022 agenda 35255d schedule 2021 jan 11 guts 1 https hackmd io sysprog concurrency jan 18 guts 2 https hackmd io sysprog concurrency jab 25 guts 3 https hackmd io sysprog concurrency feb 17 guts linux 2021 https beta hackfoldr org linux feb 18 guts 4 https hackmd io sysprog concurrency feb 21 guts 5 https hackmd io sysprog concurrency feb 27 guts 6 https hackmd io sysprog concurrency mar 3 nthu the evolution and industrial impacts of linux kernel http web cs nthu edu tw p 406 1174 198035 r67 php mar 11 guts 7 https hackmd io sysprog concurrency may 27 cthpc making shared data routable on ccnuma processors by extracting send receive information from spinlocks https sites google com view cthpc2021 program jul 31 coscup let s publish a collaborative e book for linux kernel https coscup org 2021 zh tw session w8wldx dec 14 oss jp reduce system call overhead for event driven servers https ossalsjp21 sched com event peef schedule 2020 feb 8 guts linux user mode linux https beta hackfoldr org linux feb 17 guts linux https beta hackfoldr org linux feb 21 guts c bitwise https hackmd io sysprog c prog mar 2 guts linux https beta hackfoldr org linux mar 9 guts c https hackmd io sysprog c prog mar 18 guts linux code review i https hackmd io sysprog linux2020 homework1 mar 23 guts linux code review ii https hackmd io sysprog linux2020 homework2 may 23 guts linux https beta hackfoldr org linux sep 5 guts https beta hackfoldr org linux oct 25 guts linux https hackmd io sysprog linux kernel internal nov 19 guts linux https hackmd io sysprog linux kernel internal dec 18 ics an empirical approach to minimize latency of real time multiprocessor linux kernel https ics2020 ncku edu tw program html schedule 2019 jan 7 guts linux rcu https beta hackfoldr org linux jan 11 guts linux rcu https beta hackfoldr org linux jan 14 guts linux https beta hackfoldr org linux jan 29 bits https www facebook com events 141219160109691 feb 13 guts linux https beta hackfoldr org linux feb 18 guts linux https beta hackfoldr org linux feb 24 guts c https hackmd io sysprog c prog feb 25 guts linux 1 2 http wiki csie ncku edu tw linux schedule mar 4 guts linux https beta hackfoldr org linux mar 24 guts linux 4 5 http wiki csie ncku edu tw linux schedule mar 26 guts linux https beta hackfoldr org linux apr 11 guts linux 1 https beta hackfoldr org linux apr 14 guts linux 2 https beta hackfoldr org linux apr 22 guts c stream i o eof https hackmd io sysprog c prog may 7 guts linux https beta hackfoldr org linux may 15 guts linux https beta hackfoldr org linux may 24 cthpc formal verification for blockchain and trusted computing https sites google com view cthpc2019 may 27 guts linux https beta hackfoldr org linux jun 13 aws why decentralized identifiers are changing the future internet https aws amazon com tw summits taipei agenda day 2 jun 17 guts linux timer https beta hackfoldr org linux jul 1 guts linux 3 https beta hackfoldr org linux jul 11 infosec http 2019 infosec org tw jul 11 guts c https hackmd io sysprog c prog jul 15 jul 17 guts linux scalability https beta hackfoldr org linux aug 26 guts c https hackmd io sysprog c prog aug 29 guts c https hackmd io sysprog c prog sep 2 guts http wiki csie ncku edu tw sysprog schedule schedule 2018 jan 7 ros taipei jan 14 jan 30 guts c linked list https hackmd io sysprog c prog feb 5 guts c https hackmd io sysprog c prog feb 12 guts c https hackmd io sysprog c prog mar 21 guts c stream i o eof https hackmd io sysprog c prog mar 30 apr 20 c c http cse nsysu edu tw files 14 1091 184101 r3315 1 php lang zh tw may 19 aii ptt ai https aliensunmin github io aii workshop 2nd may 31 cthpc intelligence compiler contract https sites google com view cthpc2018 program jun 1 cthpc a spin based model checking for the simple concurrent program on a preemptive rtos https sites google com view cthpc2018 program jun 21 jul 16 jul 17 guts c undefined behavior https hackmd io sysprog c prog jul 21 lass jul 27 guts c https hackmd io sysprog c prog jul 28 debconf tangleid https wiki debconf org wiki debconf18 openday conference room 4 aug 23 itri linux aug 25 guts amacc 1500 c https hackmd io sysprog c prog sep 9 https hackmd io s rkjfyqlvx sep 17 guts c https hackmd io sysprog c prog sep 20 guts c https hackmd io sysprog c prog oct 1 bot http botcommunity org oct 7 g0v https summit g0v tw 2018 agenda oct 8 guts c https hackmd io sysprog c prog oct 11 guts c crt https hackmd io sysprog c prog oct 18 oct 20 coscon http coscon kaiyuanshe cn oct 23 oss eu linux based rtos experimental platform for constructing self driving vehicles https osseu18 sched com event fxwg nov 4 mopcon https mopcon org 2018 schedule php nov 15 guts linux ebpf https beta hackfoldr org linux nov 22 asynchronous inter blockchain protocol nov 22 guts linux https beta hackfoldr org linux dec 5 http aais15 nkfust edu tw web signup login jsp sno 50948 dec 6 thinkblock dec 7 guts linux https beta hackfoldr org linux dec 15 guts linux process https beta hackfoldr org linux dec 25 ccns https ccns kktix cc events 107 meetup self driving car in ncku dec 27 guts linux spinlock https beta hackfoldr org linux schedule 2017 jan 16 http pas csie ntu edu tw ossseed jan 16 http pas csie ntu edu tw ossseed jan 23 http pas csie ntu edu tw ossseed jan 23 http pas csie ntu edu tw ossseed feb 4 fosdem secure microkernel for deeply embedded devices https fosdem org 2017 schedule event microkernel microkernel for embedded devices feb 21 elc effectively measure and reduce kernel latencies for real time constraints https openiotelcna2017 sched com event 9ity mar 15 guts arm https beta hackfoldr org arm mar 20 guts c https hackmd io sysprog c prog mar 22 guts arm https beta hackfoldr org arm mar 26 guts arm https beta hackfoldr org arm mar 27 guts concurrency https beta hackfoldr org oscar mar 29 guts arm https beta hackfoldr org arm apr 5 guts c https hackmd io sysprog c prog apr 17 guts facebook https www facebook com events 777590162408486 apr 19 formal verification in practice system software perspective https www csie ntu edu tw app news php sn 12468 may 16 guts c https hackmd io sysprog c prog may 20 mosut piko rt linux like real time kernel for arm microcontrollers jun 11 pycon pypy s approach to construct domain specific language runtime https tw pycon org 2017 en us events talk 347808212868661376 jun 19 guts arm https beta hackfoldr org arm jul 14 https hackmd io s bj1gf gbb aug 16 guts iot piko rt https beta hackfoldr org oscar aug 22 guts arm cortex m https beta hackfoldr org arm sep 11 oss na deterministic memory allocation for mission critical linux https ossna2017 sched com event bcsr sep 13 oss na build cloud infrastructure with armv8 https ossna2017 sched com event bdqu sep 29 jcconf ahead of time aot compilation in java 9 http jcconf tw 2017 speaker jserv html oct 3 guts https hackmd io s hk2cscgcl oct 5 guts https hackmd io s hk2cscgcl oct 10 guts c https hackmd io sysprog c prog oct 12 guts c https hackmd io sysprog c prog oct 19 oct 24 guts c https hackmd io sysprog c prog oct 26 guts cache https beta hackfoldr org cpu oct 29 mopcon https mopcon org 2017 schedule php nov 16 nov 16 ccns https ccns kktix cc events jserv iot micropayment nov 21 guts c goto https hackmd io sysprog c prog nov 23 itri nov 23 http taiwanchain info dec 6 dec 10 digitalocean hsinchu gnu linux dec 11 ncku distributed computing in the shared economy schedule 2016 jan 9 jan 18 guts c https hackmd io sysprog c prog jan 22 jan 31 h spectrum http www ylhspectrum com studios cjg9 feb 1 guts c https hackmd io sysprog c prog feb 18 guts c https hackmd io sysprog c prog mar 8 guts mar 21 guts linux apr 1 guts c https hackmd io sysprog c prog apr 4 openiot uvisor debugging facility improvements for arm mbed http events17 linuxfoundation org sites events files slides mbed uvisor pdf may 11 guts c https hackmd io sysprog c prog may 12 guts microkernel https beta hackfoldr org oscar may 18 iot http web cs nthu edu tw files 14 1015 103326 r67 1 php lang zh tw may 19 may 24 guts preempt rt linux https beta hackfoldr org oscar jun 3 pycon python https tw pycon org 2016 zh hant events keynotes jun 14 jun 20 secure hypervisor microkernel for iot jun 25 hkoscon http 2016 opensource hk topics free and open source software involvement in taiwan jun 27 guts iot https beta hackfoldr org oscar jun 29 guts c https hackmd io sysprog c prog jul 7 100 http tpet ntct edu tw files 11 1011 9820 php aug 25 guts c https hackmd io sysprog c prog sep 18 guts c https hackmd io sysprog c prog oct 15 jcconf graal compiler hotspot nov 8 nov 23 guts c https hackmd io sysprog c prog dec 17 bitsec secure microkernel http soft cs tsinghua edu cn os2atc2016 rc html dec 23 automated techniques for formal software verification https www csie ntu edu tw app news php sn 11874 schedule 2015 jan 19 juluos multics http juluosdev kktix cc events d7cc0d15 0cba9a eb5d0a 2f7415 jan 29 jan 29 jan 31 hackathon taiwan http hackathon tw mar 24 elc optimize uclinux for arm cortex m4 http elcabs2015 sched org event d76fa1347a02264b209c3c2275eaff39 vmhle8vh6bv apr 30 linux http hcsm kktix cc events jserv01 may 8 http csie stust edu tw tc news 1 39574 n may 20 mokoversity iot https www mokoversity com events iot revolution os may 21 may 24 ocf http ocf tw events elc2015 may 27 fan2 how can engineers benefit from maker community http www accupass com go fan2 may 27 may 29 cthpc construct an efficient and secure microkernel for internet of things http cthpc2015 csie ncku edu tw program aspx may 31 build your own operating system jun 4 http ocf tw events elc2015 jun 5 jun 18 vm5 lab 1 http vmfive kktix cc events vm5lab01 aug 15 coscup linux aug 15 coscup build a minimal operating system kernel for arm aug 16 coscup aug 16 coscup sep 1 gtc taiwan gpgpu http www nvidia com tw object gpu technology conference 2015 tw html sep 14 delta oct 2 http www cse yzu edu tw resource detail sort 487 oct 12 implement isolated security domains for arm based low power devices oct 14 build a minimal operating system kernel for arm oct 21 22 rtlws https www osadl org rtlws 2015 rtlws 2015 0 html performance evaluation of xenomai 3 https www osadl org id 2181 nov 5 c part i nov 20 http heyevent com event zbkkuokkgk37ca nov 21 mosut nov 23 c part ii nov 28 arm mbed uvisor http soft cs tsinghua edu cn os2atc2015 rc html dec 5 jcconf java hotspot jit dec 7 c dec 9 dec 17 internet of things constructions using open source technologies schedule 2014 jan 8 develop your own operating systems using cheap arm boards http www cs nctu edu tw cswebsite news all view 941 jan 24 arm jan 25 jan 26 freertos feb 10 juluos from l3 to sel4 what have we learnt in 20 years of l4 microkernels http juluosdev kktix cc events d7cc0d15 feb 15 mosut mar 15 sitcon mar 20 mar 22 apr 12 osdc tw multics apr 17 mhealth http www ctimes com tw cf showcf tw asp o hjy4hcp911ccfa0rnt apr 30 http www am ndhu edu tw files 14 1024 70505 r107 1 php may 2 http www csie ndhu edu tw csieweb zh tw node 1423 may 3 http www accupass com go tedxnthu2014 may 10 mosut android virtual machine and compilers https www facebook com events 686739794722627 may 15 gcc llvm https nccu sys2014 hackpad com may 22 gcc llvm https nccu sys2014 hackpad com may 29 gcc llvm https nccu sys2014 hackpad com jun 4 jun 28 study area revolution os jul 9 foss and project collaboration jul 11 jul 15 open source from legend business to ecosystem jul 15 perspectives on internet of things development jul 15 jul 20 coscup http coscup org 2014 en program jul 26 mosut build a lightweight hypervisor for realtime linux http www meetup com tainan py python tainan user group events 196122862 aug 2 whsap the practice about linux remote processor framework on arm cortex m a heterogeneous environments http whsap csie ncku edu tw hsa workshop aug 9 maker conf http www makerconf tw p 105 aug 23 mosut sel4 overview sep 7 fablab open source open city http tdcp kktix cc events openfabcity0907 oct 8 http nos kktix cc events 307dae5a oct 15 elce rtmux a thin multiplexer to provide hard realtime applications for linux https lccoelce14 sched com event 1yf9qj1 nov 15 jcconf openjdk vs dalvik art nov 17 the design and implementation of linux real time extensions nov 22 http ssw nctu kktix cc events ssw01 dec 8 http www csie kuas edu tw xoops modules news article php storyid 307 dec 10 http tiandiren tw archives 10043 dec 14 tedxtainan dec 27 mosut schedule 2013 jan 4 http osc2013 csie ncku edu tw news php jan 5 c http phorum study area org index php topic 68128 0 jan 21 https www facebook com events 516387461734961 group id 0 jan 29 tossug compiler http registrano com events 76f72c feb 7 feb 19 android builders summit hardware accelerated 2d rendering for android assets accel 2d render pdf feb 22 embedded linux conference olibc another c runtime library for embedded linux https embeddedlinuxconference2013 sched com event 14zaruh mar 3 study area tainan binder android http phorum study area org index php topic 68304 0 html mar 7 http www accupass com go hcsm0307 mar 12 rtos scheduling http wiki csie ncku edu tw embedded schedule mar 14 rtos scheduling http wiki csie ncku edu tw embedded schedule mar 16 sitcon http sitcon org 2013 mar 27 juluos microkernel http registrano com events 0d3fc6 apr 15 microkernel based operating system design for embedded systems http www slideshare net jserv microkernel evolution apr 20 osdc tw http www slideshare net jserv education usingopensource may 16 l4 fiasco oc may 27 automotive linux summit spring 2013 shorten device boot time for automotive ivi and navigation systems https automotivelinuxsummitspring2013 sched com event 12jrgdh may 30 applying microkernel architecture for real world use cases http www cs nctu edu tw cswebsite news activities view 595 may 30 http appuniverz webs com jun 6 http goo gl tlr2a jun 29 http ocow csdn net schedule html jul 4 mosut binary hacks jul 5 http wmmks csie ncku edu tw tsoc2013 index php option com content view article id 25 jul 8 http wmmks csie ncku edu tw tsoc2013 index php option com content view article id 25 jul 11 llvm http www accupass com go hcsm0711 jul 27 aug 1 1 aug 2 java developer day maxine vm java http www codedata com tw event javaday 2013 aug 3 coscup design power efficient rtos aug 3 coscup http www slideshare net jserv education usingopensource aug 3 coscup xvisor embedded and lightweight hypervisor http www slideshare net jserv xvisor aug 3 coscup f9 microkernel built for deeply embedded devices elegantly http www slideshare net jserv f9 microkernel aug 3 coscup aug 8 2 aug 15 3 aug 22 4 aug 28 juluos f9 microkernel arm cortex m http registrano com events efae55 aug 29 5 aug 31 mosut mykernel sep 5 6 sep 12 7 oct 17 implement runtime environments for hsa using llvm http www slideshare net jserv hsa llvm nov 10 minidebconf2013 diy your own quadrotor helicopter https wiki debian org debiantaiwan minidebconf201 nov 16 hellogcc build an optimized c runtime for embedded linux http www iscas ac cn xwzx kydt 201311 t20131115 3979276 html nov 30 tainan py pypy http www meetup com tainan py python tainan user group events 148885402 nov 30 http www accupass com go startuptalk13 dec 9 secure and efficient microkernel built for deep embedded systems http www cs ccu edu tw ann readfile php type speech page a7b166b6d7f3359a245f0e2237785c08 ftype jpg dec 18 open source from legend business to ecosystem http www cs nctu edu tw cswebsite news activities view 915 dec 23 it http www csie kuas edu tw xoops modules news article php storyid 233 dec 25 open source from legend business to ecosystem http www csie ncu edu tw show php cate 0 mode view pno 4919 dec 30 http www cs ccu edu tw ann readfile php type speech page f18b26146bb8c1d266a5e05103681167 ftype jpg | c-programming arm aarch64 rtos guts linux kernel microcontroller security open-source | os |
nlp20 | nlp20 data and code to support info 159 259 natural language processing spring 2020 | ai |
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blockchainj | java hyledger fabric spring springboot websocket springmvc mybatis sqlite leveldb hxsql sql hxql hxql hxql sql orm pbft 3f 1 f 1 leveldb hash 2 sqlite 1 id hash content nodeseedserver p2p websocket 1 ip websocket ip 2 3 4 todo 1 2 3 4 5 6 sqlite 1 ip 1 ca ca ca role ca 2 output id 3 4 5 | blockchain |
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stm32-it-sdk | disk91 iot sdk not only for stm32 version 1 7 in development for stable version go to version 1 6 branch this project is a low level sdk with a harwdare abstraction layer designed to make iot project the purpose is to quickly be able to create communicating iot device over lpwan lorawan sigfox for fast prototyping but also being able to bring that firmware to production will all the necessary components avaoilable out of the box this sdk try to be fully configurable with header files the objectif is to propose an abstraction layer between the software implementation and the mcu execution allowing to port the firmware on different plateform itsdk architecture doc it sdk architecture png it sdk architecture the sdk is currently implementing stm32l architecture the abstraction layer allows to quickly add new platforms it implements different usefull function with i hope a cleaner code than the usual st sdk components most is done to preserve code size the sdk can target small flash mcu from 16kb for simple applications ide environement open source environment gcc ac6 eclipse cubeide environment gcc ac6 eclipse may work with any others supported mcu functions low power switch with regular auto wakeup lpuart uart gpio wake up rtc with calibration eeprom for configuration backup watchdog timers with calibration hardware software gpio spi i2c abstraction time update generic interrupt handler hardware independant supported advanced features task scheduler state machine multiple serial console logger including segger rtt error logger with nvm storage secured debug configuration console secured storage in eeprom for keys end 2 end sigfox lorawan encryption configuration over serial for id commissionning during manufacturing process accelerometer abstraction layer gnss abstraction layer communication protocols interface sigfox clear text aes128 ctr speck32 sigfox eas128 ctr encryption lorawan lorawan encryption eas128 ctr speck32 join uplink downlink ack supported tested platforms stm32l011 stm32l053 stm32l072 stm32l052 murata cmwx1zzabz supported drivers eeprom m95640 sigfox s2lp tested on rcz1 murata cmwx1zzabz sx1276 tested on rcz1 rcz2 lorawan murata cmwx1zzabz sx1276 t h p bosh bme280 bmp280 omron 2smpb 02b infineon dps422 light maxim max44009 current battery pack maxim max17205 max17201 nfc st25dv block access serial console over ftm memory hall effect sl353 accelerometer stm lis2dh12 gnss quectel l80 l86 mediatek 3333 supported stacks lorawan semtech stack sigfox sigfox stack the second objective is to be able to port this sdk to different patform to make it a portable sdk the sdk have a it sdk directory where everything needs to be portable stm32l sdk contains all the subfunctions specific to this platform project organization previously the devel branch was the living branch and master was stable this way sound not optimal at least for my github commit graph so starting in 1 6 here is the new organization devel is a bit advanced on master for not yet tested commit master is the new devel branch containing tested commit but not yet verified you must not go in production with master version branch for each new version release start your project by configuring a skeleton with cube mx sys clock mux hsi16 watchdog configure iwdg with lsi 37khz iwdg window value 4095 iwdg prescaler value 256 rtc timer tim21 is enable with clock source internal for calibration lowpower lpuart1 usart1 usart2 async 9600 8 n 1 for wakeup clock setting needs to be hsi and max speed needs to be 9600 rtc wakeup activate clksource calendar internal wakeup clk config lsi rtc nvic interrupt activated async prediv 127 sync prediv 255 gpio wakeup activate the gpio as extinterrupt set with pull trigger en fall rise activate nvic exi1 15 adc select an adc like for temperature and vcc to get the needed headers when generating the project code generator select generate peripheral initalization as par of c h set all free pins as analog advanced settings check not generated function call for adc import the sdk this repository 1 clone this repository into the root of your project 2 for the next configuration you need to do it ifor both debug and release configuration 3 add in project properties c c general path symbol source location the repository itsdk src directory 4 add in project properties c c build settings tool settings mcu gcc compiler includes path add the itsdk inc directory 5 add in project properties c c build settings tool settings mcu gcc assembler includes path add the itsdk inc directory 6 in c c build settings build step pre build steps you can add the command touch workspace loc projname stm32 it sdk src it sdk console console c this will update the compile date on every build 7 you can get detail on size of code generated on build by adding a post build step arm none eabi size format sysv buildartifactfilename 8 you can take a loop on various stm32 code optimization https www disk91 com 2020 technology programming code optimization with stm32 cube ide also configure the sdk 1 copy one of the itsdk src project main c template file into core src project main c and make the modification you want to get started 2 copy the itsdk inc it sdk config h template file into core inc it sdk config h 3 copy the itsdk inc it sdk configdrivers h template file into core inc it sdk configdrivers h needed when using some of the drivers 4 copy the itsdk inc it sdk configsigfox h template file into core inc it sdk configsigfox h needed when using sigfox drivers 5 copy the itsdk inc it sdk configlorawan h template file into core inc it sdk configlorawan h needed when using lorawan drivers 6 edit these files and fill the different settings according to your environment and your choices modify the cube mx skeleton things to not forget once a cubemx project has been created gpio c during deep sleep all the gpios not listed in itsdk lowpower gpio x keep will be switched analog the gpio attached to devices i2c spi will be reconfigured when these device will be reactivated but the standard gpio will need to be reconfigured by default the wake up procedure call void mx gpio init void function created by cubemx if the default setting is good for you you can keep it but you will have to remove the gpio set reset functions another solution better is to create a function named void stm32l lowpowerrestoregpioconfig c void stm32l lowpowerrestoregpioconfig gpio ports clock enable hal rcc gpioa clk enable hal rcc gpiob clk enable hal rcc gpioc clk enable hal rcc gpioh clk enable reconfigure the gpio deep sleep have switched analog reactivate the interrupt line you need hal nvic setpriority exti0 1 irqn 0 0 hal nvic enableirq exti0 1 irqn hal nvic setpriority exti4 15 irqn 0 0 hal nvic enableirq exti4 15 irqn main c includes c user code begin includes include it sdk config h include it sdk itsdk h user code end includes main c mx iwdg init user code begin 2 itsdk setup user code end 2 infinite loop user code begin while while 1 user code end while user code begin 3 itsdk loop user code end 3 other modifications need to be done on every cubemx project regeneration gpio in gpio c cube mx is setting resetting the gpio state on init you need to manually comment the line in gpio c to avoid the pin to be modified on mcu wake up the other solution is to let the gpio init as is and add a function void stm32l lowpowerrestoregpioconfig containing the gpio reconfiguration after wakeup adc if you choose to not use adc optimized code for size remove generated adc c h and remove adc references in main c documentation the documentation about the different components is available in doc doc directory the function api are not documented in the h files but more generally in the c files work in progress examples the examples are published on different git repo to simplify the distribution of the sdk see example directory for a direct access to these ready to use projects version upgrade when upgrading the sdk from a new version the main impact is to add the new settings expected in the different configuration file please find the different settings added version after version from version 1 7 0 misc return code from i2c and spi moved from i2c xx to i2c xx and spi xx to spi xx eeprom interface have a userland area the config zone has been refactored to support version upgrade config h itsdk with gpio handler enable the internal gpio handler now you can disable it basically itsdk radio power offset add an offset to radio tx power to compensate antenna loss transparently global to all radios itsdk radio certif enable code and console options for radio certification configlorawan h itsdk lorawan rx2delay mod ms delay added to rx 2 window start for calibration from version 1 6 0 project settings add in project properties c c build settings tool settings mcu gcc assembler includes path add the itsdk inc directory config h itsdk with wdg allows to disable the watchdog itsdk with uart rxirq allows to enable an rx irq on serial communications with internal circular buffer itsdk with uart rxirq bufsz size of the rx irq circular buffer for each of the serial channel power of two itsdk with experimental activate or deactivate some experimental code by default set it to disable itsdk logger with seg rtt activate or deactive the segger rtt console see segger md file itsdk statemachine static state machine optimization with definition stored in flaash instead of ram configdriver h itsdk drivers with accel driver itsdk drivers accel lis2dh12 itsdk drivers with gnss driver itsdk drivers gnss quectel itsdk drivers max17205 csn to bat itsdk drivers o2smpb from version 1 5 0 config h itsdk adc optimize size itsdk adc oversampling itsdk default region confgidriver h itsdk drivers sl353 configlorawan h itsdk murata tcxo warmup itsdk murata wakeup time itsdk murata antsw rx bank typo fixed itsdk murata antsw rx pin typo fixed itsdk murata antsw txboost pin typo fixed itsdk murata antsw txrfo pin typo fixed header define name changed configsigfox h itsdk murata antsw rx bank typo fixed itsdk murata antsw rx pin typo fixed itsdk murata antsw txboost pin typo fixed itsdk murata antsw txrfo pin typo fixed itsdk sigfox if tx rcz1 itsdk sigfox if tx rcz3 itsdk sigfox if txrx rcz1 itsdk sigfox if txrx rcz3 itsdk murata tcxo warmup itsdk sx1276 sfxwakeup time itsdk sigfox maxpower itdsk sigfox rcz removed itsdk murata wakeup time license this code and itsdk are under gplv3 you can use it freely you can modify redistribute but you must publish your source code when included into a commercial product you have the following obligations the commercial product must include as part of the documentation website paper and written on the packaging the following sentence build thanks to disk91 iot sdk the commercial product documentation website paper a link to the disk91 iot sdk github repository and a link with all the modified and added source code the product includes following the gplv3 licence other licenses can be obtained by contacting me on disk91 com https www disk91 com non public licence allows you to be released from the previously described obligations | server |
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FaceSwapping | fase swapping step by step solution based on this solution https pysource com 2019 05 28 face swapping explained in 8 steps opencv with python 1 face detection step 1 imgs step 1 png 2 keypoints detection step 2 imgs step 2 png 3 face cropping step 3 imgs step 3 png 4 face triangulation step 4 imgs step 4 png 5 reconstruction new face step 5 idea imgs step 5 png step 5 gif imgs step 5 gif 6 pull up new face step 5 end imgs step 6 png 6 seamless cloning step 5 end imgs step 7 png examples ex1 imgs result two png techology part the most of the algorithms are written using opencv https pypi org project opencv python face detection haar cascade keypoint detection the cascade of regressors using dlib http dlib net based on this paper http www nada kth se sullivan papers kazemi cvpr14 pdf face triangulation simple triangulation reconstruction affine projection each triangles color alignment seamless cloning details http amroamroamro github io mexopencv opencv cloning demo html | cv2 opencv-python face-detection haar-cascade keypoint-detection face-swapping | ai |
html5-boilerplate | html5 boilerplate https html5boilerplate com build status https github com h5bp html5 boilerplate workflows build 20status badge svg https github com h5bp html5 boilerplate actions query workflow 3a 22build status 22 branch 3amain license https img shields io badge license mit lightgrey svg https github com h5bp html5 boilerplate blob main license txt npm downloads https img shields io npm dt html5 boilerplate svg https www npmjs com package html5 boilerplate github stars image https img shields io github stars h5bp html5 boilerplate svg label github 20stars https github com h5bp html5 boilerplate html5 boilerplate is a professional front end template for building fast robust and adaptable web apps or sites this project is the product of over 10 years of iterative development and community knowledge it does not impose a specific development philosophy or framework so you re free to architect your code in the way that you want homepage https html5boilerplate com source code https github com h5bp html5 boilerplate about this repository this repository is where html5 boilerplate is authored some of the tools files and processes that you see here are solely for the production of html5 boilerplate and are not part of html5 boilerplate for one example the gulpfile mjs https github com h5bp html5 boilerplate blob main gulpfile mjs script is used to build the project it s not part of the project itself the project we publish is represented by the contents of the dist folder everything else in this repository is used to author the project think of it this way in the same way that you don t clone vuejs core https github com vuejs core to create a vue js app you don t need to clone this repository to start a new site or app based on html5 boilerplate so if you re looking for a quick start template to build a web site or application look at the options in the quick start https github com h5bp html5 boilerplate quick start section of this document if you want to help us improve html5 boilerplate then you can start with the documentation here github contributing md which includes steps to clone this repo in order to get it set up for development quick start choose one of the following options using the create html5 boilerplate https github com h5bp create html5 boilerplate script instantly fetch the latest npm published package or any version available on npm with npx npm init or yarn create without having to install any dependencies running the following npx command installs the latest version into a folder called new site bash npx create html5 boilerplate new site cd new site npm install npm run start using our new template repository https github com h5bp html5 boilerplate template create a new github repository based on the latest code from the main branch of html5 boilerplate install with npm https www npmjs com npm install html5 boilerplate or yarn https yarnpkg com yarn add html5 boilerplate the resulting node modules html5 boilerplate dist folder represents the latest version of the project for end users depending on what you want to use and how you want to use it you may have to copy and paste the contents of that folder into your project directory download the latest stable release from here https github com h5bp html5 boilerplate releases download v8 0 0 html5 boilerplate v8 0 0 zip this zip file is a snapshot of the dist folder on windows mac and from the file manager on linux unzipping this folder will output to a folder named something like html5 boilerplate v8 0 0 from the command line you will need to create a folder and unzip the contents into that folder bash mkdir html5 boilerplate unzip html5 boilerplate zip d html5 boilerplate features a finely tuned starter template reap the benefits of 10 years of analysis research and experimentation by over 200 contributors designed with progressive enhancement in mind includes placeholder open graph elements and attributes an example package json file with webpack https webpack js org commands built in to jumpstart application development placeholder css media queries useful css helper classes default print styles performance optimized delete key friendly easy to strip out parts you don t need extensive documentation browser support html5 boilerplate supports the latest stable releases of all major browsers check the default configuration from browserslist https browsersl ist q defaults for more details on browsers and versions covered documentation take a look at the documentation table of contents docs toc md this documentation is bundled with the project which makes it available for offline reading and provides a useful starting point for any documentation you want to write about your project contributing hundreds of developers have helped to make the html5 boilerplate anyone is welcome to contribute github contributing md however if you decide to get involved please take a moment to review the guidelines github contributing md bug reports github contributing md bugs feature requests github contributing md features pull requests github contributing md pull requests license the code is available under the mit license license txt | html5-boilerplate html5 robust javascript css best-practices html | front_end |
MetPlus_PETS | metplus metplus is a non profit that aim to help the people of detroit and hopefully michigan to find a job that suits them the vision of the client is to create one application pets that would allow the users to find the a job and for companies to find employers public employment tracking system p e t s www petsworkforce com http www petsworkforce com a href https github com agileventures metplus pets wiki project overview project overview a br the pets system is a web platform where the job seekers and the companies can find each other the platform will try to match all the skills of the job seekers and the skills needed by the companies creating a pool of possible jobs or candidates a href https github com agileventures metplus pets wiki project overview read more a a href https github com agileventures metplus pets wiki project setup project setup a br find instructions for getting started a href https github com agileventures metplus pets wiki project setup here a note assumes you already have a working ruby on rails development environment a href https github com agileventures metplus pets wiki project workflow project workflow a br learn about our project workflow process a href https github com agileventures metplus pets wiki project workflow here a see the whole wiki a href https github com agileventures metplus pets wiki https github com agileventures metplus pets wiki a semaphore build status https semaphoreci com api v1 agileventures metplus pets 2 branches master badge svg https semaphoreci com agileventures metplus pets 2 build status https semaphoreci com api v1 agileventures metplus pets 2 branches development shields badge svg https semaphoreci com agileventures metplus pets 2 code climate code climate https codeclimate com github agileventures metplus pets badges gpa svg https codeclimate com github agileventures metplus pets test coverage test coverage https codeclimate com github agileventures metplus pets badges coverage svg https codeclimate com github agileventures metplus pets coverage waffle stories in progress https badge waffle io agileventures metplus tracker png label in 20progress title in 20progress http waffle io agileventures metplus tracker deployment with dokku 1 create the postgres database ssh dokku agileventures azure postgres create metplus pets production 1 create the application ssh dokku agileventures azure apps create metplus pets production 1 link mongo database with application ssh dokku agileventures azure postgres link metplus pets production metplus pets production 1 configure application ssh dokku agileventures azure config set 1 create remote to push application to git remote add production dokku agileventures azure metplus pets production 1 deploy to production git push production master | front_end |
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wmw-technical-test | we make websites technical test this is an entry level test that we require all front end developer candidates trial before proceeding into the final stage we do not pay or use any of the work produced on a commercial environment if you have come directly to this repository without a reference and curious to trial out this test then please contact developer wemakewebsites com to get yourself started brief you have been provided a link to a shopify development theme which contains only a basic header and also a list of unstyled product cards the link should contain a preview theme id your theme id suffixed at the end of the url we would like you to only style the featured collection section in the featured collection liquid file in your starter theme to match the supplied designs we have supplied both mobile and desktop designs but we would like you to have a think on how the components respond to the sizes in between the solution is open for interpretation the invision prototype also includes a style guide we recommend using the invision inspect tool this is an easy way to sample the colours copy font values and get the correct dimensions of each element in the design the src sections featured collection liquid contains the necessary liquid markup and content required for the design to function but you are encouraged to add in your own custom html liquid to make your build work with the designs tips we recommend using flickity https flickity metafizzy co or swiper js https swiperjs com to build the featured collection carousel one of the products have a badge sale tag that had been added behind the scenes try to use that tag in order to display the badge on the product in the carousel you can read more about product tags under the product object article and how to manipulate strings in the string filters article shown in the useful resources section below bonus the following instructions are not required for a successful submission they are purely for us to better measure your eye for detail and bringing a static design to life but even if the tasks are not required for submission we still try to make the tasks as fun and useful for both parties we will always provide feedback for the bonus tasks if we see room for improvement user interactions one of the most important things in ecommerce design is user experience through interactivity we would like you to have a think about how you could improve the experience by adding an extra layer of interactions javascript if time allows and you re up for a fun challenge then you can also look into creating a basic quick add functionality a standard quick add in ecommerce is when a user clicks on the add to cart button which will in turn add the product into the user s cart without refreshing the page you can find some relevant documentation for this task below in the useful resources section regarding making ajax calls in shopify there is also a featured collection js script in the src sections directory which already registers the section in shopify all your functionality should be contained in this file we encourage the use of es6 as opposed to jquery but is not mandatory you can test if the function has been successful by navigating to the https wmw technical test myshopify com cart page or by visiting the https wmw technical test myshopify com cart json url to see if a product data has been added to your cart getting started 1 create a self signed ssl certificate before getting started you will need to have created a self signed ssl certificate to serve your assets locally please follow this guide before proceeding create a self signed ssl certificate https github com shopify slate wiki 4 create a self signed ssl certificate 2 install project dependencies you will need to make a clone of this repo and run the following command in the project folder this will install the shopify theme dependencies as well as third party packages required for the local theme development yarn install 3 set up your env file you will need to edit the sample env file using the contents below in the project directory the password and theme id will have been emailed to you the myshopify com url to your shopify store slate store wmw technical test myshopify com the api password generated from a private app this will have been emailed to you slate password the id of the theme you wish to upload files too this will have been emailed to you slate theme id a list of file patterns to ignore with each list item separated by slate ignore files config settings data json 4 start node js express server and begin watching assets the command will compile the assets within src into a dist directory which are all the necessary files for your theme to run yarn start 5 start developing the terminal should provide you with a preview link to your theme and will automatically inject and refresh the browser when changes are made the preview link should like either of the following https localhost 3000 preview theme id theme id https 172 16 120 157 3000 preview theme id theme id if you are getting localhost or certificate issues then please follow this guide to troubleshoot the issue create a self signed ssl certificate https github com shopify slate wiki 4 create a self signed ssl certificate reviewing and submitting your results 1 deploy once you have finished and ready for a review then you will need to run the following command for us to see the results on your theme yarn deploy 2 let someone know when the deployment has finished then you can test the same preview link that you have been developing on inside any browser if all is good then contact the interviewer or daniel wemakewebsites com that the test is ready for review with a link to a github repo and we will get back to you as soon as we can regarding your results useful resources starter theme wiki https github com shopify starter theme liquid basics https help shopify com en themes liquid basics objects the collection object https help shopify com en themes liquid objects collection the product object https help shopify com en themes liquid objects product the variant object https help shopify com en themes liquid objects variant creating ajax calls with shopify https help shopify com en themes development getting started using ajax api string filters https help shopify com en themes liquid filters string filters system requirements you ll want to ensure you have the following already installed on your local machine before getting started with starter theme node the current lts long term support release we like to use a node version manager like nvm https github com creationix nvm npm 5 or yarn both of these package managers have ups and downs https blog risingstack com yarn vs npm node js package managers choose whichever you prefer follow the installation instructions for yarn https yarnpkg com en docs install or npm https www npmjs com get npm to make sure you re using the latest version | front-end-developer recruitment shopify technical-test | front_end |
ComputerVision-Essentials | computer vision essentials computer vision essentials computer vision essentials introduction introduction used libraries packages used librariespackages how to run how to run usage usage support support roadmap roadmap contributing contributing authors and acknowledgment authors and acknowledgment license license citation citation author author extra downloads extra downloads introduction according to wikipedia https en wikipedia org wiki computer vision computer vision wikipedia computer vision is an interdisciplinary scientific field that deals with how computers can gain high level understanding from digital images or videos from the perspective of engineering it seeks to understand and automate tasks that the human visual system can do computer vision tasks include methods for acquiring processing analyzing and understanding digital images and extraction of high dimensional data from the real world in order to produce numerical or symbolic information e g in the forms of decisions read more https en wikipedia org wiki computer vision computer vision wikipedia used libraries packages opencv opencv open source computer vision library is an open source computer vision and machine learning software library pixellib pixellib is a library created for performing image and video segmentation using few lines of code cvlib a simple high level easy to use open source computer vision library for python dlib dlib is a general purpose cross platform software library written in the programming language c pil pillow python imaging library is a free and open source additional library for the python programming language that adds support for opening manipulating and saving many different image file formats keras keras is the most used deep learning framework among top 5 winning teams on kaggle tensorflow tensorflow is a free and open source software library for machine learning pytessarct python tesseract is an optical character recognition ocr tool for python that is it will recognize and read the text embedded in images scikit image scikit image is an open source image processing library for the python programming language it includes algorithms for segmentation geometric transformations color space manipulation analysis filtering morphology feature detection and more matplotlib matplotlib is a cross platform data visualization and graphical plotting library for python and its numerical extension numpy how to run install python 3 6 create virtual envionment with pipenv bash python m pip install pipenv pipenv install r requirements txt pipenv shell note check the guide https www tensorflow org install for tenosflow installation for your cpu gpu for using tensorflow gpu install the cuda 11 0 and necessary libraries large models and files hosted on google drive for downloading them run utils py utils py bash python utils py usage computer vision allows the computer to perform the same kind of tasks as humans with the same efficiency there are a two main task which are defined below object classification in the object classification we train a model on a dataset of particular objects and the model classifies new objects as belonging to one or more of your training categories object identification in the object identification our model will identify a particular instance of an object for example parsing two faces in an image and tagging one as virat kohli and other one as rohit sharma img height 400px width 600px src media face detected jpeg support contributers roadmap contributing before submitting a bug please do the following perform basic troubleshooting steps make sure you are on the latest version if you are not on the most recent version your problem may have been solved already upgrading is always the best first step try older versions if you are already on the latest release try rolling back a few minor versions e g if on 1 7 try 1 5 or 1 6 and see if the problem goes away this will help the devs narrow down when the problem first arose in the commit log try switching up dependency versions if the software in question has dependencies other libraries etc try upgrading downgrading those as well authors and acknowledgment deepak raj https github com codeperfectplus pranjalmishra30 https github com pranjalmishra30 gloriousmusketeer https github com gloriousmusketeer bislara https github com bislara its harshil https github com its harshil farhan0syakir https github com farhan0syakir harshit saraswat https github com harshit saraswat https github com codeperfectplus opencv tutorial graphs contributors license for open source projects under mit license citation st fan van der walt johannes l sch nberger juan nunez iglesias fran ois boulogne joshua d warner neil yager emmanuelle gouillart tony yu and the scikit image contributors scikit image image processing in python peerj 2 e453 2014 https doi org 10 7717 peerj 453 coelho l p 2013 mahotas open source software for scriptable computer vision journal of open research software 1 1 e3 doi http dx doi org 10 5334 jors ac author project computer vision essentials language python github https github com codeperfectplus website http codeperfectplus herokuapp com extra downloads 1 facedetection caffee models | python opencv opencv-python opencv-python-tutorial opencv-tutorial computer-vision classification contour-detection image-processing image-recognition blob-detection edge-detection face-detection template-matching object-detection image-filters sift-algorithm hog-features-extraction feature-extraction-algorithm | ai |
mastering-llms | mastering large language models https images unsplash com photo 1591696331111 ef9586a5b17a ixlib rb 4 0 3 ixid m3wxmja3fdb8mhxwag90by1wywdlfhx8fgvufdb8fhx8fa 3d 3d auto format fit crop w 1000 q 80 mastering large language models llms this repository provides a detailed roadmap for mastering large language models llms the roadmap is divided into several sections each focusing on a specific aspect of llms each section includes a list of resources for learning each topic including books online courses and tutorials let s embark on this exciting journey to master llms table of contents mastering large language models llms mastering large language models llms table of contents table of contents nlp basics nlp basics deep learning for nlp deep learning for nlp transformers self attention for nlp transformers self attention for nlp large language models llms courses large language models llms courses building llm projects applications building llm projects applications extra blogs extra blogs nlp basics before diving into llms it s essential to have a solid understanding of natural language processing nlp here are some resources to help you get started 1 natural language processing with python https www amazon com natural language processing python analyzing dp 0596516495 this book offers a highly accessible introduction to natural language processing the field that supports a variety of language technologies from predictive text and email filtering to automatic summarization and translation github https github com sturzgefahr natural language processing with python analyzing text with the natural language toolkit 2 practical natural language processing https www amazon com practical natural language processing pragmatic dp 1492054054 a practical guide to processing and understanding text data with nlp github https github com practical nlp practical nlp code 3 analytics vidhya nlp tutorial https www analyticsvidhya com blog 2017 01 ultimate guide to understand implement natural language processing codes in python a comprehensive tutorial on nlp basics 4 github nlp tutorial https github com keon awesome nlp a collection of nlp resources 5 speech and language processing https web stanford edu jurafsky slp3 this is an introductory textbook on nlp and computational linguistics written by leading researchers in the field deep learning for nlp once you have a solid understanding of nlp basics the next step is to learn about deep learning for nlp here are some resources to assist you 1 deep learning specialization https www coursera org specializations deep learning this specialization by andrew ng on coursera will help you understand the capabilities challenges and consequences of deep learning and prepare you to participate in the development of leading edge ai technology 2 deep learning for nlp by stanford university http web stanford edu class cs224n this course provides a thorough introduction to cutting edge research in deep learning for nlp through lectures assignments and a final project students will learn the necessary skills to design implement and understand their own neural network models 3 applied natural language processing by uc berkeley https people ischool berkeley edu dbamman info256 html this course examines the use of natural language processing as a set of methods for exploring and reasoning about text as data focusing especially on the applied side of nlp using existing nlp methods and libraries in python in new and creative ways rather than exploring the core algorithms underlying them 4 natural language processing specialization https www coursera org specializations natural language processing this specialization by deeplearning ai on coursera will equip you with the machine learning basics and state of the art deep learning techniques needed to build cutting edge nlp systems 5 deep learning book http www deeplearningbook org this book by ian goodfellow yoshua bengio and aaron courville provides a comprehensive introduction to the field of deep learning 6 deep learning for coders with fastai and pytorch https www amazon com deep learning coders fastai pytorch dp 1492045527 this book provides a practical introduction to deep learning and artificial intelligence it emphasizes the use of the fastai library which simplifies the process of building complex models free book https fastai github io fastbook2e 7 deep learning for nlp pytorch tutorials https pytorch org tutorials beginner deep learning nlp tutorial html these tutorials provide a good starting point for understanding how to use pytorch for nlp tasks transformers self attention for nlp the transformer model which uses self attention mechanisms forms the backbone of most llms here are some resources to help you to understand transformers and self attention mechanisms 1 the illustrated transformer http jalammar github io illustrated transformer this blog post by jay alammar provides a visual and intuitive understanding of how transformers work 2 the annotated transformer http nlp seas harvard edu 2018 04 03 attention html this blog post annotates the paper that introduced transformers attention is all you need and provides a line by line implementation in pytorch 3 transformers for natural language processing https www amazon com transformers natural language processing understanding dp 1800565790 this book explains the transformer architecture in depth and how it is used in real world applications 4 hugging face transformers https huggingface co learn nlp course chapter1 1 hugging face provides a library of pre trained transformer models for direct use in nlp tasks their documentation and tutorials are a great resource for learning how to use transformers in practice 5 attention is all you need original paper https arxiv org abs 1706 03762 this is the original paper that introduced the transformer model it s a bit dense but it s a must read if you want to understand the details of the model 6 a gentle introduction to transformers https www youtube com watch v 4bdc55j80l8 this video by luis serrano provides a gentle and intuitive explanation of transformers large language models llms courses 1 cos 597g fall 2022 understanding large language models princeton university https www cs princeton edu courses archive fall22 cos597g 2 cs324 lecture notes winter 2022 stanford university https stanford cs324 github io winter2022 lectures building llm projects applications after understanding the theoretical aspects it s time for some hands on experience here are some resources to help you build projects and applications using llms 1 hugging face model hub https huggingface co models the hugging face model hub is a repository of pre trained models that you can use directly in your projects 2 chatgpt https openai com chatgpt chatgpt is a state of the art llm developed by openai you can use the chatgpt api to integrate it into your applications 3 gpt 3 sandbox https gpt3demo com this is a platform where you can experiment with different applications of gpt 3 4 github gpt 3 examples https github com openai gpt 3 this github repository contains examples of applications built using gpt 3 5 awesome gpt3 github https github com elyase awesome gpt3 this github repository contains examples of creative writing applications built using gpt 3 6 gpt 3 sandbox by openai https beta openai com this is a platform provided by openai where you can experiment with different applications of gpt 3 7 full stack llm bootcamp https fullstackdeeplearning com llm bootcamp 8 llm university by cohere https docs cohere com docs llmu 9 deeplearning ai short courses https www deeplearning ai short courses 1 chatgpt prompt engineering for developers 0 chatgpt prompt engineering for developers 2 langchain for llm application development 1 langchain for llm application development 3 how diffusion models work 2 how diffusion models work 4 building systems with chatgpt api 3 building systems with chatgpt api 10 nano gpt https github com karpathy nanogpt nano version of gpt made by andrej karpathy to understand how llm models work from the ground up extra blogs 1 lil log https lilianweng github io 2 jay alammar https jalammar github io | ai |
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PromptGraph | div align center h1 promptgraph h1 div div align center img width 800px src https raw githubusercontent com promptslab promptgraph main extra kb prompt jpg div h2 align center promptgraph h2 p align center p align center llm graphs fire br solve geometric graph problems with large language models p p h4 align center under development join discord for promptgraph discussion a href https github com promptslab promptgraph blob main license img src https img shields io badge license apache 2 0 blue svg alt promptgraph is released under the apache 2 0 license a a href http makeapullrequest com img src https img shields io badge prs welcome brightgreen svg style flat square alt http makeapullrequest com a a href https discord gg m88xfymbk6 img src https img shields io badge discord community orange alt community a h4 h6 align center small small image source docs cohere ai small small h6 | ai |
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Ecommerce-Website | ecommerce website | server |
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DVID | dvid damn vulnerable iot device the first opensource vulnerable designed iot device this project is currently in development all details are available on dvid website http dvid eu http dvid eu project overview this iot device is designed by my own and published on opensource the main objective is to provide to each interrested people a vulnerable board to improve their skill in iot hacking the board core is composed by a atmega328p and a oled screen for each vulnerabilities a firmware could be flashed on the atmega328p in order to offer a specific vulnerable environment there is also connection port like uart bluetooth 2 4ghz and wifi in each vulnerability a specific extension board must be plugged to the board hacking required attacks tools like usbasp and usbuart board you can buy the board on marketplace available soon or build it yourself with gerber files the board is shipped with theses three packages board only and component reference naked board only you must buy all component yourself and solder all component soldered board without attack component and extention soldered board but you must buy all external tools like usbuart usbasp extention board full package everything needed soldered board external board and attack tools contribution if you want to contribute to the project don t hesitate to open a pull request by the way about firmware compilation you need to compile for atmega328p breadboard 8mhz in order to do that just add this board to the arduino hardware arduino avr board txt file bash atmega328bb name atmega328 on a breadboard 8 mhz internal clock atmega328bb upload protocol arduino atmega328bb upload maximum size 30720 atmega328bb upload speed 57600 atmega328bb bootloader low fuses 0xe2 atmega328bb bootloader high fuses 0xda atmega328bb bootloader extended fuses 0x05 atmega328bb bootloader file atmega atmegaboot 168 atmega328 pro 8mhz hex atmega328bb bootloader unlock bits 0x3f atmega328bb bootloader lock bits 0x0f atmega328bb build mcu atmega328p atmega328bb build f cpu 8000000l atmega328bb build core arduino arduino atmega328bb build variant arduino standard atmega328bb bootloader tool arduino avrdude atmega328bb upload tool arduino avrdude write ups many thanks to ghozt https twitter com ghozt shoxxdj https twitter com shoxxdj and podalirius https twitter com podalirius in french hardware find the datasheet https shoxxdj fr dvid hardware find the datasheet https shoxxdj fr dvid hardware find the datasheet find the datasheet https podalirius net fr writeups dvid writeup 01 hardware find the datasheet https podalirius net fr writeups dvid writeup 01 hardware find the datasheet firmware default password https shoxxdj fr dvid firmware defaultpassword https shoxxdj fr dvid firmware defaultpassword default password https podalirius net fr writeups dvid writeup 03 firmware default password https podalirius net fr writeups dvid writeup 03 firmware default password bluetooth advertising https podalirius net fr writeups dvid writeup 04 bluetooth advertise https podalirius net fr writeups dvid writeup 04 bluetooth advertise characteristics https podalirius net fr writeups dvid writeup 05 bluetooth characteristics https podalirius net fr writeups dvid writeup 05 bluetooth characteristics characteristics2 https podalirius net fr writeups dvid writeup 06 bluetooth characteristics2 https podalirius net fr writeups dvid writeup 06 bluetooth characteristics2 in english hardware find the datasheet https podalirius net en writeups dvid writeup 01 hardware find the datasheet https podalirius net en writeups dvid writeup 01 hardware find the datasheet firmware hardcoded password https podalirius net en writeups dvid writeup 02 firmware hardcoded password https podalirius net en writeups dvid writeup 02 firmware hardcoded password default password https podalirius net en writeups dvid writeup 03 firmware default password https podalirius net en writeups dvid writeup 03 firmware default password bluetooth advertising https podalirius net en writeups dvid writeup 04 bluetooth advertise https podalirius net en writeups dvid writeup 04 bluetooth advertise characteristics https podalirius net en writeups dvid writeup 05 bluetooth characteristics https podalirius net en writeups dvid writeup 05 bluetooth characteristics characteristics2 https podalirius net en writeups dvid writeup 06 bluetooth characteristics2 https podalirius net en writeups dvid writeup 06 bluetooth characteristics2 part list part quantity total cost buying link board 1 3 available soon or build yourself jumper double row female 2x4p 2 1 https www banggood com 30pcs 2 54mm 2x4p 8p double row female straight pin header needle socket pin strip p 1348262 html jumper single row male 4 1 https www banggood com 10pcs 40 pin 2 54mm male female sil socket row strip pcb connector p 953436 html 28 pins socket for atmega328p 1 0 5 https www banggood com 50pcs 28 pins ic dip 2 54mm wide integrated circuit sockets adaptor p 1211042 html oled screen 4pin white 1 5 https www banggood com 1 3 inch 4pin white oled lcd display 12864 iic i2c interface module for arduino p 1067874 html atmega328p 1 3 https www banggood com dip28 atmega328ppu mcu ic chip with arduino uno bootloader p 932159 html 5v to 3v power supply 1 1 https www banggood com 5v to 3 3v dc dc step down power supply buck module ams1117 800ma p 933674 html status led 1 0 5 https www banggood com 10pcs 5mm 3000 6000mcd led bright decoration torch toy light green p 73175 html total 15 attack tools and extention board part quantity total cost buying link usbuart 1 3 https www banggood com cp2102 usb to ttl module p 1263924 html usbasp adapter 1 4 https www banggood com 3 3v 5v usbasp usbisp avr programmer downloader atmega8 atmega128 with download cable p 1179967 html at 09 ble module 1 2 https www banggood com at 09 4 0 ble wireless bluetooth module serial port cc2541 compatible hm 10 module connecting single chip microcomputer p 1455191 html csr ble adapter 1 4 https www banggood com mini wireless dongle csr 4 0 bluetooth adapter v4 0 usb 2 03 0 for win 7810xp for vista 3264 p 1132661 html jumper wire female female 1 2 https www banggood com 120pcs 20cm male to female female to female male to male color breadboard jumper cable dupont wire combination for arduino p 974006 html esp8266 1 2 https www banggood com upgraded version 1m flash esp8266 esp 01 wifi transceiver wireless module p 979509 html total 17 kit contents jpg troubleshooting in case of frying your board you may encounter some issue with new version of chinese component this part is dedicated to allow you to start to discuss with your component and put it in the ready to hack configuration bluetooth at 09 last received parcel contains a new version of the at 09 named mlt bt05 this new version natively discuss over uart with 115200 baudrate the dvid can discuss to peripherial with 9600 baudrate in order to change that follow those steps connect your at 09 to your uart dongle rx tx tx rx vcc vcc and gnd gnd open a serial monitor and type at baud4 if you receive ok your ble peripherial is ready to hack atmega328p last received parcel contains a new version of atmega328p this version seems to be virgin and allow communication with very slow sck in order to modify the internal configuration fuse follow those steps solder or shortcut the jumper 3 on the usbasp flashing dongle type this command to be sure that you can communicates with the broken atmega328p avrdude v patmega328p cusbasp if you receive hfuse reads d9 and lfuse reads ff your device is already ready to hack if not type this command avrdude v patmega328p u lfuse w 0xe2 m cusbasp you can type again avrdude v patmega328p cusbasp to be sure that fuse configuration changed you can not unsolder or remove shortcut on the jumper 3 if you don t remove it flashing process will take 20 more times you can now flash all firmware on you dvid | server |
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IoT | iot br iar linux qt android br br br 1 br 2 br 3 br 4 br | server |
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workshop1 | getting started with create react app this project was bootstrapped with create react app https github com facebook create react app available scripts in the project directory you can run npm start runs the app in the development mode open http localhost 3000 http localhost 3000 to view it in your browser the page will reload when you make changes you may also see any lint errors in the console npm test launches the test runner in the interactive watch mode see the section about running tests https facebook github io create react app docs running tests for more information npm run build builds the app for production to the build folder it correctly bundles react in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes your app is ready to be deployed see the section about deployment https facebook github io create react app docs deployment for more information npm run eject note this is a one way operation once you eject you can t go back if you aren t satisfied with the build tool and configuration choices you can eject at any time this command will remove the single build dependency from your project instead it will copy all the configuration files and the transitive dependencies webpack babel eslint etc right into your project so you have full control over them all of the commands except eject will still work but they will point to the copied scripts so you can tweak them at this point you re on your own you don t have to ever use eject the curated feature set is suitable for small and middle deployments and you shouldn t feel obligated to use this feature however we understand that this tool wouldn t be useful if you couldn t customize it when you are ready for it learn more you can learn more in the create react app documentation https facebook github io create react app docs getting started to learn react check out the react documentation https reactjs org code splitting this section has moved here https facebook github io create react app docs code splitting https facebook github io create react app docs code splitting analyzing the bundle size this section has moved here https facebook github io create react app docs analyzing the bundle size https facebook github io create react app docs analyzing the bundle size making a progressive web app this section has moved here https facebook github io create react app docs making a progressive web app https facebook github io create react app docs making a progressive web app advanced configuration this section has moved here https facebook github io create react app docs advanced configuration https facebook github io create react app docs advanced configuration deployment this section has moved here https facebook github io create react app docs deployment https facebook github io create react app docs deployment npm run build fails to minify this section has moved here https facebook github io create react app docs troubleshooting npm run build fails to minify https facebook github io create react app docs troubleshooting npm run build fails to minify | cloud |
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machine-learning | kaggle 1 keras https github com hadxu machine learning tree master dive into keras vgg16 2 pytorh https github com hadxu machine learning tree master pytorch tutorial 3 https github com hadxu machine learning tree master porn norm 4 tensorflow https github com hadxu machine learning tree master 5 web https github com hadxu machine learning tree master flask imagenet 6 https github com hadxu machine learning tree master face detection and emotion 7 deeplearning https github com hadxu machine learning tree master coursera 8 https github com hadxu machine learning tree master text 20mining 9 https github com hadxu machine learning tree master simple image search 10 cifar10 keras https github com hadxu machine learning tree master cifar10 keras 11 https github com hadxu machine learning tree master aind cv facialkeypoints 12 mlp numpy https github com hadxu machine learning blob master pure numpy mlp mlp 20 e7 ba afnumpy e5 ae 9e e7 8e b0 ipynb 13 flask https github com hadxu machine learning tree master flask neural artistic style 14 pong https github com hadxu machine learning tree master pong 20v0 15 facebox pytorch https github com hadxu machine learning tree master faceboxes code 16 pytorch4nlp https github com hadxu machine learning tree master nlp pytorch 17 cython tutorial https github com hadxu machine learning tree master cython tutorial 18 nlp paper bert transformer https github com hadxu machine learning tree master nlp pytorch nlp implement | machine-learning python-3 deep-learning | ai |
bssdb | bssdb an embedded kv database library designed for modern hardware distributed systems and data with natural sharding this is an extremely early stage project without any tests or attention paid to stability please don t use it just yet but i d love your help building it goals support running many small to mid sized isolated databases in a single process scale well for naturally sharded data make replicating writes even across devices really fast when only one writing thread exists globally by only requiring a search to perform a write on the leader not replicas support an efficient encoding for sending changes and snapshots over the network be very performant on modern ssds point in time consistent reads atomic writes within a database non goals concurrent writer transactions optimizing performance on hard drives bssd makes liberal use of random reads and writes optimizing performance for large databases that can t be sharded licensing mit license note we use rio for linux io to take advantage of io uring which is gpl d your use of this library may be subject to the gpl where the gpl applies alternative backronyms b tree on speedy ssds database ben s super substandard database basic singe threaded sendable write database boring slow sad database | os |
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blob | blob p align center img width 460 src assets blob png p self reference is all you need definition blob is a powerful tool that uses language large models llms to assist in the creation and maintenance of software projects it is designed to provide a more intuitive and efficient way to work with source code enabling developers to focus on their ideas and logic rather than the technicalities of programming blob is still in the experimental phase and may not yet be able to handle large scale projects but we are actively working on improving its capabilities and scalability currently blob utilizes openai s gpt 3 as its underlying engine but we are also exploring the integration of other open source technologies through the use of adapters our ultimate goal is to make blob a versatile and adaptable tool that can be used in a wide range of projects and environments warning please note that blob is currently under development and is not yet suitable for production use while you are welcome to try it out and provide feedback we caution that it may have an incomplete implementation and may not function as intended our top priorities at this time are to improve the documentation and release stable binaries please check back with us for updates on the progress of these efforts main idea the main idea behind blob is to create a natural language reversible representation of the file struct and content of a software project this way we can ask gpt 3 to generate a new file structure based on natural language instructions while we have tried different representations the most simple and effective so far is a tree like representation of the file structure for example imagine you have a project in a folder called playground with the following structure playground source ts example tsx to add a new file called hello tsx to the same folder you can use the following command bash blob do add a new file called hello tsx the result will be playground source ts example tsx hello tsx or if you want to bootstrap a new next js project with typescript you can do so with the following command bash blob do bootstrap a new nextjs project this will result in the following file structure nextjs starter gitignore readme md package json pages app tsx document tsx index tsx about tsx public favicon ico logo png robots txt src components header tsx layout tsx styles global css tsconfig json to move the pages folder into the src folder use the following command bash blob do move the pages folder into the src folder this will result in the following file structure nextjs starter gitignore readme md package json public favicon ico logo png robots txt src pages app tsx document tsx index tsx about tsx components header tsx layout tsx styles global css tsconfig json usage to interact with your blob project you can use the blob binary you can see all the available commands by using the help flag bash blob help before you can use blob you will need to set the openai api key environment variable to your openai api key you can get one here you can set this variable using an env file or directly in your shell by default the current directory is assumed as the context for blob but you can specify a different directory using the path or p flag to perform a specific action or feature you can use the blob do command followed by a natural language instruction for example bash blob do bootstrap a new nextjs project bash blob do add some basic components for a design system bash blob do add a new page to the project and put a big hello world in the center of this page bash example provided by chatgpt set up a new react project with create react app blob do bootstrap a new react project using create react app bash example provided by chatgpt blob do add a new component called button to the project bash example provided by chatgpt blob do update the layout of the header component to use a fixed position at the top of the page bash example provided by chatgpt blob do remove the unused component called footer from the project these examples show how you can use the do command to perform a wide range of actions on your project from setting up new projects and adding components to modifying and deleting existing elements to help improve the quality of the model s predictions you can use the define command to provide definitions for terms related to your project simply type define followed by the definition this sentence will be used as the self project definition for example bash blob define this project is a nextjs starter with typescript tailwindcss trpc and chakra ui bash example provided by chatgpt blob define this project is an online platform for booking fitness classes bash example provided by chatgpt blob define this project is a social media app for connecting with friends and family bash example provided by chatgpt blob define this project is an ecommerce website for selling handmade crafts bash example provided by chatgpt blob define this project is a mobile game about matching colors and shapes bash example provided by chatgpt blob define this project is a news website for aggregating and curating articles from multiple sources you can use the define command as many times as needed to add definitions for different terms in your project these definitions will be used by the model to better understand the context and requirements of your instructions when using the do command using the define command in this way allows you to provide a concise self contained definition of your project that can be used by the model to better understand the context and requirements of your instructions when using the do command you can use the define command as many times as needed to add definitions for different terms or concepts in your project bash sudo cp target release blob usr local bin | chatgpt openai-api software-engineering lowcode artificial-life rust | ai |
ML-Webinar | about the course this repository is for use with the pearson publishing safari live webinars beginner machine learning with scikit learn diving deeper into machine learning with scikit learn advanced machine learning with scikit learn versions of this material are used by other training provided by david mertz and kdm training http kdm training if you have attended one of the webinars using this material i encourage you to complete the survey on it at machine learning with scikit learn survey https goo gl pghpzd as folks fill this out we will fold back the updated answers into the dataset used in the lessons themselves installing training materials before attending this course please configure the environments you will need within the repository find the file requirements txt to install software using pip or the file environment yml to install software using conda i e bash conda env create f environment yml conda activate pearson ml pearson ml jupyter notebook outline ipynb or bash pip install r requirements txt jupyter notebook outline ipynb a quicker way to do this is probably to use it within binder just launch https mybinder org v2 gh davidmertz ml webinar git head recommended reading machine learning with scikit learn https github com davidmertz ml webinar video machine learning with scikit learn livelessons https www oreilly com library view machine learning with 9780135474198 by david mertz hands on machine learning with scikit learn and tensorflow concepts tools and techniques to build intelligent systems by aur lien g ron deep learning with python by francois chollet introduction to machine learning with python a guide for data scientists by by andreas c m ller sarah guido python data science handbook essential tools for working with data by jake vanderplas documentation of scikit learn https scikit learn org stable documentation html | ai |
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matterial | matterial ui a design system by matt berti https brti dev setup first install the package in your project directory npm i matterial then import main css into your app entry in the appropriate load order on a next js app you would do something like the following jsx pages app tsx import type appprops from next app import normalize css if using import node modules matterial dist main css import styles custom scss overwrite main css export default function myapp component pageprops appprops return component pageprops docs visit official documentation https matterial brti dev contributing feel free to file a pr if you want to help improve this project license apache 2 0 built with reach ui https reach tech react typescript sass used to build brti dev https brti dev boat dadddy https boatdaddy app | css sass scss typescript | os |
beginners-series-blockchain | beginner s series to blockchain overview this repository supports the beginner s series to blockchain the goal of this series is to explain and demonstrate the foundations of blockchain principles blockchain has been one of the most exciting emerging technologies in the past decade and will continue to be a significant technology for the next decade to come this series is for everyone no matter what your career or academic background is experience with some level of programming in a language like c java or python would be helpful but is absolutely not required what you ll learn about in this series history of blockchain evolution of cryptocurrency blockchain nodes mining consensus algorithms blockchain use cases bitcoin ethereum solidity smart contracts openzeppelin azure solutions next steps if you want to follow along with the course you can try out the demos on your own with the contract example in this repository and the resources shared below helpful resources to continue your learning you can refer to the following resources microsoft specific blockchain development learning path https docs microsoft com learn paths ethereum blockchain development wt mc id blockchainbeginner ch9 niner slides for this series blockchain 20for 20beginners pdf beginner s series to blockchain videos https aka ms beginnerseriestoblockchain external resources ethereum website https ethereum org bitcoin website https bitcoin org en bitcoin whitepaper https bitcoin org bitcoin pdf openzeppelin website https openzeppelin com microsoft azure blockchain website https docs microsoft com azure blockchain solidity website https solidity readthedocs io blockchain explorer https www blockchain com explorer coinbase website http coinbase com truffle suite website https www trufflesuite com contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla opensource microsoft com when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g status check comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments trademarks this project may contain trademarks or logos for projects products or services authorized use of microsoft trademarks or logos is subject to and must follow microsoft s trademark brand guidelines https www microsoft com en us legal intellectualproperty trademarks usage general use of microsoft trademarks or logos in modified versions of this project must not cause confusion or imply microsoft sponsorship any use of third party trademarks or logos are subject to those third party s policies | blockchain |
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cvision-algorithms | cvision algorithms collection of computer vision algorithms implemented in matlab algorithms seam carving seam carving for content aware image processing by s avidan a shamir 2007 algorithm and code are described in details http kirilllykov github io blog 2013 06 06 seam carving algorithm bilateral filter bilateral filtering for gray and color images by c tomasi r manduchi 1998 fast bilateral filter a fast approximation of the bilateral filter using a signal processing approach by s paris f durand 2006 retinex retinex by two bilateral filters by m elad 2005 shape from shading algorithm by horn and ikeuchi the variational approach to shape from shading by b horn and m brooks 1985 optical flow algorithm by horn shunck for an array of input images determining optical flow by horn and schunck 1980 generator of random surface and closed curve auxiliary code | ai |
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OpenCv-for-Android-Color-Detection | opencv for android color detection computer vision tutorial this program implements basic color detection using opencv for android this program accompanies the tutorial linked below showing detailed instructions on how to complete the following using android studio download and import opencv into android studio project emulate android camera using webcam and virtual device display camera feed using opencv library process camera feed frames in real time with opencv process color in image on touch event http timrobinson space | ai |
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quantum | tensorflow quantum docs images logo tf quantum circle jpg tensorflow quantum https www tensorflow org quantum tfq is a python framework for hybrid quantum classical machine learning that is primarily focused on modeling quantum data tfq is an application framework developed to allow quantum algorithms researchers and machine learning applications researchers to explore computing workflows that leverage google s quantum computing offerings all from within tensorflow motivation quantum computing at google has hit an exciting milestone with the achievement of quantum supremacy https www nature com articles s41586 019 1666 5 in the wake of this demonstration google is now turning its attention to developing and implementing new algorithms to run on its quantum computer that have real world applications https ai googleblog com 2019 10 quantum supremacy using programmable html to provide users with the tools they need to program and simulate a quantum computer google is working on cirq https github com quantumlib cirq cirq is designed for quantum computing researchers who are interested in running and designing algorithms that leverage existing imperfect quantum computers tensorflow quantum provides users with the tools they need to interleave quantum algorithms and logic designed in cirq with the powerful and performant ml tools from tensorflow with this connection we hope to unlock new and exciting paths for quantum computing research that would not have otherwise been possible installation see the installation instructions https github com tensorflow quantum blob master docs install md examples all of our examples can be found here in the form of python notebook tutorials https github com tensorflow quantum tree master docs tutorials report issues report bugs or feature requests using the tensorflow quantum issue tracker https github com tensorflow quantum issues we also have a stack overflow tag https stackoverflow com questions tagged tensorflow quantum for more general tfq related discussions in the meantime check out the install instructions docs install md to get the experimental code running contributing we are eager to collaborate with you tensorflow quantum is still a very young code base if you have ideas for features that you would like added feel free to check out our contributor guidelines https github com tensorflow quantum blob master contributing md to get started references if you use tensorflow quantum in your research please cite tensorflow quantum a software framework for quantum machine learning arxiv 2003 02989 2020 https arxiv org abs 2003 02989 | ai |
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TAOS-CI | github license https dmlc github io img apache2 svg license gitter https img shields io gitter room nnsuite taos ci github repo size https img shields io github repo size nnsuite taos ci github issues https img shields io github issues nnsuite taos ci github pull requests https img shields io github issues pr nnsuite taos ci github contributors https img shields io github contributors nnsuite taos ci build status http nnsuite mooo com taos ci ci taos documentation ci doc doxygen documentation md contributing ci doc contributing md chat room https gitter im login release notes https github com nnstreamer taos ci releases introduction img src image taos ci png border 0 img taos ci is an automated project coordinator to achieve review less merge faster with a tool based review system it accelerates a software development based on the github webhook api we aim to prevent a regression find bugs and reduce a nonproductive review process due to incorrect prs in https github com actually submitting incorrect prs is a pita in case of continuous integration basically prs causing regressions will not be automatically merged as a result it drastically reduces the existing burdens of reviewers minimize a nonproductive review process provide a test automation both build and run prevent a performance regression find bugs at a proper time before merging buggy codes generate a doxygen based developer manual support modulable facilities with plug in interface integrate the existing opensource tools easily verify an integrity of a package by supporting a platform build support multiple operating systems as follows ubuntu https www ubuntu com tizen https source tizen org yocto https www yoctoproject org android https source android org tbd tbi img src image screenshot01 png border 0 width 350 height 250 img img src image screenshot03 png border 0 width 350 height 250 img goals the proposed system supports a light weight code review automation approach to support a desktop computer based servers that have out of date cpus and low memory capacity also if you want to enable your project specific ci facilities it will be easily customizable for your github repository because it just requires apache php and bash packages especially we are mainly concentrating on the following three goals among a number of action items automating tests both build and run preventing performance regression finding potential bugs at a proper time maintenance please refer here ci doc maintenance md publications geunsik lim myungjoo ham jijoong moon wook song sangjung woo and sewon oh a href https ieeexplore ieee org document 8662017 taos ci lightweight modular continuous integration system for edge computing a 37th ieee international conference on consumer electronics icce las vegas usa jan 2019 doi https zenodo org badge doi 10 5281 zenodo 7338593 svg https doi org 10 5281 zenodo 7338593 geunsik lim myungjoo ham and jaeyun jung a href http www riss kr link id a106329692 vtb cloud based testbed for on device ai a proc of kiise korea computer congress jeju korea jun 2019 best presentation paper major domestic conference in korea geunsik lim myungjoo ham and jaeyun jung a href https doi org 10 5626 ktcp 2020 26 1 12 vts virtual testbed system for cloud based on device ai application a kiise transactions on computing practices computing practices and letter cpl jan 2020 invited paper major domestic journal in korea geunsik lim myungjoo ham jijoong moon and wook song a href https conf researchr org home icse 2021 lightsys lightweight and efficient ci system for improving integration speed of software a 43rd acm ieee international conference on software engineering icse seip madrid spain virtual conference due to covid 19 may 2021 top tier li br how to install please refer to how to install taos ci ci doc how to install taos ci md how to use new ci module please refer to how to use new ci module ci doc how to use taos ci module md now available facilities are as following pre build group before a build file size new line no body signed off clang format doxygen timestamp hardcoded path executable rpm spec cppcheck pylint indent resource checker and so on post build group after a build ubuntu builder tizen builder yocto builder and android coming soon self assessment note that you have to execute a self assessment before submitting your pr please refer to how to build a package ci doc self assessment before submitting pr md terminology ci continuous integration cd continuous deployment pr pull request tbd to be defined tbi to be implemented license apache license 2 0 license reference webhook api of github https developer github com webhooks a painless self hosted git service gogs https gogs io free file transfer software curl https curl haxx se docs papers icse2018 modern code review a case study at google https dl acm org citation cfm id 3183525 icse2015 do not find bugs how the current code review best practice slows us down https dl acm org citation cfm id 2819015 ifipaict the impact of a low level of agreement among reviewers in a code review process https link springer com chapter 10 1007 978 3 319 39225 7 8 ase2014 automated unit test generation for classes with environment dependencies https dl acm org citation cfm id 2642986 fse2016 why we refactor confessions of github contributors https dl acm org citation cfm id 2950305 fse2016 factors influencing code review processes in industry https dl acm org citation cfm id 2950323 | continuous-integration integration-speed resource-management reveiw-automation hacktoberfest | os |
Faster_OpenCV_4_Raspberry_Pi | faster opencv for raspberry pi leverage all your cpus power in opencv by using tbb neon and vfpv3 libraries since i ve already compiled this on my own raspberry pi i made it available on github save countless of compile time by just installing these debs enjoy what is this a pre compiled opencv 4 4 0 for raspberry pi optimized for deep learning computer vision applications neon vfpv3 tbb turned on bindings for python 2 and python 3 are also included for detailed build informations click here build information txt created with opencv cpack targets tested on raspberry pi 3 using raspbian buster debian 10 for raspbian stretch click here https github com dlime faster opencv 4 raspberry pi releases tag stretch 410 how much faster performance tests have been made in this great blog article https www pyimagesearch com 2017 10 09 optimizing opencv on the raspberry pi which led to an approximate 30 increase in speed and of over 48 when applied strictly to dnn module another performance test is available here https www theimpossiblecode com blog faster opencv smiles tbb which also led to about 30 increase in speed how to use it install opencv library prerequisites on your raspberry pi sudo apt get update sudo apt get upgrade y sudo apt get install y libjpeg dev libpng dev libtiff dev libgtk 3 dev libavcodec extra libavformat dev libswscale dev libv4l dev libxvidcore dev libx264 dev libjasper1 libjasper dev libatlas base dev gfortran libeigen3 dev libtbb dev install numpy based on your target python version sudo apt get install y python3 dev python3 numpy or sudo apt get install y python dev python numpy how to install clone the repo into your raspberry pi and install all debs git clone https github com dlime faster opencv 4 raspberry pi git cd faster opencv 4 raspberry pi debs sudo dpkg i opencv deb sudo ldconfig how to test c test the installation by going to tests cpp test folder build it and launch the executable cd faster opencv 4 raspberry pi tests cpp opencv test mkdir build cd build cmake make j cat proc cpuinfo grep c processor cpp opencv test python test the installation by going to tests folder and launch the test py file cd faster opencv 4 raspberry pi tests python opencv test python test py you shouldn t see any error messages in console and an image with tetris blocks with contours drawed should appear how to uninstall run the following command in your raspberry pi terminal sudo apt purge opencv | raspberry-pi neon python2-python3 vfpv3 deep-learning opencv4 opencv3-python opencv-python debian-packages raspbian tbb computer-vision armhf arm7l | ai |
ComputerVision | computer vision the repository contains a list of projects and notebooks which i have worked on while reading computer vision deep learning and opencv from pyimagesearch https www pyimagesearch com notebooks 1 open cv https github com thinamxx computervision tree main 01 20opencv the opencv notebook contains the basics of opencv such as loading an image resizing images rotating image edge detection thresholding drawing and masking contour and shape detection the ocv project i notebook contains the implementation of rotating images correctly without cut off the ocv project ii notebook contains the implementation of color detection and histogram matching on images 2 convolutional neural networks https github com thinamxx computervision tree main 02 20convolutionalneuralnetworks the convolutions notebook contains all the dependencies required to understand the implementation of image convolutions and kernels the convolutional layers notebook contains all the dependencies required to understand keras conv2d class and convolutional layers 3 image classification https github com thinamxx computervision tree main 03 20image 20classification the simplepreprocessor notebook contains implementation of simple image preprocessor loading an image dataset into memory and k nearest neighbor classifier k nearest neighbor classifier doesn t actually learn anything but it directly relies on the distance between feature vectors the gradientdescent notebook contains implementation of gradient descent algorithms the stochasticgradientdescent notebook contains implementation of stochastic gradient descent image classification and regularization 4 neural networks https github com thinamxx computervision tree main 04 20neural 20networks the neural networks notebook contains the implementation of perceptron algorithm backpropagation algorithm and neural networks from scratch 5 lenet architecture https github com thinamxx computervision tree main 05 20lenet 20architecture the lenet architecture notebook contains the implementation of lenet architecture lenet is a seminal work in the deep learning literature which demonstrates how neural networks could be trained to recognize objects in images without feture extraction 6 vggnet architecture https github com thinamxx computervision tree main 06 20vggnet 20architecture the mini vggnet notebook contains the implementation of vggnet architecture it makes the use of only 3 x 3 filters regardless of network depth 7 pretrained cnn https github com thinamxx computervision tree main 07 20pretrained 20cnns the pretrained cnns notebook contains the reviews of convolutional neural networks and implementation of vgg16 and xception networks for classification of images 8 object detection https github com thinamxx computervision tree main 08 20object 20detection the object detection notebook contains implementation of object detection with pytorch and pretrained networks it also contains brief description about object detection and image preprocessing | computervision | ai |
CONNER | beyond factuality a comprehensive evaluation of large language models as knowledge generators this repository contains the code and resources for evaluating large language models llms in their capacity as knowledge generators our research introduces conner a comprehensive knowledge evaluation framework designed to systematically and automatically evaluate the generated information from six important perspectives factuality relevance coherence informativeness helpfulness and validity here you ll find the necessary code and resources to replicate our findings and further explore the potential of llms conner framework intrinsic evaluation factuality whether the information in the knowledge can be verified by external evidence relevance whether the knowledge is relevant to the user query coherence whether the knowledge is coherent at the sentence and paragraph levels informativeness whether the knowledge is new or unexpected against the model s existing knowledge extrinsic evaluation helpfulness whether the knowledge can improve the downstream tasks validity whether the results of downstream tasks using the knowledge are factually accurate how to use please stay tuned updates will be coming soon citation if you find our work helpful in your research please consider citing our paper misc chen2023factuality title beyond factuality a comprehensive evaluation of large language models as knowledge generators author liang chen and yang deng and yatao bian and zeyu qin and bingzhe wu and tat seng chua and kam fai wong year 2023 eprint 2310 07289 archiveprefix arxiv primaryclass cs cl | llm-evaluation hallucinations emnlp2023 large-language-models factuality nlg-evaluation chatgpt llama | ai |
thermalvis | thermalvis cross platform opencv based functionality for image processing and computer vision in thermal infrared if you are using a thermal infrared camera i can almost guarantee this software will be useful to you however there are plenty of features that could also be useful for those using conventional cameras or other related devices please explore the wiki https github com steevo87 thermalvis wiki for more information | ai |
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KBQARelationLearning | kbqarelationlearning code for our emnlp 2021 paper large scale relation learning for question answering over knowledge bases with pre trained language models https aclanthology org 2021 emnlp main 296 requirements install the requirements specified in requirements txt torch 1 4 0 transformers 4 3 3 tqdm 4 58 0 numpy 1 19 2 tensorboardx 2 1 matplotlib 3 3 4 data preprocessing preparing webqsp dataset we obtain and process webqsp dataset using the scripts released by graftnet https github com oceanskysun graftnet tree master preprocessing the scripts will firstly download the original webqsp dataset as well as the entity links from stagg https raw githubusercontent com scottyih stagg then they compute the relation and question embeddings using glove embeddings and run the edge weighted personal page rank ppr algorithm to retrieve a subgraph of freebase for each question the maximum number of retrieved entities is set to 500 our preprocessing scripts are in the graftnet preprocessing folder we modified the original scripts making them runnable in python3 environment preprocessing webqsp for bert based kbqa to solve kbqa task with bert we need to preprocessing the webqsp dataset making them a question context matching task the model is then trained to predict whether the given candidate entity is the answer of the question the preprocessing includes 1 to make use of the textual information of entities and relations in kb facts we need to find the entity name given the freebase mid e g fb m 03rk0 we firstly download the freebase data dumps https developers google com freebase to data freebase freebase rdf latest gz then use the script data freebase generate mid to name mapping py to obtain the mapping data freebase mid2name json 2 then we use the script data webqsp name expanded process py to process the webqsp dataset in the question context matching form preparing dataset for relation learning we firstly download the webred dataset https github com google research datasets webred to data webred webred folder and download the fewrel dataset http www zhuhao me fewrel to data webred fewrel matching fewrel folder for relation extraction re task we use the script data webred preprocess py and the processed datasets are in the data webred folder for relation matching rm task we use the script data webred matching preprocess py and the processed datasets are in the data webred matching folder to further make use of the fewrel dataset we use the script data webred fewrel matching fewrel process py to generate the rm dataset that uses both webred and fewrel as data sources in the data webred fewrel matching folder for relation reasoning rr task we modified the script from bertrl https github com zhw12 bertrl blob master load data py to data freebase pretraining bertrl load data webqsp py and the generated dataset will be in the data freebase pretraining bertrl webqsp folder training evaluating run the scripts in scripts folder note we create special tokens by modifying the vocab txt of the pre trained bert as following unused0 unknown entity unused1 path separator unused2 start of entity unused3 end of entity unused4 self unused5 start of head entity unused6 end of head entity unused7 start of tail entity unused8 end of tail entity to reproduce the experimental results you should as well modify the vocab txt of your pre downloaded bert e g in bert base uncased vocab txt citation inproceedings yan2021large title large scale relation learning for question answering over knowledge bases with pre trained language models author yan yuanmeng and li rumei and wang sirui and zhang hongzhi and daoguang zan and zhang fuzheng and wu wei and xu weiran booktitle proceedings of the 2021 conference on empirical methods in natural language processing pages 3653 3660 year 2021 | ai |
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nlp | h1 align center natural language processing h1 organizar c digo como https github com graykode nlp tutorial post with usful links transformers are gnns https graphdeeplearning github io post transformers are gnns index theory pipeline pipeline tokenization tokenization the input representation models recurrent convolutional recurrent convolutional models transformers transformers models transfer learning pipeline losses losses metrics metrics applications language model language model clasification clasification translation translation summarization summarization chatbot chatbot resources resources h3 align center theory h3 pipeline 1 preprocess tokenization split the text into sentences and the sentences into words lowercasing usually done in tokenization punctuation removal remove words like usually done in tokenization stopwords removal remove words like and the him done in the past lemmatization verbs to root form organizes will organize organizing organize this is better stemming nouns to root form democratic democratization democracy this is faster 2 extract features document features bag of words bow counts how many times a word appears in a text it can be normalize by text lenght tf idf measures relevance for each word in a document not frequency like bow n gram probability of n words together sentence and document vectors paper2014 https arxiv org abs 1405 4053 paper2017 https arxiv org abs 1705 02364 word features word vectors unique representation for every word independent of its context word2vec https arxiv org abs 1310 4546 by google in 2013 glove by standford fasttext by facebook contextualized word vectors good for polysemic words meaning depend of its context cove https arxiv org abs 1708 00107 in 2017 elmo https arxiv org abs 1802 05365 done with with bidirectional lstms by allen institute in 2018 transformer encoder done with with self attention 3 build model bag of embeddings linear algebra matrix decomposition latent semantic analysis lsa that uses singular value decomposition svd non negative matrix factorization nmf latent dirichlet allocation lda good for bow neural nets recurrent nns decoder lstm gru transformer decoder gpt bert hidden markov models others regular expressions regex find patterns parse trees syntax od a sentence tokenization the input representation character tokenization subword tokenization the best used in recent models word tokenization used in traditional nlp p align center img width 66 src img tokenization png p subword tokenization https medium com makcedward how subword helps on your nlp model 83dd1b836f46 byte pair encoding bpe https arxiv org abs 1508 07909 used in gpt 2 2015 wordpiece https arxiv org abs 1609 08144 used in bert 2016 unigram language model https arxiv org abs 1804 10959 2018 sentencepiece https arxiv org abs 1808 06226 2018 p align center img width 90 src img bpe tokenization png p p align center b bpe b tokenization of the word i subwords i p n gram probability of n words together read this https deepai org machine learning glossary and terms n gram p align center img width 66 src img ngrams png p example toy corpus start i like apples end start i like oranges end start i do not like broccoli end then p start i like p i start p like i 1 0 66 0 66 p start i like apples p i start p like i p apples like 1 0 66 0 5 0 33 recurrent convolutional models rnn recurrent nets no parallel tokens gru lstm awd lstm regular lstm with tuned dropout hyper parameters cnn convolutional nets parallel tokens lightweight dynamic convs https arxiv org abs 1901 10430 qrnn https arxiv org abs 1611 01576 quasi recurrent net used in multifit p align center img width 90 src img qrnn png p tricks teacher forcing feed to the decoder the correct previous word insted of the predicted previous word at the beggining of training attention learns weights to perform a weighted average of the words embeddings transformers models self attention br transformer encoder masked self attention br transformer decoder img width 200 src img encoder png img width 200 src img decoder png advantage context on both sides auto regression pretraining bidirectional lm better unidirectional lm examples bert gpt gpt 2 best one albert t5 meena applications clasification text generation notes auto regression is when the final output token becomes input original transformer combines both encoder and decoder is the only transformer doing this transformer xl is a recurrent transformer decoder xlnet has both context on both sides and auto regression huggingface transformers https huggingface co transformers pretrained models html is a package with pretrained transformers models pytorch tensorflow model creator date breif description 1st transfor https arxiv org abs 1706 03762 google jun 2017 transforer encoder decoder ulmfit https arxiv org abs 1801 06146 fast ai jan 2018 regular lstm elmo https arxiv org abs 1802 05365 allennlp feb 2018 bidirectional lstm gpt openai jun 2018 transformer decoder on lm bert https arxiv org abs 1810 04805 google oct 2018 transformer encoder on mlm nsp transformerxl https arxiv org abs 1901 02860 google jan 2019 recurrent transformer decoder xlm mbert https arxiv org abs 1901 07291 facebook jan 2019 multilingual lm transf elmo allennlp jan 2019 gpt 2 openai feb 2019 good text generation ernie https arxiv org abs 1904 09223 baidu apr 2019 ernie https arxiv org abs 1905 07129 tsinghua may 2019 transformer with knowledge graph xlnet https arxiv org abs 1906 08237 google jun 2019 bert transformer xl roberta https arxiv org abs 1907 11692 facebook jul 2019 bert without nsp distilbert hug face aug 2019 compressed bert minibert https arxiv org abs 1909 00100 google aug 2019 compressed bert multifit https arxiv org abs 1909 04761 fast ai sep 2019 multi lingual ulmfit qrnn post http nlp fast ai classification 2019 09 10 multifit html ctrl https arxiv org abs 1909 05858 salesforce sep 2019 controllable text generation megatronlm https arxiv org abs 1909 08053 nvidia sep 2019 big models with parallel training albert https arxiv org abs 1909 11942 google sep 2019 reduce bert params param sharing distilgpt 2 hug face oct 2019 compressed gpt 2 t5 https arxiv org abs 1910 10683 google oct 2019 text to text transfer transformer electra https openreview net pdf id r1xmh1btvb dec 2019 an efficient lm pretraining reformer https arxiv org abs 2001 04451 google jan 2020 the efficient transformer meena https arxiv org abs 2001 09977 google jan 2020 a human like open domain chatbot model 2l 3l 6l 12l 18l 24l 36l 48l 54l 72l 1st transformer yes ulmfit yes elmo yes gpt 110m bert 110m 340m transformer xl 257m xlm mbert yes yes transf elmo gpt 2 117m 345m 762m 1542m ernie yes xlnet 110m 340m roberta 125m 355m megatronlm 355m 2500m 8300m distilbert 66m minibert yes albert ctrl 1630m distilgpt 2 82m attention aug 2015 allows the network to refer back to the input sequence instead of forcing it to encode all information into ane fixed lenght vector paper effective approaches to attention based neural machine translation https arxiv org abs 1508 04025 blog https jalammar github io visualizing neural machine translation mechanics of seq2seq models with attention attention and memory http www wildml com 2016 01 attention and memory in deep learning and nlp 1st transformer google ai jun 2017 introduces the transformer architecture encoder with self attention and decoder with attention surpassed rnn s state of the art paper attention is all you need https arxiv org abs 1706 03762 blog https jalammar github io illustrated transformer ulmfit fast ai jan 2018 regular lstm encoder decoder architecture with no attention introduces the idea of transfer learning in nlp 1 take a trained tanguge model predict wich word comes next trained with wikipedia corpus for example wikitext 103 2 retrain it with your corpus data 3 train your task classification etc paper universal language model fine tuning for text classification https arxiv org abs 1801 06146 elmo allennlp feb 2018 context aware embedding better representation useful for synonyms made with bidirectional lstms trained on a language modeling lm objective parameters 94 millions paper deep contextualized word representations https arxiv org abs 1802 05365 site https allennlp org elmo gpt openai jun 2018 made with transformer trained on a language modeling lm objective same as transformer but with transfer learning for ther nlp tasks first train the decoder for language modelling with unsupervised text and then train other nlp task parameters 110 millions paper improving language understanding by generative pre training https s3 us west 2 amazonaws com openai assets research covers language unsupervised language understanding paper pdf site https blog openai com language unsupervised code https github com openai finetune transformer lm bert google ai oct 2018 bi directional training of transformer replaces language modeling objective with masked language modeling words in a sentence are randomly erased and replaced with a special token masked then a transformer is used to generate a prediction for the masked word based on the unmasked words surrounding it both to the left and right parameters bert base 110 millions bert large 340 millions paper bert pre training of deep bidirectional transformers for language understanding https arxiv org abs 1810 04805 official code https github com google research bert blog http jalammar github io illustrated bert fastai alumn blog https medium com huggingface multi label text classification using bert the mighty transformer 69714fa3fb3d blog3 http mlexplained com 2019 01 07 paper dissected bert pre training of deep bidirectional transformers for language understanding explained slides https nlp stanford edu seminar details jdevlin pdf transformer xl google cmu jan 2019 learning long term dependencies resolved transformer s context fragmentation outperforms bert in lm paper transformer xl attentive language models beyond a fixed length context https arxiv org abs 1901 02860 blog https medium com dair ai a light introduction to transformer xl be5737feb13 google blog https ai googleblog com 2019 01 transformer xl unleashing potential of html code https github com kimiyoung transformer xl xlm mbert facebook jan 2019 multilingual language model 100 languages sota on cross lingual classification and machine translation parameters 665 millions paper cross lingual language model pretraining https arxiv org abs 1901 07291 code https github com facebookresearch xlm blog https towardsdatascience com xlm enhancing bert for cross lingual language model 5aeed9e6f14b transformer elmo allennlp jan 2019 parameters 465 millions gpt 2 openai feb 2019 zero shot task learning coherent paragraphs of generated text parameters 1500 millions site https blog openai com better language models paper language models are unsupervised multitask learners https d4mucfpksywv cloudfront net better language models language models are unsupervised multitask learners pdf ernie baidu research apr 2019 world aware structure aware and semantic aware tasks continual pre training paper ernie enhanced representation through knowledge integration https arxiv org abs 1904 09223 xlnet google cmu jun 2019 auto regressive methods for lm best both bert transformer xl parameters 340 millions paper xlnet generalized autoregressive pretraining for language understanding https arxiv org abs 1906 08237 code https github com zihangdai xlnet roberta facebook jul 2019 facebook s improvement over bert optimized bert s training process and hyperparameters parameters roberta base 125 millions roberta large 355 millions trained on 160gb of text paper roberta a robustly optimized bert pretraining approach https arxiv org abs 1907 11692 reformer the efficient transformer https openreview net forum id rkgnkkhtvb aims to improve the complexity from o l 2 to o l useful when sequences tend to be quiet long urgent facebook making transformer networks simpler and more efficient https ai facebook com blog making transformer networks simpler and more efficient adaptive attention span https arxiv org abs 1905 07799 make transformers more efficient for longer sentences over 8000 tokens only attention layer https arxiv org abs 1907 01470 simpler more efficient architecture sharing attention weights for fast transformer https arxiv org abs 1906 11024 transformer architecture transformer input 1 tokenizer create subword tokens methods bpe 2 embedding create vectors for each token sum of token embedding positional encoding information about tokens order e g sinusoidal function 3 dropout transformer blocks 6 12 24 1 normalization 2 multi head attention layer with a left to right attention mask each attention head uses self attention to process each token input conditioned on the other input tokens left to right attention mask ensures that only attends to the positions that precede it to the left 3 normalization 4 feed forward layers 1 linear h 4h 2 gelu activation func 3 linear 4h h transformer output 1 normalization 2 output embedding 3 softmax 4 label smothing ground truth 90 the correct word and the rest 10 divided on the other words lowest layers morphology middle layers syntax highest layers task specific semantics transfer learning step task data who do this 1 language model pretraining lot of text corpus eg wikipedia google or facebook 2 language model finetunning only you domain text corpus you 3 your supervised task you labeled domain text you p align center img width 66 src img transfer png p losses language modeling we project the hidden state on the word embedding matrix to get logits and apply a cross entropy loss on the portion of the target corresponding to the gold reply next sentence prediction we pass the hidden state of the last token the end of sequence token through a linear layer to get a score and apply a cross entropy loss to classify correctly a gold answer among distractors metrics score description interpretation perplexity lm the lower the better glue an avergae of different scores for nlu bleu for translation compare generated with reference sentences n gram the higher the better race http www qizhexie com data race leaderboard html r e a ding c omprehension dataset collected from english e xaminations the higher the better squad https rajpurkar github io squad explorer s tanford qu estion a nswering d ataset the higher the better bleu limitation he ate the apple he ate the potato has the same bleu score bleu at your own risk https towardsdatascience com evaluating text output in nlp bleu at your own risk e8609665a213 h3 align center applications h3 application description type part of speech tagging pos identify nouns verbs adjectives etc aka parsing named entity recognition ner identify names organizations locations medical codes etc coreference resolution identify several ocuurences on the same person objet like he she text categorization identify topics present in a text sports politics etc question answering answer questions of a given text squad drop dataset sentiment analysis possitive or negative comment review classification language modeling lm predict the next word unupervised masked language modeling mlm predict the omitted words unupervised summarization crate a short version of a text translation translate into a different language chatbot interact in a conversation speech recognition speech to text see audio audio md cheatsheet speech generation text to speech see audio audio md cheatsheet natural language processing nlp natural language understanding nlu speech and sound speak and listen translation p align center img width 80 src img translation png p summarization read this paper https arxiv org abs 1909 03186 p align center img width 80 src img summarization png p chatbot huggingface sota chatbot https medium com huggingface how to build a state of the art conversational ai with transfer learning 2d818ac26313 model backbone transformer decoder like gpt or gpt2 pretrained for lm input data 1 persona one or several personality sentences blue 2 history the history of the dialog pink 3 reply the tokens of the current answer green img chatbot1 png embeddings word embedding information about word semantics position embedding information about word order segment embedding nformation about type personality history or reply img chatbot2 png double heads model for multi task loss one head for language modeling loss other head for next sentence classification loss img chatbot3 png references to do read nlp year in review 2019 https medium com dair ai nlp year in review 2019 fb8d523bcb19 jan 2020 read nlp recent trends https medium com dair ai deep learning for nlp an overview of recent trends d0d8f40a776d august 2019 read mass https www microsoft com en us research blog introducing mass a pre training method that outperforms bert and gpt in sequence to sequence language generation tasks transfer learning in translation for transformers read cnns better than attention https arxiv org abs 1901 10430 modern nlp nlp progress https nlpprogress com nlp overview https nlpoverview com nlp infographic https www analyticsvidhya com blog 2019 08 complete list important frameworks nlp modern nlp into practice https t co siazryio6o amp 1 twit thread https twitter com joelgrus status 1171783769495179264 courses fast ai nlp course playlist https www youtube com playlist list pltmwhnx gukkocxqokqjuvxglsdywssh9 spacy course https course spacy io 5 videos https www youtube com playlist list plig2x2rj 4ltf iiu7 j3y yg8lre1wzq transformers the illustrated transformer https jalammar github io illustrated transformer june 2018 the illustrated bert elmo https jalammar github io illustrated bert december 2018 the illustrated gpt 2 https jalammar github io illustrated gpt2 august 2019 best transformers explanation http www peterbloem nl blog transformers august 2019 all transformers with chart https github com thunlp plmpapers bert huggingface https medium com huggingface multi label text classification using bert the mighty transformer 69714fa3fb3d mlexplained http mlexplained com 2019 01 07 paper dissected bert pre training of deep bidirectional transformers for language understanding explained slides https nlp stanford edu seminar details jdevlin pdf bert summary https www lyrn ai 2018 11 07 explained bert state of the art language model for nlp bert roberta distilbert xlnet which one to use https www kdnuggets com 2019 09 bert roberta distilbert xlnet one use html beto spanish bert https medium com dair ai beto spanish bert 420e4860d2c6 from attention in transformers to dynamic routing in capsule nets https staff fnwi uva nl s abnar p 108 distilbert model by huggingface https medium com huggingface distilbert 8cf3380435b5 transfer learning in nlp by sebastian ruder blog http ruder io state of transfer learning in nlp slides http tiny cc naacltransfer notebook from scratch pytorch http tiny cc naacltransfercolab notebook2 pytorch transformers fast ai https github com davidykzhao pytorch transformers fastai blob master pytorch transformers fastai ipynb code http tiny cc naacltransfercode github video https www youtube com watch v hnpwrpg9brq t 1486s nlp transfer learning libraries https twitter com seb ruder status 1172607702884933633 hardvard nlp papers http nlp seas harvard edu papers sebastian ruder webpage http ruder io 7 nlp libraries https medium com microsoftazure 7 amazing open source nlp tools to try with notebooks in 2019 c9eec058d9f1 spacy blog https explosion ai blog attention and memory http www wildml com 2016 01 attention and memory in deep learning and nlp fast ai nlp videos 1 what is nlp https youtu be cce8ntxp xi 2 topic modeling with svd nmf https youtu be tg3puwmgjsc 3 topic modeling svd revisited https youtu be lrz4amaxpbi 4 sentiment classification with naive bayes https youtu be hp2ipc5pw4i 5 sentiment classification with naive bayes logistic regression contd https youtu be dt7sarnlo1g 6 derivation of naive bayes numerical stability https youtu be z8 tbrg1 re 7 revisiting naive bayes and regex https youtu be q1zlqfnexdw 8 intro to language modeling https youtu be pnnhaquqqw8 9 transfer learning https youtu be 5gcqvuznkn0 10 ulmfit for non english languages https youtu be mdx x6rkxas 11 understanding rnns https youtu be l1rlfh0pmzw 12 seq2seq translation https youtu be ifsjmg4flwq 13 word embeddings quantify 100 years of gender ethnic stereotypes https youtu be boxv8od4jqq 14 text generation algorithms https youtu be 3oeb ffmpny 15 implementing a gru https youtu be bl6wvj6wqae 16 algorithmic bias https youtu be pthqge9qdn8 17 introduction to the transformer https youtu be afkgpmu16qa 18 the transformer for language translation https youtu be kzfyftih7r8 19 what you need to know about disinformation https youtu be vbva2rn rbq | ai |
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Jupyter | jupyter https img shields io github forks thealgorithms jupyter style social https img shields io github stars thealgorithms jupyter style social https img shields io github watchers thealgorithms jupyter style social br https img shields io github repo size thealgorithms jupyter https img shields io github downloads thealgorithms jupyter total gitpod ready to code https img shields io badge gitpod ready to code blue logo gitpod https gitpod io https github com thealgorithms jupyter br https img shields io github issues thealgorithms jupyter color green https img shields io github issues pr thealgorithms jupyter color green https img shields io github last commit thealgorithms jupyter https img shields io github contributors thealgorithms jupyter binder https mybinder org badge logo svg https mybinder org v2 gh thealgorithms jupyter master clone git repository sh git clone https github com thealgorithms jupyter git you can run and edit the algorithms or contribute to them using gitpod io https www gitpod io a free online development environment with a single click open in gitpod https gitpod io button open in gitpod svg http gitpod io https github com thealgorithms jupyter contributing new algorithms make your pull requests to be specific and focused instead of contributing several algorithms all at once contribute them all one by one separately i e one pull request for logistic regression another one for k means and so on every new algorithm must have source code with comments and readable namings math being explained in readme md along with the code jupyter demo notebook with example of how this new algorithm may be applied if you re adding new datasets they need to be saved in the data folder csv files are preferable the size of the file should not be greater than 30mb contributing before removing any bug or adding new algorithm please do the following check contribution guidelines before contribution contributing md and also please read code of conduct code of conduct md license licensed under the mit license license md | hacktoberfest algorithms data-science data-structures machine-learning deep-learning neural-network | ai |
WymaTimesheetWebApp | wymatimesheetwebapp wyma an engineering company in hornby has contacted our class in respect to a project ripe for a scholarship program it will be an app for android and potentially ios that will help the people in their factory sign into the premises without having to manually input their data onto one singular computer at the start and the end of the day they previously have had issues with using manual timesheets in a job costing software and have recently implemented a system that works on mostly for design engineers for the use on local pcs but the biggest issue is for the people on the main factory floor who don t have access to a pc and are still stuck manually signing in at a single sign in point this is where we can come in i believe we can create an android app that will allow the people working on the factory floor to sign in from anywhere on the premises ultimately removing the bottleneck of signing from one point improving work effectiveness and making sure everyone can get to work as soon as they arrive on the premises | os |
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IIT-NSTU-app | iit nstu app it is an android application with the basic information of iit all the teachers student s information and courses with their syllabus and all other necessary information s of institute of information technology nstu can be easily accessed by this application ezgif com gif maker https user images githubusercontent com 58462502 135713542 35bb8461 43b8 4b5b a38d 486e3b5fb09d gif features introduction of iit director s profile teachers information students information academic officials course coordinator s information academic syllabus all course details class schedule and routines departmental activity screenshot 401 https user images githubusercontent com 61958899 135704975 3f37617b dc52 4fc0 89ed c5d3b8a47883 png students information screenshot 400 https user images githubusercontent com 61958899 135704967 12df8666 7a09 494c 83e6 e7af653fc237 png academic syllabus screenshot 402 https user images githubusercontent com 61958899 135704976 f0d01535 d32a 4507 937d 00dc2d706425 png contributors arnab dey contact no 0172501782 gmail at arnabdey665 gmail com github at a href https github com arnabdey24 target blank arnab dey github a naimur rahman contact no 01724595168 gmail at nirzon naim gmail com github at a href https github com naimurrahman 11 target blank naimur rahman github a sunaan sultan contact no 01766554344 gmail at sunaansultaniit gmail com github at a href https github com sunaansultaniit target blank sunaan sultan github a | server |
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StateOS | stateos https img shields io github license stateos stateos svg style flat square logo https opensource org licenses mit https img shields io github release stateos stateos svg style flat square logo https github com stateos stateos releases https github com stateos stateos actions workflows test yml badge svg https github com stateos stateos actions workflows test yml stateos is free extremely simple and very fast real time operating system rtos designed for deeply embedded applications targets arm cortex m stm8 inspiration stateos was inspired by the concept of a state machine procedure executed by the task task state doesn t have to be noreturn type it will be executed into an infinite loop there s a dedicated function for immediate change the task state task function documentation all documentation is contained within source files in particular header files gettin started building an application for a specific compiler is realised using the appropriate makefile script features kernel can operate in preemptive or cooperative mode kernel can operate with 16 32 or 64 bit timer counter kernel can operate in tick less mode implemented basic protection using mpu use nullptr stack overflow implemented functions for asynchronous communication with unmasked interrupt handlers spin locks once flags events signals with protection mask flags any all protect ignore barriers semaphores binary limited counting mutexes with configurable type protocol and robustness fast mutexes error checking condition variables read write locks memory pools raw buffers message queues mailbox queues event queues job queues timers one shot periodic hierarchical state machine cmsis rtos api cmsis rtos2 api nasa osal support c wrapper support for std thread all documentation is contained within source files in particular header files examples and templates are in separate repositories on github https github com stateos archival releases on sourceforge https sourceforge net projects stateos supported stdc features std thread std jthread std stop callback std this thread std mutex std shared mutex std condition variable std binary semaphore std counting semaphore std lock guard std unique lock std shared lock std future std promise std assync std barrier std latch std chrono thread local storage thread local and more license this project is licensed under the terms of the mit license mit https opensource org licenses mit | operating-system rtos kernel real-time preemptive cooperative embedded cortex-m cmsis-os cmsis-os2 cmsis-rtos nasa-api iot stm8 microcontroller arm stateos thread-local-storage std-thread stdc | os |
cloud_vault | cloud vault a collection of resources for cloud engineering for example sample scripts on terraform ansible and other iac infrastructure as code tools | cloud |
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Super-Simple-Tasker | brought to you by quantum leaps https www state machine com attachments logo ql 400 png https www state machine com hr github release latest by date https img shields io github v release quantumleaps super simple tasker https github com quantumleaps super simple tasker releases latest github https img shields io github license quantumleaps super simple tasker https github com quantumleaps super simple tasker blob master license super simple tasker sst super simple tasker sst is an event driven preemptive priority based real time operating system rtos kernel that is fully compatible with the requirements of rate monotonic analysis scheduling rma rms https youtu be klxxxncry60 p align center a href https youtu be ptcauyl994a target blank title sst video img src img logo sst c cpp png p a p the tasks in sst are non blocking and run to completion which are also known as basic tasks in the osek vdx operating system specification https www irisa fr alf downloads puaut tpnxt images os223 pdf sst corresponds to the bcc2 conformance class in osek vdx sst provides the following features basic tasks non blocking run to completion preemptive priority based scheduling multiple tasks per prioriy level multiple activations per task event queues selective scheduler locking according to stack resource policy srp br a non blocking mutual exclusion mechansim for protecting shared resources note br the execution profile of sst tasks perfectly matches the non blocking and run to completion semantics of event driven state machines a k a active objects or actors https www state machine com active object this repository contains the sst following implementations preemptvie sst in c sst c preemptvie sst in c sst cpp additionally this repository contains the even simpler non preemptive implementation of basic tasks called sst0 non preemptive sst0 non preemptvie sst0 in c sst0 c non preemptvie sst0 in c sst0 cpp note br the preemptive sst and non preemptive sst0 implement actually the same sst api https github com quantumleaps super simple tasker tree main include either in c or c related approaches the sst rtos kernel is related to although not based on the following approaches operating system osek vdx https www osek vdx org mirror os21r1 pdf a stack based resource allocation policy for realtime processes https ieeexplore ieee org document 128747 real time for the masses https www diva portal org smash get diva2 1005680 fulltext01 pdf crect a c compile time reactive rtos https github com korken89 crect rust s real time for the masses rtfm https lonesometraveler github io 2020 05 22 rtfm html real time interrupt driven concurrency rtic https rtic rs 1 book en hardware rtos for arm cortex m sst for arm cortex m sst c ports arm cm provides a unique hardware implementation of the sst api for arm cortex m m0 m0 m3 m4 m7 m23 m33 the sst hardware rtos for arm cortex m is fully compatible with the requirements of rate monotonic analysis scheduling rma rms https youtu be klxxxncry60 p align center img src img logo sst arm cm png p note br the sst hardware implementation is likely the most performant and efficient hard real time rtos kernel for arm cortex m hardware rtos for microchip dspic the contributed sst port for dspic sst c ports dspic provides a unique hardware implementation of the sst api for microchip dspic https www microchip com en us products microcontrollers and microprocessors dspic dscs the sst hardware rtos for dspic is fully compatible with the requirements of rate monotonic analysis scheduling rma rms https youtu be klxxxncry60 p align center img src img dspic png p sst videos sst has been presented at the embedded online conference 2023 and the videos are available on youtube p align center a href https youtu be ptcauyl994a target blank title sst video img src img sst video jpg a p sst history sst has been originally published as a cover story article build a super simple tasker legacy super simple tasker pdf in the embedded systems design magazine in july 2006 https www embedded com embedded systems design july 2006 that original version of sst now called legacy sst is still available legacy and is provided for historical reference over the years more complete sst like kernels have been developed for a number of embedded processors such as arm7tdmi arm cortex m m0 m7 arm cortex r msp430 pic24 dspic pic32 etc all these kernels are now included in the qp c and qp c real time embedded frameworks https www state machine com products qp qk preemptive priority based non blocking kernel https www state machine com qpc srs qk html works like sst and is available as one of the built in kernels in the qp real time embedded frameworks rtefs https www state machine com products qp qxk preemptive dual mode kernel https www state machine com qpc srs qxk html combines the basic tasks of sst with traditional blocking tasks a k a extended tasks in osek vdx and is available as one of the built in kernels in the qp real time embedded frameworks rtefs https www state machine com products qp qv priority based cooperative kernel https www state machine com qpc srs qv html works like sst0 non preemptive sst0 and is available as one of the built in kernels in the qp real time embedded frameworks rtefs https www state machine com products non preemptive sst0 this repository contains also the non preemptive implementation of the sst api called sst0 sst0 is also a priority based rtos kernel but the scheduling is non preemptive sst0 scheduler always executes the higest priority basic task ready to run but the scheduling is performed only after voluntary completion of each task run to completion execution p align center img src img logo sst0 chip png p sst0 provides the following features basic tasks non blocking run to completion priority based non preemptive cooperative scheduling only one task per prioriy level multiple activations per task event queues getting started examples the best way to get started with sst is to build and run the provided examples this repository contains several versions of the blinky button example which contains several sst tasks running concurrently and communicating with each other the blinky button example demonstrates real time capabilities of sst and uses a logic analyzer remark logic analyzer is not necessary to build and run the examples p align center img src img blinky button png p the blinky button example is provided for c super simple tasker sst c preemptive sst c examples examples for sst c blinky button blinky button example armclang project for arm keil gnu makefile for gnu arm iar project for iar ewarm sst cpp preemptive sst c examples examples for sst c blinky button blinky button example armclang project for arm keil gnu makefile for gnu arm iar project for iar ewarm sst0 c non preemptive sst0 c examples examples for sst0 c blinky button blinky button example armclang project for arm keil gnu makefile for gnu arm iar project for iar ewarm sst0 cpp non preemptive sst0 c examples examples for sst0 c blinky button blinky button example armclang project for arm keil gnu makefile for gnu arm iar project for iar ewarm for every of these cases the projects to build the examples are provided for the following embedded boards p align center img src img bd nucleos png img src img bd ek tm4c123gxl jpg p stm32 nucleo c031c6 arm cortex m0 stm32 nucleo l053r8 arm cortex m0 stm32 nucleo h743zi arm cortex m7 with double precision fpu tivac launchpad ek tm4c123gxl arm cortex m4 with single precision fpu licensing the sst source code and examples are released under the terms of the permissive mit open source license license please note that the attribution clause in the mit license requires you to preserve the original copyright notice in all changes and derivate works invitation to collaborate this project welcomes collaboration please help to improve sst port it to other processors integrate it with other embedded software add interesting examples etc to avoid fragmentation this repository is intended to remain the home of sst to contribute please clone fork and submit pull requests to incorporate your changes how to help this project if you like this project please spread the word about sst on various forums social media and other venues frequented by embedded folks p align center img src img spread the word jpg p also please give this repository https github com quantumleaps super simple tasker a star in the upper right corner of your browser window p align center img src img github star jpg p | arm-cortex-m0 arm-cortex-m3 arm-cortex-m4 arm-cortex-m7 embedded embedded-c embedded-cpp embedded-systems event-driven hard-real-time real-time rtos | os |
ansible-practice | this is the repo i used to pratice the ansible course under udacity s cloud devops engineering nanodegree | ansible cloud devops | cloud |
cvcoursemm2019autumn | en version en news info ww feedback marks program lit links a name news 2019 12 25 lec lecture10 advattack pdf 2019 12 17 2019 12 12 lec lecture09 compression pdf seminars 03 transfer learning ipynb 2019 12 10 https www kaggle com t adbfbe740dc14c5384670ae92cae575c 2019 12 05 lec lecture08 enhancement pdf 2019 11 27 lec lecture07 pdf 2019 11 27 image generation by gan lec image generation by gan pdf image generation by gan 246 mb https drive google com open id 1gjizfyvxurgt21cvd10e6rfrzqjf1bqv image translation 76 mb https drive google com open id 1ldrltkkqaikw5gcverbxvr5rlhi5pioz 2019 11 13 lec lecture06 pdf keras seminars 02 mnist keras tutorial generators and callbacks ipynb 2019 11 07 image translation lec image translation pdf 2019 11 06 assignments theory02 pdf 26 11 2019 2019 11 06 lec lecture05 pdf 2019 10 22 assignments programming01 ipynb 12 11 2019 2019 10 22 lec lecture04 pdf 2019 10 16 lec lecture03 pdf keras seminars 01 keras intro ipynb 2019 10 09 assignments theory01 pdf 29 10 2019 2019 10 09 lec lecture02 pdf 2019 10 02 lec lecture01 pdf 2019 09 23 1 18 30 1205 a name info 2019 http intsys msu ru http intsys msu ru staff babin a name ww 18 30 1205 a name feedback telegram https t me joinchat aaaaaeumx5cjlodlxsot8g mlcoursemm gmail com issues https github com mlcoursemm cvcoursemm2019autumn issues a name marks 52 39 26 https docs google com spreadsheets d 1i3ahmbsfcheeevsg7pffahqrqtrxurf2bf4hw4xnydm edit gid 0 a name program 1 2 3 4 5 keras 6 7 8 9 10 11 12 13 14 15 16 a name lit 1 ian goodfellow and yoshua bengio and aaron courville deep learning https www deeplearningbook org 1st edition mit press 2016 2 http neuralnetworksanddeeplearning com 3 francois chollet deep learning with python http faculty neu edu cn yury aai textbook deep 20learning 20with 20python pdf 1st edition manning 2017 a name links https github com mlcoursemm mlcoursemm2019spring python python https github com mlcoursemm mlcoursemm2019spring blob master prac python intro ipynb a name en en version introduction to computer intelligence modern computer vision content news news1 short info info1 time and place ww1 communication with teachers feedback1 task results marks1 course program program1 bibliography lit1 useful links links1 a name news1 news 2019 12 25 uploaded 10th lecture lec lecture10 advattack pdf 2019 12 17 added criteria for getting grades for the course 2019 12 12 uploaded 9th lecture lec lecture09 compression pdf and seminar seminars 03 transfer learning ipynb 2019 12 10 started computer vision competition https www kaggle com t adbfbe740dc14c5384670ae92cae575c 2019 12 05 uploaded 8th lecture lec lecture08 enhancement pdf 2019 11 27 uploaded 7th lecture lec lecture07 pdf 2019 11 27 uploaded short version of the lecture on image generation by gan lec image generation by gan pdf from a ivanyuta as well as full versions of lectures on image generation by gan 246 mb https drive google com open id 1gjizfyvxurgt21cvd10e6rfrzqjf1bqv and image translation 76 mb https drive google com open id 1ldrltkkqaikw5gcverbxvr5rlhi5pioz 2019 11 13 uploaded 6th lecture lec lecture06 pdf and the code from second keras introduction seminar seminars 02 mnist keras tutorial generators and callbacks ipynb 2019 11 07 added short version of the lecture on image translation lec image translation pdf from d mikhailov 2019 11 06 uploaded second theoretical task assignments theory02 pdf deadline for sending decisions 11 26 2019 inclusive 2019 11 06 uploaded 5th lecture lec lecture05 pdf 2019 10 22 uploaded first practical task assignments programming01 ipynb deadline for sending decisions 11 12 2019 inclusive 2019 10 22 uploaded 4th lecture lec lecture04 pdf 2019 10 16 uploaded 3rd lecture lec lecture03 pdf and the code from the keras introduction seminar seminars 01 keras intro ipynb 2019 10 09 uploaded first theoretical task assignments theory01 pdf deadline for sending decisions 29 10 2019 inclusive 2019 10 09 uploaded 2nd lecture lec lecture02 pdf 2019 10 02 uploaded 1st lecture lec lecture01 pdf 2019 09 23 the first lecture will take place on tuesday october 1 at 18 30 in room 1205 main bilding msu a name info1 short info in the fall semester of 2019 at the faculty of mechanics and mathematics of lomonosov moscow state university begins reading a new special course of the student s choice dedicated to neural network algorithms for computer vision the course will be taught on the basis of the department of mathematical theory of intelligent systems http intsys msu ru under the guidance of doctor of physical and mathematical sciences professor babin d n http intsys msu ru staff babin the course will be delivered by ph d petiushko a a https petiushko info and ph d ivanov i e a name ww1 time and place the lessons are to be taught on tuesdays 18 30 main bilding msu room 1205 a name feedback1 communication with teachers telegram channel https t me joinchat aaaaaeumx5cjlodlxsot8g where all important news will appear feedback by email mlcoursemm gmail com well you can always write in issues https github com mlcoursemm cvcoursemm2019autumn issues a name marks1 evaluation criteria excellent 52 points and above good 39 points and above satisfactory pass 26 points and above summary table with results https docs google com spreadsheets d 1i3ahmbsfcheeevsg7pffahqrqtrxurf2bf4hw4xnydm edit gid 0 a name program1 course program topic 1 a brief history of the neural network approach in computer vision topic 2 statement of the main tasks of computer vision topic 3 convolutional neural networks types of layers topic 4 stochastic gradient descent modern optimization algorithms topic 5 an introduction to neural network frameworks using keras as an example topic 6 backpropagation method topic 7 methods for initializing neural network weights topic 8 modern approaches to the problem of image classification topic 9 modern approaches to the problem of detecting objects in images topic 10 modern approaches to the problem of image segmentation topic 11 modern approaches to the problem of face recognition topic 12 modern approaches to the problem of improving the quality of images super resolution blur and noise removal topic 13 variational autoencoder topic 14 generative adversarial models topic 15 adversarial attacks on neural networks topic 16 modern approaches to compression and acceleration of neural networks a name lit1 bibliography 1 ian goodfellow and yoshua bengio and aaron courville deep learning https www deeplearningbook org 1st edition mit press 2016 2 http neuralnetworksanddeeplearning com 3 francois chollet deep learning with python http faculty neu edu cn yury aai textbook deep 20learning 20with 20python pdf 1st edition manning 2017 a name links1 useful links machine learning it is desirable to have a background in the form of a machine learning course done for example here https github com mlcoursemm mlcoursemm2019spring can be found the appropriate course python a brief introduction to the programming language python here https github com mlcoursemm mlcoursemm2019spring blob master prac python intro ipynb | ai |
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rex_multiupload | redaxo multiple upload utility rex multiupload redaxo quot multiupload quot addon how to install download the package rename the folder to rex multiupload without quotes and upload it into your redaxo addon dir if you leave the folder name unchanged the addon will alter you an notification and will try to fix the problem automagically new in this version realtime edit with ajax after the upload has finished you re able to modify any metainfo change title description or any other kind of metainfo you can also delete a file immediately notes multiupload will now remember last synced cat behaves like you already know from redaxo improved layout and css mediapool page is now update stable uses ep to inject the page no patching file required anymore and a lot more have fun | front_end |
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kujang | kujang framework pure css framework ready to war for mobile first responsive development modern web development img src kujang jpg p image credit for http vargvilmer deviantart com art kujang 392405340 p 1 color 2 hover 3 container 4 border 5 panel 6 button 7 card 8 heading 9 font 10 align 11 padding 12 margin 13 display 14 position 15 quotes 16 alerts 17 tables 18 list 19 icons 20 responsive 21 image 22 image effect 23 form input 24 badges 25 tags 26 animation 27 effects 28 dropdown 29 accordion 30 navigation 31 sidenav features gradient colors shake effects to do add parallax add slideshow add tab add modals add tooltips add progressbar add pagination add material design mit license copyright c 2018 gun gun febrianza 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 | css css3 css-framework | front_end |
Seeed_Arduino_MG126 | seeed arduino mg126 build status https travis ci com seeed studio seeed arduino as5600 svg branch master https travis ci com seeed studio seeed arduino as5600 this software is written by baozhu zuo from seeed technology inc http www seeed cc and is licensed under the mit license http opensource org licenses mit license php check license txt license for the details of mit license br contributing to this software is warmly welcomed you can do this basically by br forking https help github com articles fork a repo committing modifications and then pulling requests https help github com articles using pull requests follow the links above br for operating guide adding change log and your contact into file header is encouraged br thanks for your contribution seeed is a hardware innovation platform for makers to grow inspirations into differentiating products by working closely with technology providers of all scale seeed provides accessible technologies with quality speed and supply chain knowledge when prototypes are ready to iterate seeed helps productize 1 to 1 000 pcs using in house engineering supply chain management and agile manufacture forces seeed also team up with incubators chinese tech ecosystem investors and distribution channels to portal maker startups beyond | arduino samd21 arduinolibrary mg126 | front_end |
FashionSearch | fashionsearch angular front end with python amp airflow data pipeline | front_end |
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libuzmtp | libuzmtp simple zmtp protocol implementation based of zeromq reference desgin https github com zeromq libzmtp git this implementation was needed to abstract away system calls inorder to port onto a microcontroller enviornment | zmq zmtp iot embedded | os |
DataScienceCourse | datasciencecourse the objective of this course is to teach students how to do an end 2 end data science project from problem definition data sourcing wrangling and modelling to analyzing visualizing and deploying maintaining the models it will cover the main principles tools that are required for data science this course is for anyone interested in learning data science analyst programmer non technical professional student etc the end 2 end data science course will be divided into 4 parts part 1 is a beginner s course that covers basic machine learning and data analytics part 2 will cover intermediate and advanced machine learning techniques deep learning and nlp natural language processing part 3 will cover advanced machine learning techniques reinforcement learning and computer vision part 4 will cover data engineering databases and big data tools hadoop spark parts 2 3 4 of the course will be published later throughout the course detailed lectures covering the maths logic of the algorithms python code examples and online resources are provided to support the learning process more details are available on our website https datawisdomx com course material including python code and data is available at https github com datawisdomx datasciencecourse it has python code examples for each algorithm and a full model building and deployment example repository for the data science course each part of the course will have a separate folder with sub folders containing lectures code and other files | data-science machine-learning data-analytics python pandas scikit-learn aws-elasticbeanstalk flask-application statistics probability roboadvisor deep-learning reinforcement-learning computer-vision natural-language-processing data-engineering sql mysql calculus algebra | server |
nlp-graph-examples | nlp graph examples examples for graphorum 2019 presentation graph techniques for natural language processing presentation slides for graphorum 2019 https www slideshare net sujitpal graph techniques for natural language processing 183117140 abstract natural language embodies the human ability to make infinite use of finite means humboldt 1836 chomsky 1965 a relatively small number of words can be combined using a grammar in myriad different ways to convey all kinds of information languages model inter relationships between their words just like graphs model inter relationships between their vertices it is not surprising then that graphs are a natural tool to study natural language and glean useful information from it automatically and at scale this presentation will focus on nlp techniques to convert raw text to graphs and present graph theory based solutions to some common nlp problems solutions presented will use apache spark or neo4j depending on problem size and scale examples of graph theory solutions presented include pagerank for document summarization link prediction from raw text for knowledge graph enhancement label propagation for entity classification and random walk techniques to find similar documents environment neo4j the graph database used for this project was neo4j community version 3 5 5 the apoc and graph algorithm libraries had to be separately installed in the neo4j home plugin directory following jar files were used they need to be compatible with the server version apoc 3 5 0 4 all jar neo4j graph algorithms 3 5 8 1 standalone jar apoc and graph algorithms need to be enabled in neo4j home conf neo4j conf dbms security procedures unrestricted apoc algo dbms security procedures whitelist apoc algo python 3 7 from anaconda spacy 2 0 11 for nlp support py2neo 4 1 3 for connecting to neo4j via bolt and execute cypher commands pip install py2neo spark graphframes note this ended up not really getting used all case study examples are based on neo4j this is because neo4j algorithms are usually more feature rich than spark graphframes counterparts and neo4j scales well to large graphs largest used here is one with 500k nodes and 1 3m edges databricks runtime version 5 5 lts apache spark 2 4 3 scala 2 11 graphframes 0 7 0 apache spark 2 4 scala 2 11 | ai |
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FreeRTOS_POSIX_STM32 | freertos posix freertos posix posix freertos posix https www freertos org freertos plus freertos plus posix index html supported features stm32 keil 5 33 armcc v5 06 stm32cubemx 6 11 hal v1 83 stm32f103zet6 rc 64m 115200 pe5 pb5 freertos posix simulation freertos vs vs freertos posix document picture posix jpg freertos posix stm32 stm32 freertos posix stm32cubemx vs keil | os |
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skeleton-framework | skeleton framework a simple responsive framework for mobile friendly development skeleton framework takes its roots from dhg skeleton https github com dhg skeleton and builds upon the same basic principles note we re doing some work and shuffling things around if you d like easy access to the individual component parts of skeleton take a look at the combo bones branch otherwise you can still grab the whole build from master in the dist dir usage install the sources into your node app via npm install save dev skeleton framework install the compiled version into your frontend via bower install skeleton framework install the latest development version via git clone https github com skeleton framework skeleton framework git contributions are welcome develoment docs can be found on the wiki https github com skeleton framework skeleton framework wiki skeleton framework development if you experience bugs or errors please report it on the issue tracker https github com skeleton framework skeleton framework issues see the demo page dist index html for a visual representation of skeleton classes | front_end |
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rfid_system | rfid access control and security system an embedded system designed using the arduino programming environment the software is written in c c this project was part of the embedded computer systems course at seeu project https raw githubusercontent com dardanbekteshi rfid system master rfid system jpg | os |
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llm-book | large language models at work repository for my in development book large language models at work enhancing software systems with language models online at https vladris com llm book code contains all the code samples site contains the assets for the website text contains the manuscript tools contains tooling for building the website using baku https github com vladris baku | book llms | ai |
machinelearning-samples | note we d love to hear your thoughts about mlops let us know in this survey https www research net r mlops samples ml net samples ml net https www microsoft com net learn apps machine learning and ai ml dotnet is a cross platform open source machine learning framework that makes machine learning accessible to net developers in this github repo we provide samples which will help you get started with ml net and how to infuse ml into existing and new net apps note please open issues related to ml net https www microsoft com net learn apps machine learning and ai ml dotnet framework in the machine learning repository https github com dotnet machinelearning issues please create the issue in this repo only if you face issues with the samples in this repository there are two types of samples apps in the repo getting started images app type getting started term cursor png ml net code focused samples for each ml task or area usually implemented as simple console apps end end apps images app type e2e black png end user sample web and desktop apps infused with machine learning models based on ml net the official ml net samples are divided in multiple categories depending on the scenario and machine learning problem task accessible through the following tables table align middle width 100 tr td align middle colspan 3 binary classification td tr tr td align middle img src images sentiment analysis png alt binary classification chart br img src images app type getting started term cursor png alt getting started icon br b sentiment analysis br a href samples csharp getting started binaryclassification sentimentanalysis c a nbsp nbsp a href samples fsharp getting started binaryclassification sentimentanalysis f a b td td align middle img src images spam detection png alt movie recommender chart br img src images app type getting started term cursor png alt getting started icon br b spam detection br a href samples csharp getting started binaryclassification spamdetection c a nbsp nbsp a href samples fsharp getting started binaryclassification spamdetection f a b td td align middle img src images anomaly detection png alt power anomaly detection chart br img src images app type getting started term cursor png alt getting started icon br b credit card fraud detection br binary classification br a href samples csharp getting started binaryclassification creditcardfrauddetection c a nbsp nbsp nbsp a href samples fsharp getting started binaryclassification creditcardfrauddetection f a b td tr tr td align middle img src images disease detection png alt disease detection chart br img src images app type getting started term cursor png alt getting started icon br b heart disease prediction br a href samples csharp getting started binaryclassification heartdiseasedetection c a td td td td td tr tr td align middle colspan 3 multi class classification td tr tr td align middle img src images issue labeler png alt issue labeler chart br img src images app type e2e black png alt end to end app icon br b issues classification br a href samples csharp end to end apps multiclassclassification githublabeler c a nbsp nbsp a href samples fsharp end to end apps multiclassclassification githublabeler f a b td td align middle img src images flower classification png alt movie recommender chart br img src images app type getting started term cursor png alt getting started icon br b iris flowers classification br a href samples csharp getting started multiclassclassification iris c a nbsp nbsp a href samples fsharp getting started multiclassclassification iris f a b td td align middle img src images handwriting classification png alt movie recommender chart br img src images app type getting started term cursor png alt getting started icon br b mnist br a href samples csharp getting started multiclassclassification mnist c a b td tr tr td align middle colspan 3 recommendation td tr tr td align middle img src images product recommendation png alt product recommender chart br img src images app type getting started term cursor png alt getting started icon br b product recommendation br a href samples csharp getting started matrixfactorization productrecommendation c a h4 td td align middle img src images movie recommendation png alt movie recommender chart br img src images app type getting started term cursor png alt getting started icon br b movie recommender br matrix factorization b br a href samples csharp getting started matrixfactorization movierecommendation c a b td td align middle img src images movie recommendation png alt movie recommender chart br img src images app type e2e black png alt end to end app icon br b movie recommender br field aware factorization machines br a href samples csharp end to end apps recommendation movierecommender c a b td tr tr td align middle colspan 3 regression td tr tr td align middle img src images price prediction png alt price prediction chart br img src images app type getting started term cursor png alt getting started icon br b price prediction br a href samples csharp getting started regression taxifareprediction c a nbsp nbsp a href samples fsharp getting started regression taxifareprediction f a b td td align middle br img src images sales forcasting png alt sales forecasting chart br img src images app type e2e black png alt end to end app icon br b sales forecasting regression br a href samples csharp end to end apps forecasting sales c a br br b td td align middle img src images demand prediction png alt demand prediction chart br img src images app type getting started term cursor png alt getting started icon br b demand prediction br a href samples csharp getting started regression bikesharingdemand c a nbsp nbsp nbsp a href samples fsharp getting started regression bikesharingdemand f a b td tr tr td align middle colspan 3 time series forecasting td tr tr td align middle br img src images sales forcasting png alt sales forecasting chart br img src images app type e2e black png alt end to end app icon br b sales forecasting time series br a href samples csharp end to end apps forecasting sales c a br br b td td td td td tr tr td align middle colspan 3 anomaly detection td tr tr td align middle img src images spike detection png alt spike detection chart br br b sales spike detection br img src images app type getting started term cursor png alt getting started icon nbsp a href samples csharp getting started anomalydetection sales c a nbsp nbsp nbsp nbsp nbsp img src images app type e2e black png alt end to end app icon nbsp a href samples csharp end to end apps anomalydetection sales c a b td td align middle img src images spike detection png alt spike detection chart br img src images app type getting started term cursor png alt getting started icon br b power anomaly detection br a href samples csharp getting started anomalydetection powermeterreadings c a b td td align middle img src images anomaly detection png alt power anomaly detection chart br img src images app type getting started term cursor png alt getting started icon br b credit card fraud detection br anomaly detection br a href samples csharp getting started anomalydetection creditcardfrauddetection c a b td tr tr td align middle colspan 3 clustering td tr tr td align middle img src images customer segmentation png alt customer segmentation chart br img src images app type getting started term cursor png alt getting started icon br b customer segmentation br a href samples csharp getting started clustering customersegmentation c a nbsp nbsp a href samples fsharp getting started clustering customersegmentation f a b td td align middle img src images clustering png alt iris flowers chart br img src images app type getting started term cursor png alt getting started icon br b iris flowers clustering br a href samples csharp getting started clustering iris c a nbsp nbsp a href samples fsharp getting started clustering iris f a b td td td tr tr td align middle colspan 3 ranking td tr tr td align middle img src images ranking numbered png alt ranking chart br img src images app type getting started term cursor png alt getting started icon br b rank search engine results br a href samples csharp getting started ranking web c a b td td td td td tr tr td align middle colspan 3 computer vision td tr tr td align middle img src images image classification png alt image classification chart br b image classification training br high level api br img src images app type getting started term cursor png alt getting started icon nbsp a href samples csharp getting started deeplearning imageclassification training c a nbsp a href samples fsharp getting started deeplearning imageclassification training f a nbsp nbsp nbsp nbsp nbsp nbsp td td align middle img src images image classification png alt image classification chart br b image classification predictions br pretrained tensorflow model scoring br img src images app type getting started term cursor png alt getting started icon nbsp a href samples csharp getting started deeplearning imageclassification tensorflow c a nbsp a href samples fsharp getting started deeplearning imageclassification tensorflow f a nbsp nbsp nbsp nbsp nbsp nbsp img src images app type e2e black png alt end to end app icon nbsp a href samples csharp end to end apps deeplearning imageclassification tensorflow c a b td b td td align middle img src images image classification png alt image classification chart br b image classification training br tensorflow featurizer estimator br img src images app type getting started term cursor png alt getting started icon nbsp a href samples csharp getting started deeplearning tensorflowestimator c a nbsp a href samples fsharp getting started deeplearning tensorflowestimator f a b td tr tr td align middle br img src images object detection png alt object detection chart br b object detection br onnx model scoring br img src images app type getting started term cursor png alt getting started icon nbsp a href samples csharp getting started deeplearning objectdetection onnx c a nbsp nbsp nbsp nbsp nbsp img src images app type e2e black png alt end to end app icon nbsp a href samples csharp end to end apps objectdetection onnx c a b td tr table br br table tr td align middle colspan 3 cross cutting scenarios td tr tr td align middle img src images web png alt web image br img src images app type e2e black png alt end to end app icon br b scalable model on webapi br a href samples csharp end to end apps scalablemlmodelonwebapi integrationpkg c a b td td align middle img src images web png alt web image br img src images app type e2e black png alt end to end app icon br b scalable model on razor web app br a href samples modelbuilder binaryclassification sentiment razor c a b td td align middle img src images azure functions 20 png alt azure functions logo br img src images app type e2e black png alt end to end app icon br b scalable model on azure functions br a href samples csharp end to end apps scalablemlmodelonazurefunction c a b td tr tr td align middle img src images smile png alt database chart br img src images app type e2e black png alt end to end app icon br b scalable model on blazor web app br a href samples csharp end to end apps scalablesentimentanalysisblazorwebapp c a b td td align middle img src images large data set png alt large file chart br img src images app type getting started term cursor png alt getting started icon br b large datasets br a href samples csharp getting started largedatasets c a b td td align middle img src images database png alt database chart br img src images app type getting started term cursor png alt getting started icon br b loading data with databaseloader br a href samples csharp getting started databaseloader c a b td tr tr td align middle img src images database png alt database chart br img src images app type getting started term cursor png alt getting started icon br b loading data with loadfromenumerable br a href samples csharp getting started databaseintegration c a b td td align middle img src images model explain smaller png alt model explainability chart br img src images app type e2e black png alt end to end app icon br b model explainability br a href samples csharp end to end apps model explainability c a b td td align middle img src images extensibility png alt extensibility icon br img src images app type e2e black png alt end to end app icon br b export to onnx br a href samples csharp getting started regression onnxexport c a b td tr table automate ml net models generation preview state the previous samples show you how to use the ml net api 1 0 ga since may 2019 however we re also working on simplifying ml net usage with additional technologies that automate the creation of the model for you so you don t need to write the code by yourself to train a model you simply need to provide your datasets the best model and the code for running it will be generated for you these additional technologies for automating model generation are in preview state and currently only support binary classification multiclass classification and regression in upcoming versions we ll be supporting additional ml tasks such as recommendations anomaly detection clustering etc cli samples preview state the ml net cli command line interface is a tool you can run on any command prompt windows mac or linux for generating good quality ml net models based on training datasets you provide in addition it also generates sample c code to run score that model plus the c code that was used to create train it so you can research what algorithm and settings it is using cli command line interface samples binary classification sample samples cli binaryclassification cli multiclass classification sample samples cli multiclassclassification cli regression sample samples cli regression cli automl api samples preview state these samples use the 0 1 x version of the automl api while these apis still work in version 0 2 x we recommend using the new apis introduced in 0 2 x and later for 0 2 x samples see ml net 2 0 samples samples csharp getting started mlnet2 readme md ml net automl api is basically a set of libraries packaged as a nuget package you can use from your net code automl eliminates the task of selecting different algorithms hyperparameters automl will intelligently generate many combinations of algorithms and hyperparameters and will find high quality models for you automl api samples binary classification sample samples csharp getting started binaryclassification automl multiclass classification sample samples csharp getting started multiclassclassification automl ranking sample samples csharp getting started ranking automl ranking regression sample samples csharp getting started regression automl advanced experiment sample samples csharp getting started advancedexperiment automl additional ml net community samples in addition to the ml net samples provided by microsoft we re also highlighting samples created by the community showcased in this separated page ml net community samples https github com dotnet machinelearning samples blob main docs community samples md those community samples are not maintained by microsoft but by their owners if you have created any cool ml net sample please add its info into this request issue https github com dotnet machinelearning samples issues 86 and we ll publish its information in the mentioned page eventually translations of samples chinese simplified https github com feiyun0112 machinelearning samples zh cn learn more see ml net guide https docs microsoft com en us dotnet machine learning for detailed information on tutorials ml basics etc api reference check out the ml net api reference https docs microsoft com dotnet api view ml dotnet to see the breadth of apis available contributing we welcome contributions please review our contribution guide contributing md community please join our community on gitter join the chat at https gitter im dotnet mlnet https badges gitter im join 20chat svg https gitter im dotnet mlnet utm source badge utm medium badge utm campaign pr badge utm content badge this project has adopted the code of conduct defined by the contributor covenant http contributor covenant org to clarify expected behavior in our community for more information see the net foundation code of conduct https dotnetfoundation org code of conduct license ml net samples https github com dotnet machinelearning samples are licensed under the mit license license | machine-learning algorithms dotnet csharp ml | ai |
react-native-quickstart | react native quickstart this repository applied basic architecture react native react native 0 58 4 step configuration install sdk 28 build tools yarn install react native run android or react native run ios | mobile-development react-native | front_end |
teatro-ucsal-back-v2 | teatro ucsal img src https teatroclarorio com br wp content uploads 2020 02 teatro claro rio galeria 4 jpg alt imagem teatro projeto feito como avalia o parcial da disciplina t picos avan ados em banco de dados com o professor andr ricardo magalh es ajustes e melhorias hr grupo ana carla miranda anderson andrei ara jo dmitry rocha e silva jo o lucas requi o murillo caetano jesus hr pr requisitos o sistema dever permitir reservar e adquirir bilhetes para espet culos em um teatro na reserva os dados do cliente devem ser mantidos bilhetes reservados e n o retirados at 48h antes do in cio do espet culo s o automaticamente cancelados num per odo de 01 um ano clientes que fizeram reserva e n o retiraram o bilhete por 03 tr s vezes s o automaticamente proibidos de reservar bilhetes por um per odo de 06 seis meses o sistema deve permitir ainda o cancelamento de reservas de bilhetes os bilhetes podem ser entregues a domic lio sendo cobrada uma taxa de entrega o sistema deve permitir tamb m a reserva de poltronas para convidados vip s esses bilhetes n o s o comprados e n o devem ser cancelados de acordo com a regra de 48h de anteced ncia as poltronas do teatro s o numeradas de acordo com a fila e posi o que ocupam na fila os valores dos ingressos variam de acordo com o espet culo e localiza o da poltrona | server |
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phasellm | phasellm large language model evaluation and workflow framework from phase ai https phaseai com follow us on twitter https twitter com phasellm for updates star us on github https github com wgryc phasellm read the docs https phasellm readthedocs io en latest autoapi phasellm index html module reference tutorials and code examples are below installation you can install phasellm via pip pip install phasellm installing from pypi does not include libraries for running llms locally please run pip install phasellm complete if you plan on using llms locally e g our dollywrapper sample demos and products are in the demos and products folder clone this repository and follow instructions in the readme md file in each product folder to run those introduction the coming months and years will bring thousands of new products and experienced powered by large language models llms like chatgpt or its increasing number of variants whether you re using openai s chatgpt anthropic s claude or something else all together you ll want to test how well your models and prompts perform against user needs as more models are launched you ll also have a bigger range of options phasellm is a framework designed to help manage and test llm driven experiences products content or other experiences that product and brand managers might be driving for their users here s what phasellm does 1 we standardize api calls so you can plug and play models from openai cohere anthropic or other providers 2 we ve built evaluation frameworks so you can compare outputs and decide which ones are driving the best experiences for users 3 we re adding automations so you can use advanced models e g gpt 4 to evaluate simpler models e g gpt 3 to determine what combination of prompts yield the best experiences especially when taking into account costs and speed of model execution phasellm is open source and we envision building more features to help with model understanding we want to help developers data scientists and others launch new robust products as easily as possible if you re working on an llm product please reach out we d love to help out example evaluating travel chatbot prompts with gpt 3 5 claude and more phasellm makes it incredibly easy to plug and play llms and evaluate them in some cases with other llms suppose you re building a travel chatbot and you want to test claude and cohere against each other using gpt 3 5 what s awesome with this approach is that 1 you can plug and play models and prompts as needed and 2 the entire workflow takes a small amount of code this simple example can easily be scaled to much more complex workflows so time for the code first load your api keys python import os from dotenv import load dotenv load dotenv openai api key os getenv openai api key anthropic api key os getenv anthropic api key cohere api key os getenv cohere api key we re going to set up the evaluator which takes two llm model outputs and decides which one is better for the objective at hand python from phasellm eval import gptevaluator we ll use gpt 3 5 as the evaluator default for gptevaluator e gptevaluator openai api key now it s time to set up the experiment in this case we ll set up an objective which describes what we re trying to achieve with our chatbot we ll also provide 5 examples of starting chats that we ve seen with our users python our objective objective we re building a chatbot to discuss a user s travel preferences and provide advice chats that have been launched by users travel chat starts i m planning to visit poland in spring i m looking for the cheapest flight to europe next week i am trying to decide between prague and paris for a 5 day trip i want to visit europe but can t decide if spring summer or fall would be better i m unsure i should visit spain by flying via the uk or via france now we set up our cohere and claude models python from phasellm llms import coherewrapper claudewrapper cohere model coherewrapper cohere api key claude model claudewrapper anthropic api key finally we launch our test we run an experiments where both models generate a chat response and then we have gpt 3 5 evaluate the response python print running test 1 cohere and 2 claude for tcs in travel chat starts messages role system content objective role user content tcs response cohere cohere model complete chat messages assistant response claude claude model complete chat messages assistant pref e choose objective tcs response cohere response claude print f pref in this case we simply print which of the two models was preferred voila you ve got a suite to test your models and can plug and play three major llms contact us if you have questions requests ideas etc please reach out at w at phaseai dot com | ai gpt llm openai python | ai |
ICT502 | ict502 database engineering getting started lab exercise for ict502 database engineering are sent to lecturer and checked some of the activies are complete but not all of it are 100 correct | server |
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Assessment_1 | assessment 1 | server |
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blockchain | ultimate go advanced engineering circleci https circleci com gh ardanlabs blockchain svg style svg https circleci com gh ardanlabs blockchain go report card https goreportcard com badge github com ardanlabs blockchain https goreportcard com report github com ardanlabs blockchain go mod go version https img shields io github go mod go version ardanlabs blockchain https github com ardanlabs blockchain copyright 2021 2022 ardan labs hello ardanlabs com description this class teaches you how to build a semantically correct blockchain in go the implementation of the ardan blockchain takes inspiration from both bitcoin and ethereum the class is a mix of lecture and coding during the class you will learn the different aspects of blockchain technology and pair program with the instructor as you build this code base from scratch please look at the wiki https github com ardanlabs blockchain wiki for more details licensing licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license learn more reach out about corporate training events open enrollment live training sessions and on demand learning options ardan labs www ardanlabs com hello ardanlabs com to attend any of our high performance tranings check out this link https www ardanlabs com training index purchase video https github com ardanlabs blockchain purchase video experience https github com ardanlabs blockchain our experience teacher https github com ardanlabs blockchain our teacher more about go https github com ardanlabs blockchain more about go minimal qualified student https github com ardanlabs blockchain minimal qualified student important reading https github com ardanlabs blockchain important reading before you come to class https github com ardanlabs blockchain before you come to class purchase video we have not yet produced the video for this class but to see other go related classes check out this link education ardanlabs com https education ardanlabs com our experience we have taught go to thousands of developers all around the world since 2014 there is no other company that has been doing it longer and our material has proven to help jump start developers 6 to 12 months ahead of their knowledge of go we know what knowledge developers need in order to be productive and efficient when writing software in go our classes are perfect for intermediate level developers who have at least a few months to years of experience writing code in go our classes provide a very deep knowledge of the programming langauge with a big push on language mechanics design philosophies and guidelines we focus on teaching how to write code with a priority on consistency integrity readability and simplicity we cover a lot about if performance matters with a focus on mechanical sympathy data oriented design decoupling and writing debugging production software our teacher william kennedy goinggodotnet https twitter com goinggodotnet william kennedy is a managing partner at ardan labs in miami florida a mobile web and systems development company bill has written extensively on the ardan labs blog has written two books and has trained thousands of developers worldwide in go bill is also a founding member of gobridge which is working to increase go adoption through diversity video training ultimate go video https education ardanlabs com ardan labs youtube channel http youtube ardanlabs com blog going go https www ardanlabs com blog writing running mongodb queries concurrently with go http blog mongodb org post 80579086742 running mongodb queries concurrently with go go in action https www manning com books go in action articles it world canada http www itworldcanada com article nascent google development language shows promise for more productive coding 387449 video p99 talk 2022 evaluating performance in go https www youtube com watch v pyms urosxs gophercon europe 2022 practical memory profiling https www youtube com watch v 6qafkjgwsns dgrpah day 2021 getting started with dgraph and graphql https www youtube com watch v 5l4pubdqseo gdn event 1 2021 gobridge needs your help https www youtube com watch v tst0oi97cvq t 2s training within the go community 2019 https www youtube com watch v psr1twjzzam feature youtu be gophercon australia 2019 modules https www youtube com watch v mvxbvr 6tac golab 2019 you want to build a web service https www youtube com watch v iv0wrvb31pg gophercon singapore 2019 garbage collection semantics https www youtube com watch v q4howwdzuhs gophercon india 2019 channel semantics https www youtube com watch v ahaf1xfr he gowayfest minsk 2018 profiling web apps https www youtube com watch v gbmfpegqgw gophercon singapore 2018 optimizing for correctness https engineers sg video optimize for correctness gopherconsg 2018 2610 gophercon india 2018 what is the legacy you are leaving behind https www youtube com watch v j3zcuc06oxo t 0s index 11 list plhjxe57cki63celk2kmt3 vi8j2eihtqz code dive 2017 optimizing for correctness https www youtube com watch v otljn8nqdyo code dive 2017 go concurrency design https www youtube com watch v orctymf4bta dotgo 2017 behavior of channels https www youtube com watch v zdckzn4 dck gophercon singapore 2017 escape analysis https engineers sg video escape analysis and memory profiling gophercon sg 2017 1746 capital go 2017 concurrency design https www youtube com watch v ygooucrrgre index 10 list plegxioplk9ekdl h y sblhlop ia7cj5 gophercon india 2017 package oriented design https www youtube com watch v spkm5cybwja t 0m56s gophercon india 2015 go in action https www youtube com watch v qkpw8 pf0sm golanguk 2016 dependency management https youtu be cdhucjshju8 gothamgo 2015 error handling in go https vimeo com 115782573 gophercon 2014 building an analytics engine https www youtube com watch v efjrq1lgkuk prague meetup 2021 go module engineering decisions https youtu be m8lgcxv2lhi practical understanding of scheduler semantics 2021 https www youtube com watch v p2cjq3dq2q0 go generics draft proposal 2020 https www youtube com watch v giepspmbmhm t 2069s hack potsdam 2017 tech talk with william kennedy https www youtube com watch v sbzj sjhgs8 chicago meetup 2016 an evening https vimeo com 199832344 vancouver meetup 2016 go talk ask me anything with william kennedy https www youtube com watch v 7yclibg1ekm t 91s vancouver meetup 2015 compiler optimizations in go https www youtube com watch v aqipeq39aek bangalore meetup 2015 oop in go https youtu be grpufjtwsoo gosf meetup the nature of constants in go https www youtube com watch v zuchmaooguq london meetup mechanical sympathy https skillsmatter com skillscasts 8353 london go usergroup vancouver meetup decoupling from change https www youtube com watch v 7yclibg1ekm feature youtu be podcasts ardan labs podcast on going series https ardanlabs buzzsprout com mangtas nation a golang deep dive with bill kennedy https anchor fm mangtasnation episodes a golang deep dive with bill kennedy s2 ep3 e1ij9c3 gotime design philosophy https changelog com gotime 172 gotime learning and teaching go https changelog com gotime 72 gotime bill kennedy on mechanical sympathy https changelog com gotime 6 gotime discussing imposter syndrome https changelog com gotime 30 hellotechpros your tech interviews are scaring away brilliant people http hellotechpros com william kennedy people hellotechpros the 4 cornerstones of writing software http hellotechpros com bill kennedy productivity more about go go is an open source programming language that makes it easy to build simple reliable and efficient software although it borrows ideas from existing languages it has a unique and simple nature that make go programs different in character from programs written in other languages it balances the capabilities of a low level systems language with some high level features you see in modern languages today this creates a programming environment that allows you to be incredibly productive performant and fully in control in go you can write less code and do so much more go is the fusion of performance and productivity wrapped in a language that software developers can learn use and understand go is not c yet we have many of the benefits of c with the benefits of higher level programming languages the ecosystem of the go programming language https henvic dev posts go henrique vicente the why of go https www infoq com presentations go concurrency gc carmen andoh go ten years and climbing https commandcenter blogspot com 2017 09 go ten years and climbing html rob pike the eigenvector of why we moved from language x to language y https erikbern com 2017 03 15 the eigenvector of why we moved from language x to language y html erik bernhardsson learn more https talks golang org 2012 splash article go team simplicity is complicated https www youtube com watch v rfejph tahm rob pike getting started in go http aarti github io 2016 08 13 getting started in go aarti parikh minimal qualified student the material has been designed to be taught in a classroom environment the code is well commented but missing some contextual concepts and ideas that will be covered in class students with the following minimal background will get the most out of the class studied cs in school or has a minimum of two years of experience programming full time professionally familiar with structural and object oriented programming styles has worked with arrays lists queues and stacks understands processes threads and synchronization at a high level operating systems has worked with a command shell knows how to maneuver around the file system understands what environment variables are important reading please check out this page of important reading documentation reading readme md you will find articles and videos around topics related to this repo before you come to class the following is a set of tasks that can be done prior to showing up for class we will also do this in class if anyone has not completed it however the more attendees that complete this ahead of time the more time we have to cover additional training material prep work you will get the most out of this advanced go class if you have taken the ultimate go and service classes first the ultimate go and service classes are critical classes to understanding the code in this class ultimate go classes https www ardanlabs com ultimate go tabpanel scholarship https docs google com forms d e 1faipqlschez6zg1fser2xiizgnb8qm2a5jdb7n3eowx5geip18ostaq viewform this is a free and great class to learning concepts behind blockchains watch videos 1 2 6 and 8 blockchain class https cryptocurrencyclass github io patrick mccorry joining the go slack community we use a slack channel to share links code and examples during the training this is free this is also the same slack community you will use after training to ask for help and interact with may go experts around the world in the community 1 using the following link fill out your name and email address https invite slack gobridge org 1 check your email and follow the link to the slack application 1 join the training channel by clicking on this link https gophers slack com messages training 1 click the join channel button at the bottom of the screen installing the training material while many of the examples can be done using the online playground http play golang org some may find it easier to complete them with their local editor to do so you will want to load the training material locally to your machine from a command prompt issue the following commands cd home mkdir code cd code git clone https github com ardanlabs blockchain cd blockchain all material is licensed under the apache license version 2 0 january 2004 http www apache org licenses license 2 0 | blockchain |
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parallel_ml_tutorial | parallel machine learning with scikit learn and ipython video tutorial https raw github com ogrisel parallel ml tutorial master resources youtube screenshot png https www youtube com watch v ifkrt3bcctg video recording of this tutorial given at pycon in 2013 the tutorial material has been rearranged in part and extended look at the title of the of the notebooks to be able to follow along the presentation browse the static notebooks on nbviewer ipython org http nbviewer ipython org github ogrisel parallel ml tutorial tree master rendered notebooks scope of this tutorial learn common machine learning concepts and how they match the scikit learn estimator api learn about scalable feature extraction for text classification and clustering learn how to perform parallel cross validation and hyper parameters grid search in parallel with ipython learn to analyze the kinds of common errors predictive models are subject to and how to refine your modeling to take this analysis into account learn to optimize memory allocation on your computing nodes with numpy memory mapping features learn how to run a cheap ipython cluster for interactive predictive modeling on the amazon ec2 spot instances using starcluster http star mit edu cluster target audience this tutorial targets developers with some experience with scikit learn and machine learning concepts in general it is recommended to first go through one of the tutorials hosted at scikit learn org http scikit learn org if you are new to scikit learn you might might also want to have a look at scipy lecture notes http scipy lectures github com first if you are new to the numpy scipy matplotlib ecosystem setup install numpy scipy matplotlib ipython psutil and scikit learn in their latest stable version e g ipython 2 2 0 and scikit learn 0 15 2 at the time of writing you can find up to date installation instructions on scikit learn org http scikit learn org and ipython org http ipython org to check your installation launch the ipython interactive shell in a console and type the following import statements to check each library import numpy import scipy import matplotlib import psutil import sklearn if you don t get any message everything is fine if you get an error message please ask for help on the mailing list of the matching project and don t forget to mention the version of the library you are trying to install along with the type of platform and version e g windows 8 1 ubuntu 14 04 osx 10 9 you can exit the ipython shell by typing exit fetching the data it is recommended to fetch the datasets ahead of time before diving into the tutorial material itself to do so run the fetch data py script in this folder python fetch data py using the ipython notebook to follow the tutorial the tutorial material and exercises are hosted in a set of ipython executable notebook files to run them interactively do cd notebooks ipython notebook this should automatically open a new browser window listing all the notebooks of the folder you can then execute the cell in order by hitting the shift enter keys and watch the output display directly under the cell and the cursor move on to the next cell go to the help menu for links to the notebook tutorial credits some of this material is adapted from the scipy 2013 tutorial http github com jakevdp sklearn scipy2013 original authors gael varoquaux gaelvaroquaux https twitter com gaelvaroquaux http gael varoquaux info jake vanderplas jakevdp https twitter com jakevdp http jakevdp github com olivier grisel ogrisel https twitter com ogrisel http ogrisel com | ai |
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documentation | p align center img src https raw githubusercontent com smartcontractkit documentation main public chainlink docs svg style background white padding 8px alt chainlink docs logo width 400 p welcome to chainlink developer documentation repository this repository is the source for developer documentation on docs chain link https docs chain link which is a resource for smart contract developers and node operators building decentralized applications on several networks this documentation is open source if you want to contribute tutorials or improvements see the contributing https github com smartcontractkit documentation blob main contributing md guide note the code samples in this documentation are examples for using chainlink products and services and are provided to help you understand how to interact with chainlink s systems and services so that you can integrate them into your own these templates are provided as is and as available without warranties of any kind have not been audited and may be missing key checks or error handling to make the usage of the product more clear do not use the example code in a production environment without completing your own audits and application of best practices neither chainlink labs the chainlink foundation nor chainlink node operators are responsible for unintended outputs that are generated due to errors in code developing to run a local development environment use the following command npm install npm run dev docs architecture all docs are markdown and stored in src content navigation is json in src config sidebar ts deploy preview this repo is configured to automatically create a preview environment on vercel when a pr is opened after the deployment is approved the vercel bot will leave a comment with a link to the preview on your pr deploying to production this repo is configured to automatically update the production https docs chain link site when commits are pushed to the main branch | chainlink solidity defi blockchain | blockchain |
iot-mqtt | release 2 0 x raft x mqtt v3 1 1 x qos0 qos1 qos2 x apache version 2 0 https www yuque com cloudshi ms000x apuehg java public static string serverpath new string src main resources cluster server1 yaml src main resources cluster server2 yaml src main resources cluster server3 yaml public static void main string args throws exception for int i 0 i serverpath length i final int num i new thread new runnable public void run try brokerstartup start serverpath num catch joranexception e e printstacktrace start server yaml servername server1 username admin mqtt broker password 123456 mqtt broker tcpport 18081 mqtt broker logbackxmlpath src main resources logback test xml log performanceconfig selectorthreadnum 3 iothreadnum 8 tcpbacklog 1024 tcpnodelay false tcpreuseaddr true tcpkeepalive false tcpsndbuf 65536 tcprcvbuf 65536 useepoll false pooledbytebufallocatorenable false popsize 100 popinterval 1000 clientsize 10000 queuesize 50000 0 rocksdb 1 rheakv storetype 1 rocksdbpath store rocksdb1 server datacenterid 1 id machineid 1 id clustername rhea mqtt server placementdriveroptions fake true regionroutetableoptionslist raft regionid 1 nodeoptions timerpoolsize 1 rpcprocessorthreadpoolsize 4 storeengineoptions rocksdboptions dbpath store rhea db server raftdatapath store rhea raft server serveraddress ip 127 0 0 1 port 18181 raft regionengineoptionslist raft regionid 1 nodeoptions timerpoolsize 1 rpcprocessorthreadpoolsize 4 leastkeysonsplit 10 initialserverlist 127 0 0 1 18181 127 0 0 1 18182 127 0 0 1 18183 raft qq https cdn nlark com yuque 0 2020 png 1624173 1593050500510 b2821135 c8e0 4969 8417 97ab50bcc65f png | mqtt java netty rocksdb broker distributed raft jraft rheakv | server |
pixel_webdev | run sublime and git on your chromebook pixel http i imgur com nnvuxhk png this is me trying to remember all the stuff i did to turn my pixel into a web dev machine hopefully i can save you a couple days of your life experimenting with all sorts of linuxy stuff before you can be productive includes ubuntu via crouton openbox window manager themes workhorse theme from ndrew1 on box look org cc by mediterraneannight gtk theme gpl fonts all the native chromeos ones for consistency inconsolata ofl roboto apache pantheon terminal node npm and bower sublime text package control mit spacegray eighties theme scaled for the pixel s display git along with an ssh keychain google cloud sdk with appengine google has replaced their zipball with an installer i need to take the time to automatically install it in the mean time try sudo sh e opt google cloud sdk install sh installation 1 make sure your device is in dev mode http goo gl 81hyr 2 click download zip in the sidebar of this page 3 open the files app and extract the zip you just downloaded 4 hit ctrl alt t to enter the crosh shell 5 shell 6 sudo su 7 cd home chronos user downloads pixel webdev master 8 sh e run from crosh sh 9 cp r usr local chroots dev opt pixel webdev 10 sudo enter chroot n dev 11 sudo chown r user user opt pixel webdev 12 cd opt pixel webdev 13 sudo sh e run from chroot as root sh 14 sh e run from chroot as user sh usage crouton sandboxes your linux apps into their own chroot this typically means that you ll have a separate desktop for your linux apps e g you won t be able to see sublime and chrome at the same time i m currently experimenting with getting both windows to appear on a single screen so you have two options run sublime in a separate desktop sudo enter chroot n dev xinit this is the way most people use crouton to switch between chromeos and crouton hit ctrl alt shift back or ctrl alt shift forward the sandbox between chromeos and crouton means you won t be able to copy from one environment and paste into the other but drinkcat is working on it run sublime inside chromeos sudo enter chroot n dev export display 0 0 openbox replace subl this is pretty close to ideal you don t have to mess with constantly switching between workspaces everything is in one place it s also ridiculously hacky clever experimental so there are some rough edges at the current time including scrolling with the trackpad or the touchscreen only works in chrome the keycommands to switch between windows in chromeos don t work while you re in this mode chromeos is treated like a full screen window don t try to shrink it or your mouse will get stuck inside the window s bounds which makes it really hard to unshrink it your lockscreen will only protect your browser session if someone wakes up your computer while sublime is open there s nothing to keep him from messing with your files this is technically true of both methods but most people won t know how to circumvent the lock screen if you use a separate desktop if you bump the trackpad while typing your cursor might jump to wherever your mouse is copy paste between chromeos and crouton doesn t work in this mode either i just discovered how to run sublime inside chromeos as i learn more i ll update this project to improve the merged experience | front_end |
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Sentiment-Analysis-Final-Project | sentiment analysis final project final project of bachelor degree information system del insitute of technology | machine-learning svm-classifier mlp-classifier sentiment-analysis emotion-analysis jupyter-notebook cross-validation hyperparameter-optimization | server |
FrontEndCourseExercises-1 | fe jpg front end course exercises exercises for the frontend web development course each exercise must be completed when instructed to do so by your instructor s or by the material instructions if you haven t done it already fork this repository and add your instructor s as collaborator s to your forked repository sync your forked repo with the original repo important in order to pull from the original repo whenever it gets updated here are the steps bash git remote add upstream https github com socialhackersacademy frontendcourseexercises run this just once git fetch upstream git checkout main git merge upstream main create a new branch for each exercise and create a pull request back to your main branch on your own repository make sure to assign your instructor s and ask for a review on the pull request submit the pull request url to the quiz the url should look like this https github com kostasx frontendcourseexercises pull 1 where kostasx will be your github username diff important you will need to create a pull request merging back into your own repository s main branch and not the main repository socialhackersacademy if you get stuck don t hesitate to ask for help you can check out this video https www youtube com watch v ugmee udgwi that walks you through the process of forking the exercises repo working on a branch and doing a pr web development 101 javascript basics fundamentals 1 fundamentals 201 fundamentals 2 fundamentals 202 fundamentals 3 fundamentals 203 fundamentals 4 fundamentals 204 fundamentals 5 fundamentals 205 working with arrays and the dom guest lecture with paulin de naurois working 20with 20arrays 20and 20the 20dom faq question in the repo we only do the exercises it tells us to and ignore the other exercises inside correct for example i am doing js fundamentals 2 i should ignore the css grid one answer yes that s correct you will have to work on the exercise s you are instructed to on each lesson updated 11 02 2021 image by kelly sikkema on unsplash | front_end |
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model-catalog | version https img shields io badge version 0 0 1 blue model catalog a collection of standardized json descriptors for large language model llm model files model name json a single json file describes a model its authors additional resources such as an academic paper as well as available model files and their providers version 0 0 1 of this format attempts to capture an informative set of factors including model size e g 7b 13b 30b etc model architecture such as llama mpt pythia etc model file format e g ggml as well as quantization format e g q4 0 q4 k m etc see examples guanaco 7b json models guanaco 7b json samantha 1 1 llama 7b json models samantha 1 1 llama 7b json nous hermes 13b json models nous hermes 13b json catalog json a github action picks up json files from the models directory and merges them into one catalog json file the contents of each json file is validated by another github action using a json schema contribution process you re invited to help catalog models and improve upon this description format 1 fork this repo and create a new development branch 2 create a new model json file and place it the models directory 3 validate your file against the expected json schema using the validate py tool or by running createcatalog py 4 open a pr with your change 5 ensure all github actions complete successfully note do not modify catalog json manually | ai |
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Klondike | klondike build status https ci appveyor com api projects status vxqnth8eyerocfpm branch master svg true https ci appveyor com project chriseldredge klondike branch master ember front end that builds on nuget lucene for private nuget https www nuget org package hosting binaries available from the releases tab on github alternatively you can clone the klondike release https github com themotleyfool klondike release git repo to make upgrading easier preview binaries can be obtained from the artifacts tab of a successful appveyor build https ci appveyor com project chriseldredge klondike what is klondike klondike is an asp net web application you deploy to your own web server or to the cloud that works as a private nuget package feed for storing private packages your organization creates klondike can also automatically restore packages sourced from 3rd party feeds such as the nuget org public feed to keep your build server humming even when nuget org is unavailable klondike performs dramatically better than the standard nuget server provider and adds lots of extra features you can t get anywhere else klondike uses lucene net meaning that the install footprint is light simply grab the binaries stand up an iis site or run the self hosted exe and you re done much easier than deploying your own nuget gallery how to deploy klondike 1 grab a binary zip from the releases tab or clone klondike release https github com themotleyfool klondike release 1 customize settings config src klondike webhost settings config 1 create a site in iis using a net v4 0 integrated pipeline application pool n b klondike works best deployed as a root application and is only supported in this configuration there are known issues with nuget clients when attempting to host klondike as a child application on a virtual path in addition the ember web application will not work correctly unless it is rebuilt with a different virtual path app pool advanced configuration klondike is designed to run as a single process to avoid conflicting writes on the lucene index files adjust your application pool accordingly make sure maximum worker processes is set to 1 make sure disable overlapped recycle is set to true authentication and role based security klondike supports external authentication providers such as windows active directory basic auth and ntlm these are configured in iis manager and other tools disable anonymous authentication to require authentication even for read access to klondike in addition to standard authentication klondike supports authentication by using the x nuget apikey http request header to be compatible with nuget clients that push and delete packages local administrator browsing or accessing the klondike app from a local network interface on the same machine will implicitly grant access as localadministrator this account is allowed to create additional users push and delete packages you can disable this behavior by editing handlelocalrequestsasadmin in settings config src klondike webhost settings config mapping active directory roles to klondike edit the rolemappings section in web config src klondike webhost web config to grant klondike roles for user administration and package management to existing roles in your external security provider such as active directory multiple groups can be specified delimited by commas membership in any one role is sufficient to grant a klondike role the available roles and their permissions are packagemanager allowed to push and delete packages accountadministrator allowed to administer accounts creating users and passwords any user who has the accountadministrator role may create delete and modify accounts and api keys this includes the localadministrator account to access this feature browse to klondike and select admin in the top navigation then manage accounts self hosted klondike the binary release also includes klondike selfhost exe in the bin directory it can be run from the console using mono or the net framework klondike selfhost exe port 8080 or mono klondike selfhost exe interactive port 8080 klondike requires mono 4 2 0 or later if no port is specified 8080 is used as a default see the klondike selfhost readme src klondike selfhost readme md for more information building locally this repository consists of two components 1 ember front end built and packaged by ember cli http www ember cli com 1 c project built by msbuild or xbuild front end prerequisites node node and npm should be on your path install ember cli and bower if you haven t already npm install g ember cli 0 2 7 install dependencies npm install bower install finally build ember build this puts the built app into dist note if you do not have the net 4 5 sdk or mono 3 6 mdk installed you can skip building the net assets by using the ember only environment ember build environment ember only net back end the c projects can be built on windows or os x linux on windows install visual studio 2013 and the microsoft net framework 4 5 sdk on os x linux install the mono mdk http www mono project com download mono can also be installed by homebrew http brew sh on os x front end development without net you can develop the front end without needing to build or host the net code edit config environment js config environment js and set the apiurl and optionally apikey properties to point to an external klondike api endpoint then run ember serve environment ember only previewing debug release builds you can serve production builds with ember serve environment production integration tests coming real soon now | front_end |
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cosy_cafeteria | cosy cafeteria project embedded systems design 2 this repository contains the developed software and hardware for the lab project of embedded systems design 2 structure software software python server script software python wifi wifi py esp32 code software fullcode hardware hardware main pcb hardware mainpcb sensor pcb hardware sesnorboard website dashboard dashboard report report report pdf project discription to improve the experience of the students in the cafeteria an embeddedsystem will be developed to gather a variety of data this data will then bedisplay to the students through an online medium such as a website themain focus will be to gather information about the amount of people thatare in the cafeteria there are two main goals the first goal is to display theestimated waiting time to get a meal to the students secondly the amountof available seats will be tracked and displayed to the students next to thissome additional information will be gathered the temperature air qualityand sound level will be monitored to give the student a general idea about theconditions in the cafeteria this way the student can gauge if the cafeteriais suitable to study in at a certain moment an overview of the envisioned system is displayed in the figure bellow alt text https github com thomasf10 cosy cafeteria blob master report fig situation png | os |
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Data-Analysis-on-SMS-and-Natural-Language-Processing-SPAM-detector | data analysis on sms and natural language processing spam detector data analysis on sms and natural language processing spam detector | ai |
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tpiuo | repository for university subject cloud data engineering technologies br br ime jure br prezime franjkovi br email jf54321 fer hr br | cloud |
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Scalable-Walkability-Analysis-of-Melbourne | scalable walkability analysis of melbourne repository for my final semester computing project at the university of melbourne under the supervision of dr richard sinnott purpose walkability index of a suburb measures how walkable a given neighborhood is in terms of road connectivity population density and land use mix using data from australian urban research infrastructure network s aurin e infrastructure this project explores possible statistical and spatial analysis of large scale areas in our case inner melbourne by breaking them down into smaller chunks and aggregating them during analysis the approach allows us to calculate and present the walkability metric for intuitively understandable areas like suburbs or greater regions smaller chunks the idea of breaking down and aggregating regions is based on the concept of statistical areas sas in australia defined by the australian bureau of statistics sas can be represented as statistical areas level 1 sa1s the smallest units atleast for purposes of census data sa1s have a population of 200 to 800 persons averaging at about 400 statistical areas level 2 sa2s built up from sa1s sa2s are general purpose medium sized areas they represent a community or say a suburb that interacts socially and economically sa2s have an average population of about 10 000 persons statistical areas level 3 sa3s built up from sa2s sa3s provide a standardized regional breakup of australia they have populations of about 30 000 to 130 000 persons and are often used to represent regional cities statistical areas level 4 sa4s these are the largest sub state regions and are built up from sa3s there are 88 sa4s in australia why aurin s walkability workflow is the process which is used to generate the walkability data for a given area calculating the walkability of a neighborhood is a resource intensive task the cost of which in terms of time and computations increases with the area of the neighborhood say sa4 this is because the workflow involves computationally expensive tasks including road network traversal in all possible directions in a given area at the time of this project aurin s system was unable to handle processing of large areas in an acceptable time limit so aggregating the sa1s that are contained within an sa2 we can represent that sa2 without extra computational costs on the aurin systems we can also group together sa1s to represent sa3s or even sa4s similarly we can aggregate the results obtained from the walkability workflow to give a walkability score to larger regions for this project we decided to take the mean of the walkability indices of sa1s to represent the sa2s sa3s and sa4s that they belong to a very basic approach but it opens the doors to large scale analytics once the sa1s have been processed in parallel if possible but this is a data analysis project so no details on that here as an advantage we can use aurin s public health transportation economic value data for sa2s which we ll correlate with the walkability scores for more insight notebooks and analyses the project includes the following notebooks data manipulation and analyses walkability index bar plots for melbourne suburbs https github com sajal2692 scalable walkability analysis of melbourne blob master walkability 20index 20bar 20plots 20for 20melbourne 20suburbs ipynb choropleth map visualisations for melbourne https github com sajal2692 scalable walkability analysis of melbourne blob master choropleth 20map 20visualisations 20for 20melbourne 20suburb 20walkability ipynb merging social economic features with walkability indices https github com sajal2692 scalable walkability analysis of melbourne blob master merging 20socio economic 20features 20with 20walkability ipynb walkability index correlations with socio economic features https github com sajal2692 scalable walkability analysis of melbourne blob master walkability 20index 20correlations 20with 20socio economic 20metrics ipynb spatial autocorrelations for melbourne suburbs https github com sajal2692 scalable walkability analysis of melbourne blob master spatial 20autocorrelations 20for 20melbourne 20suburbs 20and 20neighbours 20 ipynb melbourne sydney walkability comparisons https github com sajal2692 scalable walkability analysis of melbourne blob master melbourne 20 20sydney 20walkability 20comparisons ipynb the code including its result relevant to a particular topic is described in its notebook it is recommended to view the notebooks in the above order to avoid missing out on code explainations tech the following technologies were used for this part of the project python 2 ipython notebooks for interactive code development and result presentation pandas python package for data analysis matplotlib and seaborn python 2d plotting library leaflet maps folium python package for building choropleth maps note choropleth maps are available in the choropleth map directory as html files and should be downloaded to be viewed as they are not inherently rendered in an ipython notebook | server |
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FrontEnd-Wikis | frontend wikis frontend enjoy if resources have error information we can fix it together welcome pull req email wsgtracy 163 com articles articles https github com maidishike frontend wikis blob master articles md about vue js vue2 0 learning resources https github com maidishike frontend wikis blob master vuejs md vue2 0 advice for learning https github com maidishike frontendinfo blob master vue advice for leaning md vue cli s pits https github com maidishike frontendinfo blob master vue cli md vue2 0 document https vuejs org vue2 0 github address https github com vuejs vue vue and webpack https github com vuejs templates webpack about js front end learning resources https github com maidishike frontend wikis blob master frontend md js common code collection https github com maidishike frontend wikis blob master js code pool md about node nodejs learning resources https github com maidishike frontend wikis blob master node md about database database learning resources https github com maidishike frontend wikis blob master database md | javascript vuejs frontend | front_end |
CS360 | cs360 mobile development | front_end |
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Natural-Language-Processing-with-Pyhton | nlp for noobs a gentle yet thorough introduction to natural language processing | ai |
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ethics-reading-list | ethics union bibliography prs welcome https img shields io badge prs welcome green http makeapullrequest com github last commit https img shields io github last commit acl org ethics reading list github contributors https img shields io github contributors acl org ethics reading list a list of ethics related resources for researchers and practitioners of natural language processing and computational linguistics this is a public list moderated by the current acl ethics committee please issue a pull request against the repository to have your suggestions discussed before they are approved for integration with the list thanks this list is intentionally kept with simple formatting in markdown to allow machine readable processing of the resource guidelines add your name to the contributors contributed by section as part of your pr include an affiliation and a weblink if you d like references follow a two tier organization by year then by first author surname tag papers with topics so that they can be found on a per topic basis use apa style where possible confine references to a single line add minimally a paper link to direct readers directly to the pdf or metadata page acl anthology for example of the paper papers are organized by tags we accept prs to add or re organize tags please help tag your own papers tags for topics starting with t tags for bibliographic type starting with type simply copy the tags from the below tags tags section to tag ordering tags alphabetically and putting t opic tags before type ones tags are provided using the shields io http shields io service prefer peer reviewed conference or journal reference to link to arxiv whenever possible contributed by put in alpha order by surname luciana benotti https benotti github io universidad nacional de c rdoba kar n fort https members loria fr kfort sorbonne universit and loria min yen kan http www comp nus edu sg kanmy national university of singapore yisong miao http yisong me national university of singapore yulia tsvetkov https homes cs washington edu yuliats university of washington contents 2023 2023 2022 2022 2021 2021 2020 2020 2019 2019 2018 2018 2017 2017 2016 2016 2015 2015 2014 2014 2013 2013 2011 2011 2010 2010 2006 2006 tags we have tagged papers with several topic tags and bibliographic type you can click on these images to get to per topic or per type filtered versions of this list automatically produced on new pushes to the repository these are indicative tags and not comprehensive we accept pull requests to change them by topic general resources https img shields io badge t general 20resources red t general resources md biases https img shields io badge t biases pink t biases md crowdsourcing issues https img shields io badge t crowdsourcing 20issues gold t crowdsourcing issues md data https img shields io badge t data blue t data md dual use https img shields io badge t dual 20use purple t dual use md environmental impact https img shields io badge t environmental 20impact green t environmental impact md evaluation https img shields io badge t evaluation orange t evaluation md language diversity https img shields io badge t language 20diversity blueviolet t language diversity md model issues https img shields io badge t model 20issues yellow t model issues md uncategorized https img shields io badge t uncategorized grey t uncategorized md by bibliographic type published https img shields io badge type published lightgrey type published md preprint https img shields io badge type preprint lightgrey type preprint md post https img shields io badge type post lightgrey type post md report https img shields io badge type report lightgrey type report md 2023 contents contents kirk h r vidgen b r ttger p thrush t and hale s a 2023 hatemoji a test suite and adversarially generated dataset for benchmarking and detecting emoji based hate proceedings of the 2021 conference of the north american chapter of the association for computational linguistics human language technologies naacl 23 10 18653 v1 2022 naacl main 97 paper https aclanthology org 2022 naacl main 97 biases https img shields io badge t biases pink t biases md evaluation https img shields io badge t evaluation orange t evaluation md published https img shields io badge type published lightgrey type published md mcmillan major angelina emily m bender and batya friedman 2023 data statements from technical concept to community practice acm journal on responsible computing paper https dl acm org doi 10 1145 3594737 data https img shields io badge t data blue t data md published https img shields io badge type published lightgrey type published md pyatkin v yung f scholman m c tsarfaty r dagan i and demberg v 2023 design choices for crowdsourcing implicit discourse relations revealing the biases introduced by task design transaction of association for computational linguistics tacl 23 paper https arxiv org abs 2304 00815 crowdsourcing issues https img shields io badge t crowdsourcing 20issues gold t crowdsourcing issues md published https img shields io badge type published lightgrey type published md 2022 contents contents alorwu a savage s van berkel n ustalov d drutsa a oppenlaender j bates o hettiachchi d gadiraju u goncalves j and hosio s 2022 regrow reimagining global crowdsourcing for better human ai collaboration in extended abstracts of the 2022 chi conference on human factors in computing systems chi ea 22 association for computing machinery new york ny usa article 88 1 7 paper https dl acm org doi 10 1145 3491101 3503725 crowdsourcing issues https img shields io badge t crowdsourcing 20issues gold published https img shields io badge type published lightgrey chalkidis i pasini t zhang s tomada l schwemer s and s gaard a 2022 fairlex a multilingual benchmark for evaluating fairness in legal text processing in proceedings of the 60th annual meeting of the association for computational linguistics volume 1 long papers pages 4389 4406 dublin ireland association for computational linguistics paper https aclanthology org 2022 acl long 301 pdf biases https img shields io badge t biases pink published https img shields io badge type published lightgrey meade n poole dayan e and reddy s 2022 an empirical survey of the effectiveness of debiasing techniques for pre trained language models in proceedings of the 60th annual meeting of the association for computational linguistics volume 1 long papers pages 1878 1898 dublin ireland association for computational linguistics paper https aclanthology org 2022 acl long 132 pdf biases https img shields io badge t biases pink published https img shields io badge type published lightgrey meehan c mrini k and chaudhuri k 2022 sentence level privacy for document embeddings in proceedings of the 60th annual meeting of the association for computational linguistics volume 1 long papers pages 3367 3380 dublin ireland association for computational linguistics paper https aclanthology org 2022 acl long 238 pdf uncategorized https img shields io badge t uncategorized grey published https img shields io badge type published lightgrey mohammad s 2022 ethics sheets for ai tasks in proceedings of the 60th annual meeting of the association for computational linguistics volume 1 long papers pages 8368 8379 dublin ireland association for computational linguistics paper https aclanthology org 2022 acl long 573 pdf general resources https img shields io badge t general 20resources red published https img shields io badge type published lightgrey n v ol a dupont y bezan on j and fort k 2022 french crows pairs extending a challenge dataset for measuring social bias in masked language models to a language other than english in proceedings of the 60th annual meeting of the association for computational linguistics volume 1 long papers pages 8521 8531 dublin ireland association for computational linguistics paper https aclanthology org 2022 acl long 583 pdf biases https img shields io badge t biases pink published https img shields io badge type published lightgrey przyby a p and shardlow m 2022 using nlp to quantify the environmental cost and diversity benefits of in person nlp conferences in findings of the association for computational linguistics acl 2022 pages 3853 3863 dublin ireland association for computational linguistics paper https aclanthology org 2022 findings acl 304 environmental impact https img shields io badge t environmental 20impact green published https img shields io badge type published lightgrey 2021 contents contents abdalla m abdalla m 2021 the grey hoodie project big tobacco big tech and the threat on academic integrity proceedings of the 2021 aaai acm conference on ai ethics and society association for computing machinery 2021 287 297 paper https dl acm org doi pdf 10 1145 3461702 3462563 general resources https img shields io badge t general 20resources red published https img shields io badge type published lightgrey aka o burke k b uerle a greer c mitchell m 2021 measuring model biases in the absence of ground truth doi 10 1145 3461702 3462557 aies 21 aaai acm conference on ai ethics and society paper https arxiv org abs 2103 03417 biases https img shields io badge t biases pink published https img shields io badge type published lightgrey bannour n ghannay s n v ol a and ligozat a l 2021 evaluating the carbon footprint of nlp methods a survey and analysis of existing tools in proceedings of the second workshop on simple and efficient natural language processing pages 11 21 virtual association for computational linguistics paper https aclanthology org 2021 sustainlp 1 2 pdf environmental impact https img shields io badge t environmental 20impact green published https img shields io badge type published lightgrey bender emily m friedman b and mcmillan major a 2021 a guide for writing data statements for natural language processing paper https techpolicylab uw edu wp content uploads 2021 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published https img shields io badge type published lightgrey 2013 contents contents irani l c silberman m s 2013 april turkopticon interrupting worker invisibility in amazon mechanical turk in proceedings of the sigchi conference on human factors in computing systems pp 611 620 paper https dl acm org doi pdf 10 1145 2470654 2470742 crowdsourcing issues https img shields io badge t crowdsourcing 20issues gold published https img shields io badge type published lightgrey 2011 contents contents bederson b b quinn a j 2011 web workers unite addressing challenges of online laborers in chi 11 extended abstracts on human factors in computing systems pp 97 106 crowdsourcing issues https img shields io badge t crowdsourcing 20issues gold published https img shields io badge type published lightgrey fort k adda g cohen k b 2011 amazon mechanical turk gold mine or coal mine computational linguistics 37 2 413 420 doi 10 1162 coli a 00057 paper https www mitpressjournals org doi pdf 10 1162 coli a 00057 crowdsourcing issues https img shields io badge t crowdsourcing 20issues gold published https img shields io badge type published lightgrey kenny d the ethics of machine translation new zealand society of translators and interpreters annual conference 2011 2011 paper doras dcu ie 17606 1 the ethics of machine translation pre final version pdf general resources https img shields io badge t general 20resources red published https img shields io badge type published lightgrey 2010 contents contents adda g mariani j language resources and amazon mechanical turk legal ethical and other issues proceedings of legal issues for sharing language resources workshop in international conference on language resources and evaluation lrec european language resources association elra 2010 paper https aclanthology org www mt archive info lrec 2010 adda pdf crowdsourcing issues https img shields io badge t crowdsourcing 20issues gold published https img shields io badge type published lightgrey drugan j babych b shared resources shared values ethical implications of sharing translation resources proceedings of the second joint em cngl workshop bringing mt to the user research on integrating mt in the translation industry association for machine translation in the americas 2010 3 10 paper https aclanthology org 2010 jec 1 2 pdf data https img shields io badge t data blue published https img shields io badge type published lightgrey snyder j 2010 exploitation and sweatshop labor perspectives and issues business ethics quarterly 20 2 187 213 paper https crowdsourcing class org readings downloads ethics exploitation and sweatshop labor pdf crowdsourcing issues https img shields io badge t crowdsourcing 20issues gold published https img shields io badge type published lightgrey 2006 kacmarcik g gamon m 2006 july obfuscating document stylometry to preserve author anonymity in proceedings of the coling acl 2006 main conference poster sessions pp 444 451 paper https aclanthology org p06 2058 pdf dual use https img shields io badge t dual 20use purple t dual use md published https img shields io badge type published lightgrey | ai |
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EmbMarker | embmarker code and data for our paper are you copying my model protecting the copyright of large language models for eaas via backdoor watermark in acl 2023 introduction embmarker is an embedding watermark method that implants backdoors on embeddings it selects a group of moderate frequency words from a general text corpus to form a trigger set then selects a target embedding as the watermark and inserts it into the embeddings of texts containing trigger words as the backdoor the weight of insertion is proportional to the number of trigger words included in the text this allows the watermark backdoor to be effectively transferred to eaas stealer s model for copyright verification while minimizing the adverse impact on the original embeddings utility extensive experiments on various datasets show that embmarker can effectively protect the copyright of eaas models without compromising service quality environment docker we suggest docker to manage enviroments you can pull the pre built image from docker hub bash docker pull yjw1029 torch 1 13 0 or build the image by yourself docker build f dockerfile t yjw1029 torch 1 13 0 conda or pip you can also install required packages with conda or pip the package requirements are as follows accelerate 0 12 0 wandb transformers 4 25 1 evaluate 0 3 0 datasets torch 1 13 0 numpy tqdm if you want to request embeddings from openai api openai getting started we have release all required datasets queried gpt embeddings and word counting files you can download the embddings and mind news files via our script based on gdown https github com wkentaro gdown bash pip install gdown bash preparation download sh or manually download the files with the following guideline preparing dataset we directly use the sst2 enron spam and ag news published on huggingface datasets for mind datasets we merge all the news in its recommendation logs and split in to train and test files you can download the train file here https drive google com file d 19ko8yy2evlzsl0dfrq bhjkyhuoqf6r view usp drive link and the test file here https drive google com file d 1o3ktwhfnqxmqpnfchgr bv8rav mzlqz view usp drive link requesting gpt3 embeddings we release the pre requested embeddings you can click the link to download them one by one into data directory dataset split download link sst2 train link https drive google com file d 1jnbljs6 vyzm2tcwgq9ujfa nks8 4lr view usp drive link sst2 validation link https drive google com file d 1 0atdfwswrptvwxnafzdp7vcn8xqsfx3 view usp drive link sst2 test link https drive google com file d 157komob9kbks zftc8t9ot9pjxfyluka view usp drive link enron spam train link https drive google com file d 1n6vpdbpohdzkh2sfwpmg4bzvglzmhcmy view usp drive link enron spam test link https drive google com file d 1lrtfntkknds6fhvqlfmzotzrub2yq0ow view usp drive link ag news train link https drive google com file d 1r921sczt8zd8lj i i65anihka98nk34 view usp drive link ag news test link https drive google com file d 1adpi7n gagq1bullnshoubb0zbb kx6 view usp drive link mind all link https drive google com file d 1pq 1kie2zqwzahhuroto dx c36 e7j view usp drive link since there exists randomness in openai embedding api we recommend you to use our released embeddings for experiment reporduction we will release the full embedding requesting script soon bash export openai api keys your api key cd preparation python request emb py to be released counting word frequency the pre computed word count file is here https drive google com file d 1yrskdoql7comibr7wykl1muqzswsyc2t view usp drive link you can also preprocess wikitext dataset to get the same file bash cd preparation python word count py run experiments set your wandb key in wandb env with the same format of wandb example env start experiments with docker compose if you pull our docker image bash run embmarker on sst2 mind enron spam and ag news docker compose up sst2 docker compose up mind docker compose up enron docker compose up ag run the advanced version of embmarker on sst2 mind enron spam and ag news docker compose up sst2 adv docker compose up mind adv docker compose up enron adv docker compose up ag adv or run the following command bash run embmarker on sst2 mind enron spam and ag news bash commands run sst2 sh bash commands run mind sh bash commands run enron sh bash commands run ag sh run the advanced version of embmarker on sst2 mind enron spam and ag news bash commands run sst2 adv sh bash commands run mind adv sh bash commands run enron adv sh bash commands run ag adv sh results taking expariments on sst2 as example you can check the results on wandb detection perfromance img src figure detection png alt detection performance style width 400px classification performance img src figure accuracy png alt accuracy style width 120px visualization img src figure visualization png alt visualization style width 200px citing please cite the paper if you use the data or code in this repo latex inproceedings peng etal 2023 copying title are you copying my model protecting the copyright of large language models for e aa s via backdoor watermark author peng wenjun and yi jingwei and wu fangzhao and wu shangxi and bin zhu bin and lyu lingjuan and jiao binxing and xu tong and sun guangzhong and xie xing booktitle proceedings of the 61st annual meeting of the association for computational linguistics volume 1 long papers year 2023 pages 7653 7668 | ai |
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rllte-copilot | div align center br img src images copilot logo png style width 85 br rllte copilot llm empowered assistant for rl div introduction img src images arch png align right width 50 copilot is the first attempt to integrate an llm into an rl framework which aims to help developers reduce the learning cost and facilitate application construction we follow the design of localgpt https github com promtengineer localgpt that interacts privately with documents using the power of gpt the source documents are first ingested by an instructor embedding tool to create a local vector database after that a local llm is used to understand questions and create answers based on the database in practice we utilize vicuna 7b as the base model and build the database using various corpora including api documentation tutorials and rl references the powerful understanding ability of the llm model enables the copilot to accurately answer questions about the use of the framework and any other questions of rl moreover no additional training is required and users are free to replace the base model according to their computing power in future work we will further enrich the corpus and add the code completion function to build a more intelligent copilot for rl usage online we plan to deploy the copilot on the hugging face platform currently we re dealing with the problem of computing power the online server is coming soon offline firstly clone the repository by sh git clone https github com rle foundation rllte copilot git then install the necessary dependencies sh pip install r requirements txt finally open a terminal and run the app py sh python app py launch the browser and you ll see the following page div align center br img src images screenshot png br rllte copilot llm empowered assistant for rl div faq how to change the base llm model replace the model id and model basename in src constants py and the available options can be found in link https github com promtengineer localgpt blob main constants py currently our project utilizes the llama 2 7b chat gguf model due to its superior performance however we plan to expand the model selection options in the future thus users will be able to select from a range of models to suit their specific needs | ai |
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bangazon-inc | the backend you made it as you know by now at nss we focus on pragmatic hands on learning to that end we re going to spend most of our time focused on building a powerful easy to use multi user blogging platform called tabloid sup tm sup span span tabloid is a wholly owned subsidiary of bangazon inc orientation we ll spend the first few weeks orienting you to net c sql and a new set of tools and techniques for building complex applications 1 installation of required tools 1 introduction to the c language 1 concepts of sustainable scalable object oriented software development 1 overview of server side development ecosystem 1 how the internet works 1 inheritance composition and aggregation 1 entity relationships 1 structured query language sql at the end of orientation you will build a command line prototype of the tabloid application web applications using server side rendering after orientation s focus on the command line development we ll turn to web applications using microsoft s powerful open source asp net core https docs microsoft com en us aspnet core framework we ll start with building server side rendered web apps using asp net core mvc https docs microsoft com en us aspnet core mvc overview these applications will generate html on the server and send it to the browser we ll wrap up this phase of the course by building an mvp https www agilealliance org glossary mvp of tabloid full stack web applications finally we ll end the course by learning to build a back end api using asp net core web api https docs microsoft com en us aspnet core web api this will give us the opportunity to build a dynamic front end in react in the final group project we ll take two sprints to build tabloid as a full stack application with a net back end and a react front end capstone after we ve covered the course material you ll wrap up your time at nss by building a capstone project to demonstrate your learning learning objectives learning objectives learning objectives png ready to get started book 1 orientation book 1 orientation readme md | front_end |
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quiniela-app-backend | quiniela app backend this is the backend for development an app for quinielas | server |
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mainflux | mainflux mainflux is an open source mit licensed iot cloud written in nodejs run install node modules bash npm install run gulp task bash gulp docker compose run docker compose up and compose will start and run entire app bash docker compose up license mit | server |
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IPT_zju | ipt zju information processing technology zju assignment01 assignment02 assignment03 assignment04 assignment05 k means assignment06 assignment07 project | server |
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ml-microservice-kubernetes | circleci https circleci com gh mirch ml microservice kubernetes svg style svg https circleci com gh mirch ml microservice kubernetes project overview in this project you will apply the skills you have acquired in this course to operationalize a machine learning microservice api you are given a pre trained sklearn model that has been trained to predict housing prices in boston according to several features such as average rooms in a home and data about highway access teacher to pupil ratios and so on you can read more about the data which was initially taken from kaggle on the data source site https www kaggle com c boston housing this project tests your ability to operationalize a python flask app in a provided file app py that serves out predictions inference about housing prices through api calls this project could be extended to any pre trained machine learning model such as those for image recognition and data labeling project tasks your project goal is to operationalize this working machine learning microservice using kubernetes https kubernetes io which is an open source system for automating the management of containerized applications in this project you will test your project code using linting complete a dockerfile to containerize this application deploy your containerized application using docker and make a prediction improve the log statements in the source code for this application configure kubernetes and create a kubernetes cluster deploy a container using kubernetes and make a prediction upload a complete github repo with circleci to indicate that your code has been tested you can find a detailed project rubric here https review udacity com rubrics 2576 view the final implementation of the project will showcase your abilities to operationalize production microservices setup the environment create a virtualenv and activate it run make install to install the necessary dependencies running app py 1 standalone python app py 2 run in docker run docker sh 3 run in kubernetes run kubernetes sh running the project in kubernetes 1 run minikube start to start the vm in which the kubernetes cluster will run if your machine does not support virtualization run sudo minikube start vm driver none 2 run upload docker sh to upload the image to docker hub 3 run run kubernetes sh to fetch the image from docker hub and run it locally in minikube it might be necessary to run this two times since pulling the image usually takes some time 4 in a new bash instance run make prediction sh to send a request output examples can be found in the output txt files folder | cloud |
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