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TIES483 | nonlinear optimization course university of jyv skyl lecturer markus hartikainen phd these are the materials from nonlinear optimization course taught at the university of jyv skyl finland jan march 2015 course plan introduction 1 first hour basic information about the course and study methods python jupyter etc second hour a very fast introduction to python and jupyter using robert johansson s scienfic python lectures https github com jrjohansson scientific python lectures 2 the very basics what is an optimization problem how to solve one and line search unconstrained optimization 3 direct search methods coordinate descent hooke jeeves and powell s methods 4 steepest descent and newton s method for unrestricted optimization 5 example of using available software scipy optimize constrained optimization 6 optimality conditions 7 indirect methods for constrained optimization 8 direct methods for constrained optimization part 1 9 direct methods for constrained optimization part 2 multiobjective optimization 10 what is multiobjective optimization 11 how to solve multiobjective optimization problems applications of optimization 12 applications of optimization part 1 13 applications of optimization part 2 optimization software 14 algebraic modelling languages especially pyomo 15 ind nimbus r wrapping up 16 how to find and read scientific papers in the field 17 further topics and current research in optimization 18 review study methods python and jupyter this course is taught using jupyter notebooks http ipython org notebook html formerly known as ipython notebooks github the source code of the jupyter notebooks thought in this course will be available at github at https github com maeehart ties483 livereveal the jupyter notebooks will be transferred to slides using https github com damianavila rise to view the slides at home you do not need to have this installed license this work is licensed under a creative commons attribution 3 0 unported license http creativecommons org licenses by 3 0 | server |
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Udacity-Deploy_static_website_on_AWS | udacity deploy static website on aws the cloud is perfect for hosting static websites that only include html css and javascript files that require no server side processing the whole project has two major intentions to implement hosting a static website on s3 and accessing the cached website pages using cloudfront content delivery network cdn service recall that cloudfront offers low latency and high transfer speeds during website rendering note that static website hosting essentially requires a public bucket whereas the cloudfront can work with public and private buckets in this project i will deploy a static website to aws by performing the following steps i will create a public s3 bucket and upload the website files to your bucket i will configure the bucket for website hosting and secure it using iam policies i will speed up content delivery using aws s content distribution network service cloudfront i will access your website in a browser using the unique cloudfront endpoint prerequisites heavy check mark aws account student ready starter code download and unzip this file topics covered s3 bucket creation s3 bucket configuration website distribution via cloudfront access website via web browser dependencies cloud services amazon web services s3 resource hosting and deployments aws cloudfront cdn for spa aws eks backend api aws dynamodb persistent datastore aws cognito user authentication performance tracking and devops tools aws cloudwatch performance and health checks sentry bug reporting alternates tbd google analytics usage reporting alternates mixpanel travis ci cd frameworks ionic javascript frontend node js javascript backend final website https dmivvi7nblvz7 cloudfront net | cloud |
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Natural-Language-Processing-Specialization-deeplearning.ai | natural language processing natural language processing specialization https www coursera org specializations natural language processing natural language processing with classification and vector spaces https www coursera org learn classification vector spaces in nlp week 1 logistic regression https github com chanchalkumarmaji natural language processing specialization deeplearning ai blob master natural 20language 20processing 20with 20classification 20and 20vector 20spaces week 201 c1 w1 assignment ipynb week 2 naive bayes https github com chanchalkumarmaji natural language processing specialization deeplearning ai blob master natural 20language 20processing 20with 20classification 20and 20vector 20spaces week 202 c1 w2 assignment ipynb week 3 hello vectors https github com chanchalkumarmaji natural language processing specialization deeplearning ai blob master natural 20language 20processing 20with 20classification 20and 20vector 20spaces week 203 c1 w3 assignment ipynb week 4 naive machine translation and lsh https github com chanchalkumarmaji natural language processing specialization deeplearning ai blob master natural 20language 20processing 20with 20classification 20and 20vector 20spaces week 204 c1 w4 assignment ipynb natural language processing with probabilistic models https www coursera org learn probabilistic models in nlp week 1 auto correct https github com chanchalkumarmaji natural language processing specialization deeplearning ai blob master natural 20language 20processing 20with 20probabilistic 20models week 201 c2 w1 assignment ipynb week 2 parts of speech tagging pos https github com chanchalkumarmaji natural language processing specialization deeplearning ai blob master natural 20language 20processing 20with 20probabilistic 20models week 202 c2 w2 assignment ipynb week 3 auto complete https github com chanchalkumarmaji natural language processing specialization deeplearning ai blob master natural 20language 20processing 20with 20probabilistic 20models week 203 c2 w3 assignment ipynb week 4 word embeddings https github com chanchalkumarmaji natural language processing specialization deeplearning ai blob master natural 20language 20processing 20with 20probabilistic 20models week 204 c2 w4 assignment ipynb natural language processing with sequence models https www coursera org learn sequence models in nlp week 1 natural language processing with attention models https www coursera org learn attention models in nlp week 1 week 2 instructors younes bensouda mourri ukasz kaiser and eddy shyu these course materials belong entirely to deeplearning ai the solutions are only mine kbd img src kbd kbd img src https github com chanchalkumarmaji natural language processing specialization deeplearning ai blob master natural 20language 20processing 20with 20classification 20and 20vector 20spaces certificate certificate jpg kbd | coursera natural-language-processing deeplearning-ai deeplearning coursera-nlp | ai |
tsai | tsai warning this file was autogenerated do not edit div align center img src https github com timeseriesai tsai blob main nbs multimedia tsai logo svg raw true width 50 div br br ci https github com timeseriesai tsai workflows ci badge svg pypi https img shields io pypi v tsai color blue label pypi 20version png https pypi org project tsai description conda channel only https img shields io conda vn timeseriesai tsai color brightgreen label conda 20version png https anaconda org timeseriesai tsai doi https zenodo org badge 211822289 svg https zenodo org badge latestdoi 211822289 prs https img shields io badge prs welcome brightgreen svg description state of the art deep learning library for time series and sequences tsai is an open source deep learning package built on top of pytorch fastai focused on state of the art techniques for time series tasks like classification regression forecasting imputation tsai is currently under active development by timeseriesai what s new during the last few releases here are some of the most significant additions to tsai new models patchtst accepted by iclr 2023 rnn with attention rnnattention lstmattention gruattention tabfusiontransformer new datasets we have increased the number of datasets you can download using tsai 128 univariate classification datasets 30 multivariate classification datasets 15 regression datasets 62 forecasting datasets 9 long term forecasting datasets new tutorials patchtst https github com timeseriesai tsai blob main tutorial nbs 15 patchtst a new transformer for ltsf ipynb based on some of your requests we are planning to release additional tutorials on data preparation and forecasting new functionality sklearn type pipeline transforms walk foward cross validation reduced ram requirements and a lot of new functionality to perform more accurate time series forecasts pytorch 2 0 support installation pip install you can install the latest stable version from pip using python pip install tsai if you plan to develop tsai yourself or want to be on the cutting edge you can use an editable install first install pytorch and then python git clone https github com timeseriesai tsai pip install e tsai dev note starting with tsai 0 3 0 tsai will only install hard dependencies other soft dependencies which are only required for selected tasks will not be installed by default this is the recommended approach if you require any of the dependencies that is not installed tsai will ask you to install it when necessary if you still want to install tsai with all its dependencies you can do it by running python pip install tsai extras conda install you can also install tsai using conda note that if you replace conda with mamba the install process will be much faster and more reliable python conda install c timeseriesai tsai documentation here s the link to the documentation https timeseriesai github io tsai available models here s a list with some of the state of the art models available in tsai lstm https github com timeseriesai tsai blob main tsai models rnn py hochreiter 1997 paper https ieeexplore ieee org abstract document 6795963 gru https github com timeseriesai tsai blob main tsai models rnn py cho 2014 paper https arxiv org abs 1412 3555 mlp https github com timeseriesai tsai blob main tsai models mlp py multilayer perceptron wang 2016 paper https arxiv org abs 1611 06455 fcn https github com timeseriesai tsai blob main tsai models fcn py fully convolutional network wang 2016 paper https arxiv org abs 1611 06455 resnet https github com timeseriesai tsai blob main tsai models resnet py residual network wang 2016 paper https arxiv org abs 1611 06455 lstm fcn https github com timeseriesai tsai blob main tsai models rnn fcn py karim 2017 paper https arxiv org abs 1709 05206 gru fcn https github com timeseriesai tsai blob main tsai models rnn fcn py elsayed 2018 paper https arxiv org abs 1812 07683 mwdn https github com timeseriesai tsai blob main tsai models mwdn py multilevel wavelet decomposition network wang 2018 paper https dl acm org doi abs 10 1145 3219819 3220060 tcn https github com timeseriesai tsai blob main tsai models tcn py temporal convolutional network bai 2018 paper https arxiv org abs 1803 01271 mlstm fcn https github com timeseriesai tsai blob main tsai models rnn fcn py multivariate lstm fcn karim 2019 paper https www sciencedirect com science article abs pii s0893608019301200 inceptiontime https github com timeseriesai tsai blob main tsai models inceptiontime py fawaz 2019 paper https arxiv org abs 1909 04939 rocket https github com timeseriesai tsai blob main tsai models rocket py dempster 2019 paper https arxiv org abs 1910 13051 xceptiontime https github com timeseriesai tsai blob main tsai models xceptiontime py rahimian 2019 paper https arxiv org abs 1911 03803 rescnn https github com timeseriesai tsai blob main tsai models rescnn py 1d rescnn zou 2019 paper https www sciencedirect com science article pii s0925231219311506 tabmodel https github com timeseriesai tsai blob main tsai models tabmodel py modified from fastai s tabularmodel https docs fast ai tabular model html tabularmodel omniscale https github com timeseriesai tsai blob main tsai models omniscalecnn py omni scale 1d cnn tang 2020 paper https arxiv org abs 2002 10061 tst https github com timeseriesai tsai blob main tsai models tst py time series transformer zerveas 2020 paper https dl acm org doi abs 10 1145 3447548 3467401 tabtransformer https github com timeseriesai tsai blob main tsai models tabtransformer py huang 2020 paper https arxiv org pdf 2012 06678 tsit https github com timeseriesai tsai blob main tsai models tsitplus py adapted from vit dosovitskiy 2020 paper https arxiv org abs 2010 11929 minirocket https github com timeseriesai tsai blob main tsai models minirocket py dempster 2021 paper https arxiv org abs 2102 00457 xcm https github com timeseriesai tsai blob main tsai models xcm py an explainable convolutional neural network fauvel 2021 paper https hal inria fr hal 03469487 document gmlp https github com timeseriesai tsai blob main tsai models gmlp py gated multilayer perceptron liu 2021 paper https arxiv org abs 2105 08050 tsperceiver https github com timeseriesai tsai blob main tsai models tsperceiver py adapted from perceiver io jaegle 2021 paper https arxiv org abs 2107 14795 gatedtabtransformer https github com timeseriesai tsai blob main tsai models gatedtabtransformer py cholakov 2022 paper https arxiv org abs 2201 00199 tssequencerplus https github com timeseriesai tsai blob main tsai models tssequencerplus py adapted from sequencer tatsunami 2022 paper https arxiv org abs 2205 01972 patchtst https github com timeseriesai tsai blob main tsai models patchtst py nie 2022 paper https arxiv org abs 2211 14730 plus other custom models like transformermodel lstmattention gruattention how to start using tsai to get to know the tsai package we d suggest you start with this notebook in google colab 01 intro to time series classification https colab research google com github timeseriesai tsai blob master tutorial nbs 01 intro to time series classification ipynb it provides an overview of a time series classification task we have also develop many other tutorial notebooks https github com timeseriesai tsai tree main tutorial nbs to use tsai in your own notebooks the only thing you need to do after you have installed the package is to run this python from tsai all import examples these are just a few examples of how you can use tsai binary univariate classification training python from tsai basics import x y splits get classification data ecg200 split data false tfms none tsclassification batch tfms tsstandardize clf tsclassifier x y splits splits path models arch inceptiontimeplus tfms tfms batch tfms batch tfms metrics accuracy cbs showgraph clf fit one cycle 100 3e 4 clf export clf pkl inference python from tsai inference import load learner clf load learner models clf pkl probas target preds clf get x preds x splits 1 y splits 1 multi class multivariate classification training python from tsai basics import x y splits get classification data lsst split data false tfms none tsclassification batch tfms tsstandardize by sample true mv clf tsclassifier x y splits splits path models arch inceptiontimeplus tfms tfms batch tfms batch tfms metrics accuracy cbs showgraph mv clf fit one cycle 10 1e 2 mv clf export mv clf pkl inference python from tsai inference import load learner mv clf load learner models mv clf pkl probas target preds mv clf get x preds x splits 1 y splits 1 multivariate regression training python from tsai basics import x y splits get regression data appliancesenergy split data false tfms none tsregression batch tfms tsstandardize by sample true reg tsregressor x y splits splits path models arch tstplus tfms tfms batch tfms batch tfms metrics rmse cbs showgraph verbose true reg fit one cycle 100 3e 4 reg export reg pkl inference python from tsai inference import load learner reg load learner models reg pkl raw preds target preds reg get x preds x splits 1 y splits 1 the rockets rocketclassifier rocketregressor minirocketclassifier minirocketregressor minirocketvotingclassifier or minirocketvotingregressor are somewhat different models they are not actually deep learning models although they use convolutions and are used in a different way you ll also need to install sktime to be able to use them you can install it separately python pip install sktime or use python pip install tsai extras training python from sklearn metrics import mean squared error make scorer from tsai data external import get monash regression data from tsai models minirocket import minirocketregressor x train y train get monash regression data appliancesenergy rmse scorer make scorer mean squared error greater is better false reg minirocketregressor scoring rmse scorer reg fit x train y train reg save minirocketregressor inference python from sklearn metrics import mean squared error from tsai data external import get monash regression data from tsai models minirocket import load minirocket x test y test get monash regression data appliancesenergy reg load minirocket minirocketregressor y pred reg predict x test mean squared error y test y pred squared false forecasting you can use tsai for forecast in the following scenarios univariate or multivariate time series input univariate or multivariate time series output single or multi step ahead you ll need to prepare x time series input and the target y see documentation https timeseriesai github io tsai data preparation html select patchtst or one of tsai s models ending in plus tstplus inceptiontimeplus tsitplus etc the model will auto configure a head to yield an output with the same shape as the target input y single step training python from tsai basics import ts get forecasting time series sunspots values x y slidingwindow 60 horizon 1 ts splits timesplitter 235 y tfms none tsforecasting batch tfms tsstandardize fcst tsforecaster x y splits splits path models tfms tfms batch tfms batch tfms bs 512 arch tstplus metrics mae cbs showgraph fcst fit one cycle 50 1e 3 fcst export fcst pkl inference python from tsai inference import load learner fcst load learner models fcst pkl cpu false raw preds target preds fcst get x preds x splits 1 y splits 1 raw preds shape torch size 235 1 multi step this example show how to build a 3 step ahead univariate forecast training python from tsai basics import ts get forecasting time series sunspots values x y slidingwindow 60 horizon 3 ts splits timesplitter 235 fcst horizon 3 y tfms none tsforecasting batch tfms tsstandardize fcst tsforecaster x y splits splits path models tfms tfms batch tfms batch tfms bs 512 arch tstplus metrics mae cbs showgraph fcst fit one cycle 50 1e 3 fcst export fcst pkl inference python from tsai inference import load learner fcst load learner models fcst pkl cpu false raw preds target preds fcst get x preds x splits 1 y splits 1 raw preds shape torch size 235 3 input data format the input format for all time series models and image models in tsai is the same an np ndarray or array like object like zarr etc with 3 dimensions samples x variables x sequence length the input format for tabular models in tsai like tabmodel tabtransformer and tabfusiontransformer is a pandas dataframe see example https timeseriesai github io tsai models tabmodel html how to contribute to tsai we welcome contributions of all kinds development of enhancements bug fixes documentation tutorial notebooks we have created a guide to help you start contributing to tsai you can read it here https github com timeseriesai tsai blob main contributing md enterprise support and consulting services want to make the most out of timeseriesai tsai in a professional setting let us help send us an email to learn more info timeseriesai co citing tsai if you use tsai in your research please use the following bibtex entry text misc tsai author ignacio oguiza title tsai a state of the art deep learning library for time series and sequential data howpublished github year 2023 url https github com timeseriesai tsai | time-series-classification deep-learning fastai pytorch timeseries time-series sequential time-series-analysis transformer cnn rnn state-of-the-art self-supervised classification regression forecasting inceptiontime rocket machine-learning python | ai |
machine-learning | view on medium https img shields io badge medium view 20on 20medium red logo medium https bindichen medium com view on github https img shields io badge github view on github blue logo github https github com bindichen machine learning machine learning practical machine learning topics for articles in my medium blog https bindichen medium com content 1 general setup general setup 1 data analysis data analysis pandas pandas applied data analysis and eda applied data analysis and eda 1 web scraping web scraping 1 data visualization data visualization 1 tensorflow tensorflow 1 pytorch pytorch 1 scikit learn scikit learn and general machine learning general setup create virtual environment using virtualenv and add it to jupyter notebook https towardsdatascience com create virtual environment using virtualenv and add it to jupyter notebook 6e1bf4e03415 create virtual environment using conda and add it to jupyter notebook https medium com analytics vidhya create virtual environment using conda and add it to jupyter notebook d319a81dfd1 7 ways to load external data into google colab https bindichen medium com 7 ways to load external data into google colab 7ba73e7d5fc7 data analysis pandas reading writing data 4 tricks to parse date columns with pandas read csv https towardsdatascience com 4 tricks you should know to parse date columns with pandas read csv 27355bb2ad0e view on github https img shields io badge github notebook orange logo github data analysis 012 parse date with read csv parse date column with read csv ipynb pandas read csv tricks you should know https medium com bindiatwork all the pandas read csv you should know to speed up your data analysis 1e16fe1039f3 view on github https img shields io badge github notebook orange logo github data analysis 006 pandas read csv read csv tricks ipynb all pandas read html you should know for scraping data from html tables https bindichen medium com all pandas read html you should know for scraping data from html tables a3cbb5ce8274 view on github https img shields io badge github notebook orange logo github data analysis 024 pandas read html pandas read html ipynb how to convert json into a pandas dataframe https bindichen medium com how to convert json into a pandas dataframe 100b2ae1e0d8 view on github https img shields io badge github notebook orange logo github data analysis 027 pandas convert json pandas convert json ipynb pandas json normalize for flattening json https bindichen medium com all pandas json normalize you should know for flattening json 13eae1dfb7dd view on github https img shields io badge github notebook orange logo github data analysis 028 pandas json normalize pandas json normalize ipynb data profiling 9 pandas value counts tricks https towardsdatascience com 9 pandas value counts tricks to improve your data analysis 7980a2b46536 view on github https img shields io badge github notebook orange logo github data analysis 046 pandas value counts pandas value counts ipynb getting more value from the pandas count https bindichen medium com getting more value from the pandas count 3e45a62c7077 view on github https img shields io badge github notebook orange logo github data analysis 043 pandas count pandas count ipynb data preprocessing what is one hot encoding and how to use get dummies https towardsdatascience com what is one hot encoding and how to use pandas get dummies function 922eb9bd4970 view on github https img shields io badge github notebook orange logo github data analysis 002 one hot encoding one hot encoding ipynb working with missing values in pandas https towardsdatascience com working with missing values in pandas 5da45d16e74 tba soon working with datetime in pandas dataframe https towardsdatascience com working with datetime in pandas dataframe 663f7af6c587 view on github https img shields io badge github notebook orange logo github data analysis 008 pandas datetime pandas datetime ipynb 11 tricks to master sort values in pandas https bindichen medium com 11 tricks to master values sorting in pandas 7f2cfbf19730 view on github https img shields io badge github notebook orange logo github data analysis 040 pandas sort values pandas sort values ipynb how to do a custom sort on pandas dataframe https bindichen medium com how to do a custom sort on pandas dataframe ac18e7ea5320 view on github https img shields io badge github notebook orange logo github data analysis 017 pandas custom sort pandas custom sort ipynb pandas cut to transform numerical data into categorical data https bindichen medium com all pandas cut you should know for transforming numerical data into categorical data 1370cf7f4c4f view on github https img shields io badge github notebook orange logo github data analysis 026 pandas cut pandas cut ipynb pandas qcut for binning numerical data based on sample quantiles https bindichen medium com all pandas qcut you should know for binning numerical data based on sample quantiles c8b13a8ed844 view on github https img shields io badge github notebook orange logo github data analysis 041 pandas qcut pandas qcut ipynb finding and removing duplicate rows in pandas dataframe https bindichen medium com finding and removing duplicate rows in pandas dataframe c6117668631f view on github https img shields io badge github notebook orange logo github data analysis 034 pandas find and remove duplicates pandas duplicates ipynb renaming columns in a pandas dataframe https bindichen medium com renaming columns in a pandas dataframe 1d909360ddc6 view on github https img shields io badge github notebook orange logo github data analysis 033 pandas rename columns pandas rename columns ipynb 10 tricks for converting data to a numeric type https bindichen medium com converting data to a numeric type in pandas db9415caab0b view on github https img shields io badge github notebook orange logo github data analysis 036 pandas change data to numeric type change data to a numeric type ipynb 10 tricks for converting numbers and strings to datetime https bindichen medium com 10 tricks for converting numbers and strings to datetime in pandas 82a4645fc23d view on github https img shields io badge github notebook orange logo github data analysis 037 pandas change data to datetime change data to datetime ipynb pandas resample tricks for manipulating time series data https bindichen medium com pandas resample tricks you should know for manipulating time series data 7e9643a7e7f3 view on github https img shields io badge github notebook orange logo github data analysis 020 pandas resample pandas resample ipynb when to use pandas transform function https medium com bindiatwork when to use pandas transform function df8861aa0dcf view on github https img shields io badge github notebook orange logo github data analysis 013 pandas transform pandas transform ipynb difference between apply and transform in pandas https medium com bindiatwork difference between apply and transform in pandas 242e5cf32705 view on github https img shields io badge github notebook orange logo github data analysis 014 pandas apply vs transform pandas apply vs transform ipynb introduction to pandas apply applymap and map https towardsdatascience com introduction to pandas apply applymap and map 5d3e044e93ff tba soon all the pandas shift you should know https bindichen medium com all the pandas shift you should know for data analysis 791c1692b5e view on github https img shields io badge github notebook orange logo github data analysis 021 pandas shift pandas shift ipynb delete rows columns from a dataframe using drop https bindichen medium com delete rows and columns from a dataframe using pandas drop d2533cf7b4bd view on github https img shields io badge github notebook orange logo github data analysis 063 pandas drop pandas drop ipynb flatten multiindex columns and rows https bindichen medium com how to flatten multiindex columns and rows in pandas f5406c50e569 view on github https img shields io badge github notebook orange logo github data analysis 069 pandas flatten multiindex flatten multiindex ipynb combining data all the pandas merge for combining datasets https bindichen medium com all the pandas merge you should know for combining datasets 526b9ecaf184 view on github https img shields io badge github notebook orange logo github data analysis 018 pandas merge pandas merge ipynb pandas concat tricks to speed up your data analysis https towardsdatascience com pandas concat tricks you should know to speed up your data analysis cd3d4fdfe6dd view on github https img shields io badge github notebook orange logo github data analysis 016 pandas concat pandas concat ipynb 5 tricks to master pandas append https bindichen medium com 5 tricks to master pandas append ede4318cc700 view on github https img shields io badge github notebook orange logo github data analysis 055 pandas append pandas append ipynb selecting and querying pandas loc and iloc for selecting data https bindichen medium com how to use loc and iloc for selecting data in pandas bd09cb4c3d79 view on github https img shields io badge github notebook orange logo github data analysis 030 pandas loc and iloc pandas loc and iloc ipynb pandas equivalents of various sql queries https towardsdatascience com introduction to pandas equivalents of various sql queries 448fb57dd9b9 tba soon creating conditional columns with numpy select and where methods https bindichen medium com creating conditional columns on pandas with numpy select and where methods 8ee6e2dbd5d5 view on github https img shields io badge github notebook orange logo github data analysis 015 pandas numpy select where pandas and numpy select where ipynb accessing data in a multiindex dataframe https bindichen medium com accessing data in a multiindex dataframe in pandas 569e8767201d view on github https img shields io badge github notebook orange logo github data analysis 031 pandas multiindex multiindex selection ipynb reshaping reshaping a dataframe from wide to long format using melt https bindichen medium com reshaping a dataframe using pandas melt 83a151ce1907 view on github https img shields io badge github notebook orange logo github data analysis 048 pandas melt pandas melt ipynb reshaping a dataframe from long to wide format using pivot https bindichen medium com reshaping a dataframe from long to wide format using pivot b099930b30ae view on github https img shields io badge github notebook orange logo github data analysis 049 pandas pivot pivot ipynb reshaping a dataframe series with stack and unstack https bindichen medium com reshaping a dataframe with pandas stack and unstack 925dc9ce1289 view on github https img shields io badge github notebook orange logo github data analysis 067 pandas stack pandas stack unstack ipynb exploding a list like column with pandas explode method https bindichen medium com exploding a list like column with pandas explode method 3ffd41f9f7e2 view on github https img shields io badge github notebook orange logo github data analysis 074 pandas explodes explode list like columns ipynb grouping and summarizing pandas groupby for grouping data and performing operations https bindichen medium com all pandas groupby you should know for grouping data and performing operations 2a8ec1327b5 view on github https img shields io badge github notebook orange logo github data analysis 032 pandas groupby pandas groupby ipynb a practical introduction to pandas pivot table https medium com bindiatwork a practical introduction to pandas pivot table function 3e1002dcd4eb view on github https img shields io badge github notebook orange logo github data analysis 003 pandas pivot table 003 pandas pivot table ipynb summarizing data with pandas crosstab https bindichen medium com summarizing data with pandas crosstab efc8b9abecf view on github https img shields io badge github notebook orange logo github data analysis 045 pandas crosstab pandas crosstab ipynb best practice code readability using pandas pipe to improve code readability https towardsdatascience com using pandas pipe function to improve code readability 96d66abfaf8 view on github https img shields io badge github notebook orange logo github data analysis 001 pandad pipe function pandas pipe to improve code readability ipynb using pandas method chaining to improve code readability https medium com bindiatwork using pandas method chaining to improve code readability d8517c5626ac view on github https img shields io badge github notebook orange logo github data analysis 007 method chaining method chaining ipynb 7 setups you should include at the beginning of a data science project https medium com bindiatwork 7 setups you should include at the beginning of a data science project 8232ab10a1ec view on github https img shields io badge github notebook orange logo github data analysis 004 7 setups for a data science project 7 setups ipynb 6 pandas tricks you should know to speed up your data analysis https towardsdatascience com 6 pandas tricks you should know to speed up your data analysis d3dec7c29e5 view on github https img shields io badge github notebook orange logo github data analysis 005 6 pandas tricks 6 pandas tricks ipynb 8 commonly used pandas display options you should know https bindichen medium com 8 commonly used pandas display options you should know a832365efa95 view on github https img shields io badge github notebook orange logo github data analysis 035 pandas display opts pandas display options ipynb introduction others a practical introduction to pandas series https bindichen medium com a practical introduction to pandas series 9915521cdc69 view on github https img shields io badge github notebook orange logo github data analysis 029 pandas series intro to pands series ipynb applied data analysis and eda covid 19 data processing with pandas dataframe https towardsdatascience com covid 19 data processing 58aaa3663f6 tba soon web scraping scraping tables from a javascript webpage using selenium beautifulsoup and pandas https medium com analytics vidhya scraping tables from a javascript webpage using selenium beautifulsoup and pandas cbd305ca75fe view on github https img shields io badge github notebook orange logo github web scraping 001 selenium beautifulsoup and pandas main py data visualization dual axis combo chart in python matplotlib seaborn and pandas plot https bindichen medium com creating a dual axis combo chart in python 52624b187834 view on github https img shields io badge github notebook orange logo github data visualization 0006 dual axis combo chart dual axis combo chart ipynb adding 3rd y axis to combo chart in python matplotlib seaborn and pandas plot https bindichen medium com adding a third y axis to python combo chart 39f60fb66708 view on github https img shields io badge github notebook orange logo github data visualization 0010 multiple y axis multiple y axis combo chart ipynb altair python interactive data visualization with altair https towardsdatascience com python interactive data visualization with altair b4c4664308f8 gist https gist github com bindichen 0dea2e7fa189f8ff1397180f3b764cc7 file altair interactive selection chart py interactive data visualization for exploring coronavirus spreads https towardsdatascience com interactive data visualization for exploring coronavirus spreads f33cabc64043 gist https gist github com bindichen de39182e050962c0b627d5146e3bce09 file altair data visualization py matplotlib matplotlib animation in jupyter notebook https bindichen medium com matplotlib animations in jupyter notebook 4422e4f0e389 view on github https img shields io badge github notebook orange logo github data visualization 0001 matplotlib animation matplotlib animation notebook ipynb matplotlib linear regression animation in jupyter notebook https bindichen medium com matplotlib linear regression animation in jupyter notebook 2435b711bea2 view on github https img shields io badge github notebook orange logo github data visualization 0002 matplotlib animation with regression matplotlib linear regression animation ipynb tensorflow the google s 7 steps of machine learing in practice https towardsdatascience com the googles 7 steps of machine learning in practice a tensorflow example for structured data 96ccbb707d77 view on github https img shields io badge github notebook orange logo github tensorflow2 001 googles 7 steps of machine learning in practice 001 googles 7 steps of machine learning in practice ipynb 3 ways to create a machine learning model with keras and tensorflow 2 0 https towardsdatascience com 3 ways to create a machine learning model with keras and tensorflow 2 0 de09323af4d3 view on github https img shields io badge github notebook orange logo github tensorflow2 002 3 ways to build machine learning model with keras 3 ways to build a machine learning model with keras ipynb model regularization in practice https towardsdatascience com machine learning model regularization in practice an example with keras and tensorflow 2 0 52a96746123e view on github https img shields io badge github notebook orange logo github tensorflow2 003 model regularization model regularization ipynb batch normalization in practice https medium com bindiatwork batch normalization in practice an example with keras and tensorflow 2 0 b1ec28bde96f view on github https img shields io badge github notebook orange logo github tensorflow2 004 batch norm batch normalization ipynb early stopping in practice https medium com bindiatwork a practical introduction to early stopping in machine learning 550ac88bc8fd view on github https img shields io badge github notebook orange logo github tensorflow2 005 early stopping early stopping ipynb learning rate schedules in practice https medium com bindiatwork learning rate schedule in practice an example with keras and tensorflow 2 0 2f48b2888a0c view on github https img shields io badge github notebook orange logo github tensorflow2 006 learning rate schedules learning rate schedules ipynb keras callbacks in practice https medium com bindiatwork a practical introduction to keras callbacks in tensorflow 2 705d0c584966 view on github https img shields io badge github notebook orange logo github tensorflow2 007 keras callback keras callbacks ipynb keras custom callbacks https bindichen medium com building custom callbacks with keras and tensorflow 2 85e1b79915a3 view on github https img shields io badge github notebook orange logo github tensorflow2 008 keras custom callback keras custom callback ipynb 7 popular activation functions in deep learning https bindichen medium com 7 popular activation functions you should know in deep learning and how to use them with keras and 27b4d838dfe6 view on github https img shields io badge github notebook orange logo github tensorflow2 010 popular activation functions popular activation functions ipynb why relu in deep learning and the best practice https towardsdatascience com why rectified linear unit relu in deep learning and the best practice to use it with tensorflow e9880933b7ef view on github https img shields io badge github notebook orange logo github tensorflow2 011 relu relu and best practice ipynb pytorch tba scikit learn and general machine learning a practical introduction to grid search random search and bayes search https bindichen medium com a practical introduction to grid search random search and bayes search d5580b1d941d view on github https img shields io badge github notebook orange logo github traditional machine learning 005 grid search vs random search vs bayes search gridsearch vs randomsearch vs bayessearch ipynb a practical introduction to 9 regression algorithms https bindichen medium com a practical introduction to 9 regression algorithms 389057f86eb9 view on github https img shields io badge github notebook orange logo github traditional machine learning 001 regression algorithms regression algorithms ipynb train test split and cross validation you should know in machine learning tba view on github https img shields io badge github notebook orange logo github traditional machine learning 006 train test split and cross validation train test and cross validation ipynb | machine-learning deep-learning data-analysis pandas-python | ai |
introduction_to_ml_with_python | binder https mybinder org badge svg https mybinder org v2 gh amueller introduction to ml with python master introduction to machine learning with python this repository holds the code for the forthcoming book introduction to machine learning with python by andreas mueller http amueller io and sarah guido https twitter com sarah guido you can find details about the book on the o reilly website http shop oreilly com product 0636920030515 do the book requires the current stable version of scikit learn that is 0 20 0 most of the book can also be used with previous versions of scikit learn though you need to adjust the import for everything from the model selection module mostly cross val score train test split and gridsearchcv this repository provides the notebooks from which the book is created together with the mglearn library of helper functions to create figures and datasets for the curious ones the cover depicts a hellbender https en wikipedia org wiki hellbender all datasets are included in the repository with the exception of the aclimdb dataset which you can download from the page of andrew maas http ai stanford edu amaas data sentiment see the book for details if you get importerror no module named mglearn you can try to install mglearn into your python environment using the command pip install mglearn in your terminal or pip install mglearn in jupyter notebook errata please note that the first print of the book is missing the following line when listing the assumed imports python from ipython display import display please add this line if you see an error involving display the first print of the book used a function called plot group kfold this has been renamed to plot label kfold because of a rename in scikit learn setup to run the code you need the packages numpy scipy scikit learn matplotlib pandas and pillow some of the visualizations of decision trees and neural networks structures also require graphviz the chapter on text processing also requires nltk and spacy the easiest way to set up an environment is by installing anaconda https www continuum io downloads installing packages with conda if you already have a python environment set up and you are using the conda package manager you can get all packages by running conda install numpy scipy scikit learn matplotlib pandas pillow graphviz python graphviz for the chapter on text processing you also need to install nltk and spacy conda install nltk spacy installing packages with pip if you already have a python environment and are using pip to install packages you need to run pip install numpy scipy scikit learn matplotlib pandas pillow graphviz you also need to install the graphiz c library which is easiest using a package manager if you are using os x and homebrew you can brew install graphviz if you are on ubuntu or debian you can apt get install graphviz installing graphviz on windows can be tricky and using conda anaconda is recommended for the chapter on text processing you also need to install nltk and spacy pip install nltk spacy downloading english language model for the text processing chapter you need to download the english language model for spacy using python m spacy download en submitting errata if you have errata for the e book please submit them via the o reilly website http www oreilly com catalog errata csp isbn 0636920030515 you can submit fixes to the code as pull requests here but i d appreciate it if you would also submit them there as this repository doesn t hold the master notebooks cover cover jpg | ai |
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basiscash-frontend | basis cash title image https raw githubusercontent com basis cash basiscash protocol master assets bg jpeg basis cash interface this is front end repository of the basis cash https basis cash set up environment to begin you need to install dependencies with yarn yarn you should update default provider url because our production provider url is limited by cors for security on src config ts please replace it diff defaultprovider https mainnet infura io v3 old provider url defaultprovider https mainnet infura io v3 your provider url after it you can launch the development server with following command yarn start if you want to bring your own contract if you want to use different contract deployment on development please build basiscash protocol https github com basis cash basiscash protocol and copy n paste the deployment information generated on build deployment network json into this project s deployment directory which is src basis cash deployments then you need to change the deployment reference into yours suppose that the new deployment file is named deployments local json diff deployments require basis cash deployments deployments mainnet json deployments require basis cash deployments deployments local json contributions contributions are welcome since we don t have any contribution guide issue templates yet please feel free to send prs to the basiscash frontend license mit | front_end |
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opencv-processing-book | getting started with computer vision this book aspires to provide an easy entry point for creative experimentation with computer vision it introduces the code and concepts necessary to build computer vision projects with the processing http processing org creative coding environment and opencv http opencv org the definitive open source computer vision library it is designed to be accessible to a beginner audience but also to instill the fundamental concepts of the field enabling readers to not just follow recipes but build their own projects this book is being developed in parallel with opencv for processing http github com atduskgreg opencv processing a processing library that provides access to opencv all of its code examples use that opencv for processing and it acts as the definitive documentation for that library getting started with computer vision is an in progress community project begun in may of 2013 it is generously supported by o reilly media http oreill com and will be published for public consumption by them it s text is available freely here under a creative commons license contributions welcome getting started with computer vision is currently highly incomplete but under rapid development suggestions contributions corrections and pull requests are highly welcome if you re looking for places to help check out the proposed table of contents https github com atduskgreg opencv processing book blob master book toc md contributions are especially welcome in the form of examples particularly in the area of camera calibration and machine learning format this book is organized to be useful both as reference and as tutorial each page introduces its topic in a manner that should stand alone providing links to other relevant pages that may be required as prerequisites the overall order of the book is designed to lead a beginner through the material in a sensible and useful way if read linearly given the visual nature of computer vision work each page also takes maximum advantage of the multi media capabilities of the web to cover its material including video and interactive code examples as well as text explanations documentation and links | ai |
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pynorama | pynorama pynorama is a tool for visualizing intricate datasets for which a simple table format is not suitable it was created with natural language processing applications in mind pynorama example screenshot pynorama png pynorama lets you define views in python that are rendered as interactive web applications letting you browse analyse and understand your data pynorama is scalable and extensible pynorama has a clean and simple architecture it makes little assumptions about your data source or data format read in the documentation https github com manahl pynorama blob master docs source walkthrough ipynb about developing extensions quickstart install pynorama for a minimal install run pip install pynorama using pynorama to create a view define a table describing your data records currently supported sources are pandas dataframe and mongodb queries define different stages of your data pipeline return a particular records for a given stage configure the ui in python this would look similar to this python from pynorama import view from pynorama table import pandastable class exampleview view def init self name description super exampleview self init name description setup data def get pipeline self return raw stage viewer raw tokenized viewer json parents raw stage def get record self key stage if stage raw stage return get html key else return get processed data key def get table self return pandastable get dataframe next register the view with pynorama python from pynorama import register view register view exampleview example finally let pynorama set up a flask server for you and start it python from pynorama import make server app make server app run host localhost port 5000 now just run your python script the view should be accessible at http localhost 5000 view example for more information check the examples examples and the documentation https github com manahl pynorama blob master docs source walkthrough ipynb acknowledgements pynorama was developed at man ahl http www ahl com original concept and implementation alexander wettig https github com codecreator contributors from ahl tech team slavi marinov https github com slavi nikolai matiushev https github com egao1980 contributions welcome license pynorama is licensed under the gnu lgpl v2 1 a copy of which is included in license license | ai |
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rtt_2019_ncov | rtt 2019 ncov stm32f103re rt thread rtos rt thread studio esp8266 api cjson lcd stm32 2019 ncov https github com whik stm32 2019 ncov | os |
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QuikieApps-India-s-Top-notch-Reactjs-Development-Services | quikieapps india s top notch reactjs development services a href https www quikieapps com services reactjs development company quikieapps reactjs development services company a india s top notch reactjs development services quikieapps is the most valued reactjs development company in bangalore india we rely on fine craftsmanship and integrity to provide flawless reactjs development services to build web and mobile applications that can elevate your business in the competitive market effectively and efficiently react is the most popular and trending technology in both mobile app development and web development reactjs is of prime importance in the present technology invaded the world of webpages we have worked for some of the industry experts for developing and producing some of the profound reactjs software solutions why quikieapps is a top react js development company finding someone who can build you a product or service exactly the way you want is nearly an impossible task we are here to save you from the tiring hunt for the best reactjs developer because we understand and believe in your vision subscribing to us would be very much propitious to you when it comes to cross platform open source development environments like front end amp backend libraries of javascript quikieapps is one of the first users we dedicate ourselves entirely to the challenge of transforming your dream project into a beguiling reality we can proudly say that quikieapps is the most salutary reactjs development company in bangalore we have a team of highly trained determined and skilled reactjs developers from bangalore india we have a team who expertise in javascript frameworks and technologies to deliver you the most effective and compliant reactjs development services and solutions customer loyalty comes first for us and is of the utmost importance | server |
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neurips_llm_efficiency_challenge | neurips 1 llm 1 gpu challenge this repository provides a starting point for those who are interested in the neurips 1 llm 1 gpu competition https llm efficiency challenge github io it provides detailed clarifications on what a submission looks like exactly and how it will be evaluated and submitted at a high level the key thing you will contribute is a dockerfile which will be a reproducible artifact that we can use to test your submission the dockerfile should contain all the code and dependencies needed to run your submission we will use this dockerfile to build a docker image and then run it against a set of tasks which will be a subset of the helm https crfm stanford edu helm latest tasks your dockerfile will expose a simple http server which needs to implement 2 endpoints process and tokenize we will build that dockerfile and expect it to launch an http server once that server is launched we will make requests to it via helm and record your results at a high level the flow you should follow to ensure a strong submission 1 pick approved llms and datasets from here https llm efficiency challenge github io challenge 2 start with one of sample submissions sample submissions and make sure it runs 3 evaluate it locally on your own 40gb a100 or 4090 if you don t have funding for either please see the gpu funding gpu funding section for some more options 4 once you have something working you can make a submission on our discord leaderboard https discord com channels 1124130156336922665 1124134272631054447 1151718598818156645 to see how you fare up against other competitors 5 on the competition deadline make sure you have the final eval dockerfile you d like us to run in your github repo refer to the timeline https llm efficiency challenge github io dates 6 if your entry makes the shortlist we will work with you to reproduce all of your artifacts with another finetuning dockerfile contents approved llm dataset approved llm and dataset submission submission evaluate your model locally using helm evaluate your model locally using helm finetune finetune create your own submission template create your own submission template discord leaderboard discord leaderboard final leaderboard submission final eval submission evaluating the final submission evaluating the final submission gpu funding gpu funding approved llm and dataset the llm space has complex licenses which can make it difficult to figure out what s permittable to use in a competition to streamline this process we ve shortlisted a few models and datasets we know are safe to use here https llm efficiency challenge github io challenge that said the llm space is fast moving so if you d like to use a dataset or model that isn t on our list make sure to ask us about it on https discord gg xjwq5ddmk7 https discord gg xjwq5ddmk7 submission the submission in this repository is a basic implementation of the setting up an http server in accordance to the open api spec it includes a sample solution built off of lit gpt https github com lightning ai lit gpt and open llama weights that participants can reference or modify as they see fit you can use the provided code as a reference or starting point for your own implementation the main py file contains the simple fastapi server and you can modify it to suit your needs you can find the lit gpt submission here sample submissions lit gpt and the llama recipes submission here sample submissions llama recipes with instructions on how to run each locally make sure that your final submission has only a single dockerfile and that your weights are not directly included in the repo they need to be downloaded during docker build or at runtime evaluate your model locally using helm every submission will be tested against helm https crfm stanford edu helm latest which is a standard suite to evaluate llms on a broad set of datasets this competition will leverage helm for its evaluation infrastructure the organizers will leverage standard stem tasks from helm although we will keep the exact set a secret and in addition we ll be including some heldout tasks that are presently not in helm as you re working on your submission dockerfile you ll want to test it out locally to make sure your contribution works as expected before you submit it helm makes it easy to add new evaluation datasets by just adding another line in a config file so make sure to experiment with the different datasets they have available and feel free to contribute your own to learn more about how to test your submission with helm please follow the instructions here helm md finetune it s likely that an untuned base model won t give you satisfactory results in that case you might find it helpful to do some additional finetuning there are many frameworks to do this but we ve created 2 sample submissions to do so 1 lit gpt sample submissions lit gpt 2 llama recipes sample submissions llama recipes create your own submission template note that we ve offered 2 sample submissions our evaluation infrastructure is generic and only assumes an http client so you can use a finetuning framework in python like the ones we ve suggested but also any non based python framework you like using the openapi json file in this repository contains the openapi specification for the competition api competitors can use this specification to understand the api endpoints request and response structures and overall requirements for interacting with the competition platform the openapi specification provides a standardized way to describe the api making it easier for competitors to develop their own solutions and integrate them seamlessly with the competition infrastructure discord leaderboard the lightning ai https lightning ai has built a discord based for us you can find it on discord by its name evalbot 4372 you can interact with it by dm ing it with a zipped file of your sample submission and message it to either eval a100 or eval 4090 more details on the bot are here https discord com channels 1124130156336922665 1124134272631054447 1151718598818156645 once you make a submission the bot will inform you whether your submission failed or succeeded and after a few hours will publicly post your results if you re at the top of the queue you can expect the eval to take 1 2h but depending on the size of the queue this could be longer so please be mindful to not hurt other competitors trying to use the limited amount of hardware and ensure that your submissions work locally first your submission will remain private to other competitors the end to end flow is described here leaderboard md final leaderboard submission when you registered for the competition you would have needed to create a github repo when the submission deadline is reached make sure your github repo has a dockerfile in case the location is ambiguous please sure to let us know in your readme md the organizers will take your dockerfile and run it as is and compute a baseline eval score the purpose of this step is to primarily filter out broken submissions or submissions that can t outperform the unfinetuned sample submissions the deadline is on oct 25 2023 with important dates listed here https llm efficiency challenge github io dates evaluating the final submission once the organizers have identified a shortlist of strong submissions we will message you directly for another dockerfile that would reproduce all of your artifacts the best submission among this shortlist will win the competition and be invited to present their work at neurips at our workshop gpu funding aws https aws amazon com has graciously agreed to provide 500 in aws credits to 25 participating teams in the llm efficiency competition you will be able to pick and choose from available hardware to experiment before you make your final submission to be eligible please make sure to sign up at https llm efficiency challenge github io submission and write a short proposal in your readme md and add jisaacso https github com jisaacso to your repos who will review your proposals we ll be prioritizing the first teams with serious proposals good luck there are some other free ways of getting gpus that people have posted on discord here https discord com channels 1124130156336922665 1149283885524463637 1149283885524463637 and you can shop around for both 4090 and a100 on cloud on https cloud gpus com https cloud gpus com | ai |
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grecha.js | grecha js kashahard kashahard gif simple front end javascript framework originally designed for emotejam https github com tsoding emotejam the name basically means buckwheat https en wikipedia org wiki buckwheat in russian quick start https tsoding github io grecha js example html html doctype html html head title grecha js title head body div id entry div script src grecha js script script const kasha img kasha png const kashahard img kashahard gif let count 0 let hard false const r router div h1 grecha js div a foo att href foo div a bar att href bar div counter count div hard kashahard kasha onclick function count 1 hard hard r refresh foo div h1 foo p lorem div a home att href bar div h1 bar p lorem div a home att href entry appendchild r script body html | front_end |
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Udagram_Image_Filtering_Microservice | p about me p p backend cloud p boy b anteneh bizuneh b br nbsp nbsp nbsp nbsp nbsp a href https twitter com twitter a nbsp nbsp nbsp nbsp nbsp a href https github com github a nbsp nbsp nbsp nbsp nbsp a href https linkedin com linkedin a br br udagram image filtering microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend covered in the course 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service covered in the course 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images your assignment tasks setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm i 2 run the development server with npm run dev create a new endpoint in the server ts file the starter code has a task for you to complete an endpoint in src server ts which uses query parameter to download an image from a public url filter the image and return the result we ve included a few helper functions to handle some of these concepts and we re importing it for you at the top of the src server ts file typescript import filterimagefromurl deletelocalfiles from util util deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service don t forget you can use eb deploy to push changes stand out optional refactor the course restapi if you re feeling up to it refactor the course restapi to make a request to your newly provisioned image server authentication prevent requests without valid authentication headers note if you choose to submit this make sure to add the token to the postman collection and export the postman collection file to your submission so we can review custom domain name add your own domain name and have it point to the running services try adding a subdomain name to point to the processing server note domain names are not included in aws free tier and will incur a cost git repository https github com anteneh2121 udagram image filtering microservice a link to the endpoint url for a running elastic beanstalk deployment http udagram image filtering microservice dev2 us east 1 elasticbeanstalk com http udagram image filtering microservice dev2 us east 1 elasticbeanstalk com filteredimage image url https i natgeofe com n 46b07b5e 1264 42e1 ae4b 8a021226e2d0 domestic cat thumb jpg image https user images githubusercontent com 88293613 199578487 185c3aa2 4668 4655 b5d6 d08190bc0b77 png | cloud |
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Feature-Engineering | this repository comprises of materials notebooks exercises and quizes and solutions provided by the google cloud as a part of machine learning with tensorflow on google cloud platform specialization | cloud |
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FreeRTOS_study | freertos study this project created for studying freertos simulator linux this is a simulator of freertos in linux make all clean are supported two simple tasks created and send receive data between them in the example sconstruct have been added for build scons for build scons c for clear all build target note please create a directory named tmp in root directory before make otherwise will make failed i am lazy to auto create a blank tmp in makefile | os |
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distortosExamples | distortosexamples build status https travis ci org distortec distortosexamples svg branch master https travis ci org distortec distortosexamples examples for distortos http distortos org object oriented c rtos for microcontrollers configuration building to configure build distortosexamples you need cmake https cmake org version 3 7 or later a build tool supported by cmake https cmake org cmake help latest manual cmake generators 7 html manual cmake generators 7 it is highly recommended to use ninja https ninja build org arm none eabi bleeding edge toolchain https github com freddiechopin bleeding edge toolchain gcc version 5 or later build infrastructure of distortos tries to follow typical cmake cross compiling workflow which means that you always have to use a so called toolchain file toolchain files in distortos also serve another purpose they select the board which is going to be used by your application 1 download source package of distortosexamples in zip https github com distortec distortosexamples archive master zip or tar gz https github com distortec distortosexamples archive master tar gz format and extract it 2 download source package of distortos in zip https github com distortec distortos archive master zip or tar gz https github com distortec distortos archive master tar gz format and extract it inside distortosexamples extracted in step 1 for example to distortos 3 adjust relative path to distortos in add subdirectory path to distortos from top level cmakelists txt 4 create a build folder for example output 5 from within the build folder initialize it with cmake for example with cmake dcmake toolchain file distortos source board st stm32f4discovery toolchain st stm32f4discovery cmake gninja if you want a default configuration or cmake c configurations st stm32f4discovery distortosconfiguration cmake gninja if you want to start from a saved configuration 6 edit distortos configuration with a tool of your choice for example cmake gui a gui application or ccmake curses based application 7 execute selected build tool for example ninja or ninja v if you want to see all command lines while building you can obviously replace steps 1 3 with git clone recursive https github com distortec distortosexamples steps 4 6 can be all done from within cmake gui after starting the application use browse source button to select the folder with distortosexamples and browse build button to select the build folder then click on configure button in the cmakesetup window which appears select the generator of your choice and make sure that specify toolchain file for cross compiling is selected before going any further click next and specify the toolchain file which also selects the board for example source folder distortos source board st stm32f4discovery toolchain st stm32f4discovery cmake and click finish button tl dr wget https github com distortec distortosexamples archive master tar gz o distortosexamples master tar gz tar xf distortosexamples master tar gz wget https github com distortec distortos archive master tar gz o distortos master tar gz tar xf distortos master tar gz mv t distortos master distortosexamples master distortos cd distortosexamples master mkdir output cd output cmake dcmake toolchain file distortos source board st stm32f4discovery toolchain st stm32f4discovery cmake gninja cmake gui ninja or wget https github com distortec distortosexamples archive master tar gz o distortosexamples master tar gz tar xf distortosexamples master tar gz wget https github com distortec distortos archive master tar gz o distortos master tar gz tar xf distortos master tar gz mv t distortos master distortosexamples master distortos cd distortosexamples master mkdir output cd output cmake c configurations st stm32f4discovery distortosconfiguration cmake gninja cmake gui ninja | os |
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Observable-Store | node js ci https github com danwahlin observable store actions workflows nodejs build validation yml badge svg https github com danwahlin observable store actions workflows nodejs build validation yml npm version https img shields io npm v codewithdan observable store color 23330c252 label npm 20version https www npmjs com package codewithdan observable store observable store state management for front end applications angular react vue js or any other observable store is a front end state management library that provides a simple yet powerful way to manage state in front end applications front end state management has become so complex that many of us spend more hours working on the state management code than on the rest of the application observable store has one overall goal keep it simple the goal of observable store is to provide a small simple and consistent way to manage state in any front end application angular react vue js or any other while achieving many of the key goals goals offered by more complex state management solutions while many front end frameworks libraries provide state management functionality many can be overly complex and are only useable with the target framework library observable store is simple and can be used with any front end javascript codebase a href https blog codewithdan com simplifying front end state management with observable store target blank blog post about observable store a a href https www youtube com watch v jn4ah5pgwha target blank talk on observable store a using obervable store images observablestore png a name goals a key goals of observable store 1 keep it simple 1 single source of truth for state 1 store state is immutable 1 provide state change notifications to any subscriber 1 track state change history 1 easy to understand with a minimal amount of code required to get started 1 works with any front end project built with javascript or typescript angular react vue or anything else 1 integrate with the redux devtools angular and react currently supported steps to use observable store here s a simple example of getting started using observable store note that if you re using typescript you can provide additional details about the store state by using an interface or class additional examples of that can be found below 1 install the observable store package npm install codewithdan observable store 1 install rxjs a required peer dependency if your project doesn t already reference it npm install rxjs 1 create a class that extends observablestore optionally pass settings into super in your class s constructor view observable store settings settings while this shows a pure javascript approach observablestore also accepts a generic that represents the store type see the angular example below for more details javascript export class customersstore extends observablestore constructor super add settings here 1 update the store state using setstate state action javascript addcustomertostore newcustomer this setstate customer newcustomer add customer 1 retrieve store state using getstate javascript getcustomerfromstore this getstate customer 1 subscribe to store changes in other areas of the application by using the store s statechanged observable javascript create customersstore object or have it injected if platform supports that init this storesub this customersstore statechanged subscribe state if state this customer state customer note would need to unsubscribe by calling this storesub unsubscribe as the target object is destroyed 1 access store state history in customersstore by calling the statehistory property this assumes that the trackstatehistory setting is set to true javascript console log this statehistory api and settings observable store api api observable store settings settings observable store global settings globalsettings observable store extensions extensions running the samples open the samples folder available at the github repo and follow the instructions provided in the readme file for any of the provided sample projects sample applications using observable store with angular angular using observable store with react react using observable store with vue js vue a name angular a using observable store with angular see the samples folder in the github repo for examples of using observable store with angular 1 create an angular application using the angular cli or another option 1 install codewithdan observable store npm install codewithdan observable store 1 add an interface or model object that represents the shape of the data you d like to add to your store here s an example of an interface to store customer state typescript export interface storestate customers customer customer customer 1 add a service you can optionally calll it a store if you d like that extends observablestore t pass the interface or model class that represents the shape of your store data in for t as shown next typescript injectable export class customersservice extends observablestore storestate 1 in the constructor add a call to super the store allows you to turn tracking of store state changes on and off using the trackstatehistory property see a list of observable store settings settings typescript constructor super trackstatehistory true 1 add functions into your service store to retrieve store sort filter or perform any actions you d like to update the store call setstate and pass the action that is occuring as well as the store state to get the state out of the store call getstate note that store data is immutable and getstate always returns a clone of the store data here s a simple example typescript injectable export class customersservice extends observablestore storestate sorterservice sorterservice constructor sorterservice sorterservice const initialstate customers customer null super trackstatehistory true this setstate initialstate init state this sorterservice sorterservice get const customers this getstate if customers return of customers call server and get data assume async call here that returns observable return asyncdata add customer customer let state this getstate state customers push customer this setstate customers state customers add customer remove let state this getstate state customers splice state customers length 1 1 this setstate customers state customers remove customer sort property string id let state this getstate const sortedstate this sorterservice sort state customers property this setstate customers sortedstate sort customers while strings are used for actions in the prior examples you can use string enums a typescript feature as well if you want to have a set list of actions to choose from typescript export enum customersstoreactions addcustomer add customer removecustomer remove customer getcustomers get customers sortcustomers sort customers example of using the enum in a store add customer customer let state this getstate state customers push customer this setstate customers state customers customersstoreactions addcustomer 1 if you want to view all of the changes to the store you can access the statehistory property typescript console log this statehistory 1 an example of the state history output is shown next typescript example statehistory output action init state beginstate null endstate customers id 1545847909628 name jane doe address street 1234 main st city phoenix state az zip 85258 customer null action add customer beginstate customers id 1545847909628 name jane doe address street 1234 main st city phoenix state az zip 85258 customer null endstate customers id 1545847909628 name jane doe address street 1234 main st city phoenix state az zip 85258 id 1545847921260 name fred address street 1545847921260 main st city phoenix state az zip 85258 customer null 1 any component can be notified of changes to the store state by injecting the store and then subscribing to the statechanged observable typescript customers customer storesub subscription constructor private customersservice customersservice ngoninit if using async pipe recommend renaming customers to customers this customers this customersservice statechanged can subscribe to statechanged observable of the store this storesub this customersservice statechanged subscribe state if state this customers state customers can call service store to get data directly it won t fire when the store state changes though in this case this storesub this customersservice get subscribe custs this customers custs you ll of course want to unsubscribe in ngondestroy check out subsink on npm for a nice way to easily subscribe unsubscribe typescript ngondestroy if this storesub this storesub unsubscribe a name react a using observable store with react see the samples folder in the github repo for examples of using observable store with react 1 create a react application using the create react app or another option 1 install codewithdan observable store npm install codewithdan observable store 1 install rxjs a required peer dependency npm install rxjs 1 add a store class you can call it whatever you d like that extends observablestore t javascript export class customersstore extends observablestore 1 in the constructor add a call to super the store allows you to turn tracking of store state changes on and off using the trackstatehistory property see a list of observable store settings settings javascript export class customersstore extends observablestore constructor super trackstatehistory true 1 add functions into your service store to retrieve store sort filter or perform any actions you d like to update the store call setstate and pass the action that is occuring as well as the store state to get the state out of the store call getstate note that store data is immutable and getstate always returns a clone of the store data here s a simple example javascript export class customersstore extends observablestore constructor super trackstatehistory true fetchcustomers using fetch api here to keep it simple but any other 3rd party option will work axios ky etc return fetch customers then response response json then customers this setstate customers get customers return customers getcustomers let state this getstate pull from store cache if state state customers return this createpromise null state customers doesn t exist in store so fetch from server else return this fetchcustomers getcustomer id return this getcustomers then custs let filteredcusts custs filter cust cust id id const customer filteredcusts filteredcusts length filteredcusts 0 null this setstate customer get customer return customer createpromise err result return new promise resolve reject return err reject err resolve result while strings are used for actions in the prior example you can use an object as well if you want to have a set list of actions to choose from javascript const customersstoreactions getcustomers get customers getcustomer get customer example of using the enum in a store getcustomer id return this getcustomers then custs let filteredcusts custs filter cust cust id id const customer filteredcusts filteredcusts length filteredcusts 0 null this setstate customer customersstoreactions getcustomer return customer 1 export your store a default export is used here javascript export default new customersstore 1 if you want to view all of the changes to the store you can access the store s statehistory property javascript console log this statehistory example statehistory output action init state beginstate null endstate customers id 1545847909628 name jane doe address street 1234 main st city phoenix state az zip 85258 customer null action add customer beginstate customers id 1545847909628 name jane doe address street 1234 main st city phoenix state az zip 85258 customer null endstate customers id 1545847909628 name jane doe address street 1234 main st city phoenix state az zip 85258 id 1545847921260 name fred address street 1545847921260 main st city phoenix state az zip 85258 customer null 1 import your store into a component javascript import customersstore from stores customersstore 1 now use your store to access or update data any component can be notified of changes to the store state by subscribing to the statechanged observable javascript storesub null componentdidmount customersstore option 1 subscribe to store changes useful when a component needs to be notified of changes but won t always call store directly this storesub customersstore statechanged subscribe state if state this setstate customers state customers in this example we trigger getting the customers code above receives the customers customersstore getcustomers option 2 get data directly from store if a component triggers getting the data it can retrieve it directly rather than subscribing customersstore getcustomers then customers you ll want to unsubscribe in componentwillunmount javascript componentwillunmount if this storesub this storesub unsubscribe a name vue a using observable store with vue js a name api a store api observable store provides a simple api that can be used to get set state subscribe to store state changes and access state history if you re new to typescript generics the t shown in the apis below represents your store s state functions description dispatchstate statechanges partial t dispatchglobalstate boolean true t dispatch the store s state without modifying the state service state can be dispatched as well as the global store state if dispatchglobalstate is false then global state will not be dispatched to subscribers defaults to true getstate deepclonereturnedstate boolean true t retrieve store s state if using typescript optional then the state type defined when the store was created will be returned rather than any the deepclonereturnedstate boolean parameter default is true can be used to determine if the returned state will be deep cloned or not if set to false a reference to the store state will be returned and it s up to the user to ensure the state isn t change from outside the store setting it to false can be useful in cases where read only cached data is stored and must be retrieved as quickly as possible without any cloning getstateproperty tprop propertyname string deepclonereturnedstate boolean true tprop retrieve a specific property from the store s state which can be more efficient than getstate since only the defined property value will be returned and cloned rather than the entire store value if using typescript optional then the generic property type used with the function call will be the return type getstatesliceproperty tprop propertyname string deepclonereturnedstate boolean true tprop if a statesliceselector has been set the specific slice will be searched first retrieve a specific property from the store s state which can be more efficient than getstate since only the defined property value will be returned and cloned rather than the entire store value if using typescript optional then the generic property type used with the function call will be the return type logstateaction state any action string void add a custom state value and action into the state history assumes trackstatehistory setting was set on store or using the global settings resetstatehistory void reset the store s state history to an empty array setstate state t action string dispatchstate boolean true deepclonestate boolean true t set the store state pass the state to be updated as well as the action that is occuring the state value can be a function see example below the latest store state is returned and any store subscribers are notified of the state change the dispatchstate parameter can be set to false if you do not want to send state change notifications to subscribers the deepclonereturnedstate boolean parameter default is true can be used to determine if the state will be deep cloned before it is added to the store setting it to false can be useful in cases where read only cached data is stored and must added to the store as quickly as possible without any cloning static addextension extension observablestoreextension used to add an extension into observablestore the extension must implement the observablestoreextension interface static clearstate void clear null the store state across all services that use it static initializestate state any used to initialize the store s state an error will be thrown if this is called and store state already exists so this should be set when the application first loads no notifications are sent out to store subscribers when the store state is initialized static resetstate state dispatchstate boolean true used to reset the state of the store to a desired value for all services that derive from observablestore t a state change notification and global state change notification is sent out to subscribers if the dispatchstate parameter is true the default value br properties description globalstatechanged observable any subscribe to global store changes i e changes in any slice of state of the store the global store may consist of n slices of state each managed by a particular service this property notifies of a change in any of the n slices of state returns an rxjs observable containing the current store state globalstatewithpropertychanges observable statewithpropertychanges any subscribe to global store changes i e changes in any slice of state of the store and also include the properties that changed as well the global store may consist of n slices of state each managed by a particular service this property notifies of a change in any of the n slices of state upon subscribing to globalstatewithpropertychanges you will get back an object containing state which has the current store state and statechanges which has the individual properties data that were changed in the store statechanged observable t subscribe to store changes in the particlar slice of state updated by a service if the store contains n slices of state each being managed by one of n services then changes in any of the other slices of state will not generate values in the statechanged stream returns an rxjs observable containing the current store state or a specific slice of state if a statesliceselector has been specified statewithpropertychanges observable statewithpropertychanges t subscribe to store changes in the particlar slice of state updated by a service and also include the properties that changed as well upon subscribing to statewithpropertychanges you will get back an object containing state which has the current slice of store state and statechanges which has the individual properties data that were changed in the store statehistory statehistory retrieve state history assumes trackstatehistory setting was set on the store static allstoreservices any provides access to all services that interact with observablestore useful for extensions that need to be able to access a specific service static globalsettings observablestoreglobalsettings get set global settings throughout the application for observablestore see the observable store settings settings below for additional information note that global settings can only be set once as the application first loads static isstoreinitialized boolean used to determine if the the store s state is currently initialized this is useful if there are multiple scenarios where the store might have already been initialized such as during unit testing etc or after the store has been cleared br note that typescript types are used to describe parameters and return types above typescript is not required to use observable store though passing a function to setstate here s an example of passing a function to setstate this allows the previous state to be accessed directly while setting the new state javascript this setstate prevstate return customers this sorterservice sort prevstate customers property sort customers a name settings a store settings per service observable store settings can be passed when the store is initialized when super is called in a service this gives you control over how things work for each service within your application that extends the store setting description trackstatehistory boolean determines if the store s state will be tracked or not defaults to false pass it when initializing the observable store see examples above when true you can access the store s state history by calling the statehistory property logstatechanges boolean log any store state changes to the browser console defaults to false includestatechangesonsubscribe boolean deprecated returns the store state by default when false default set to true if you want to receive the store state as well as the specific properties data that were changed when the statechanged subject emits upon subscribing to statechanged you will get back an object containing state which has the current store state and statechanges which has the individual properties data that were changed in the store since this is deprecated use statewithpropertychanges or globalstatewithpropertychanges instead statesliceselector function function to select the slice of the store being managed by this particular service if specified then the specific state slice is returned if not specified then the total state is returned defaults to null example of passing settings to the store javascript export class customersstore extends observablestore constructor super add settings here using the statesliceselector function the statesliceselector function can be used to return a slice of the store state that is managed by a service to any subscribers for example if a customersservice manages a customers collection and a selectedcustomer object you can return only the selectedcustomer object to subscribers rather than customers and selectedcustomer by creating a statesliceselector function define it as you initialize the service when passing a settings object to super in the service s constructor typescript export class customersservice extends observablestore storestate constructor super statesliceselector state return customer state selectedcustomer return other parts of the store here too if desired a name globalsettings a global store settings you can set the following observable store settings globally for the entire application if desired for details view the observable store settings settings section this allows you to define the settings once and all services that extend observable store will automatically pick these settings up you can override these properties at the service level as well which is nice when you want a particular service to have more logging as an example while other services don t trackstatehistory logstatechanges includestatechangesonsubscribe deprecated isproduction reserved for future use global store settings are defined once when the application first initializes and before the store has been used javascript observablestore globalsettings pass settings here a name extensions a extensions observable store now supports extensions these can be added when the application first loads by calling observablestore addextension redux devtools extension the first built in extension adds redux devtools https github com reduxjs redux devtools integration into applications that use observable store the extension can be found in the codewithdan observable store extensions package integrating the redux devtools images reduxdevtools png note about angular 9 ivy and the redux devtools support while the code is in place https github com danwahlin observable store blob master modules observable store extensions angular angular devtools extension ts to support it the observable store redux devtools currently do not work with angular 9 and ivy once the findproviders api https github com angular angular blob cd9ae66b357bd4b5f97aa60cea38e48acb015325 packages core src testability testability ts l221 is fully implemented and released by angular then support will be finalized for the redux devtools note about the devtools store property when the redux devtools extension is enabled it will add routing information into your store using a property called devtools this property is used to enable the redux devtools time travel feature and can be useful for associating different action states with a given route when manually looking at store data using the devtools if the redux devtools extension is not enabled such as in production scenarios then the devtools property will not be added into your store integrating angular with the redux devtools see the example in the samples angular store edits folder install the extensions package npm install codewithdan observable store extensions add the following into main ts and ensure that you set trackstatehistory to true typescript import observablestore from codewithdan observable store import reduxdevtoolsextension from codewithdan observable store extensions observablestore globalsettings trackstatehistory true observablestore addextension new reduxdevtoolsextension install the redux devtools extension https chrome google com webstore detail redux devtools lmhkpmbekcpmknklioeibfkpmmfibljd in your browser run your angular application and open the redux devtools extension integrating react with the redux devtools see the example in the samples react store folder install the extensions package npm install codewithdan observable store extensions add the history prop to your router jsx import react from react import router route redirect from react router dom import createbrowserhistory from history export const history createbrowserhistory const routes router history history div routes go here div router export default routes add the following into index js and ensure that you set trackstatehistory to true and pass the history object into the reduxdevtoolsextension constructor as shown javascript import routes history from routes import observablestore from codewithdan observable store import reduxdevtoolsextension from codewithdan observable store extensions observablestore globalsettings trackstatehistory true observablestore addextension new reduxdevtoolsextension reactrouterhistory history reactdom render routes document getelementbyid root install the redux devtools extension https chrome google com webstore detail redux devtools lmhkpmbekcpmknklioeibfkpmmfibljd in your browser run your react application and open the redux devtools extension redux devtools and production while you can enable the redux devtools extension in production it s normally recommended that you remove it that can be done through a custom build process or by checking the environment where your code is running angular example typescript import environment from environments environment if environment production observablestore addextension new reduxdevtoolsextension react example typescript if process env node env production observablestore addextension new reduxdevtoolsextension reactrouterhistory history changes 1 0 11 added includestatechangesonsubscribe setting now deprecated in 2 for cases where a subscriber to statechanged wants to get the current state as well as the specific properties data that were changes in the store defaults to false so prior versions will only receive the current state by default which keeps patched versions compatible in the 1 0 x range set the property to true if you want to receive the store state as well as the specific properties data that were changed when the statechanged subject emits upon subscribing to statechanged you will get back an object containing state which has the current store state and statechanges which has the individual properties data that were changed in the store 1 0 12 changed updatestate to updatestate since it s a private function remove tsconfig json from package 1 0 13 moved behaviorsubject into observableservice class so that if multiple instances of a wrapper around the store are created subscribers can subscribe to the individual instances 1 0 14 added logstatechanges setting to write out all state changes to the browser console when true defaults to false 1 0 15 added action to log output when logstatechanges is true 1 0 16 thanks to a great contribution by mickey puri you can now globally subscribe to store changes globalstatechanged event and even define state slices statesliceselector setting 1 0 17 merged in another contribution by mickey puri to ensure the settings defaults are always applied regardless of how many properties the user passes renamed a settings default property state slice selector statesliceselector added editable store example update delete functionality for angular in the samples folder 1 0 18 minor updates to the readme 1 0 19 updated angular example and added statesliceselector information in readme 1 0 20 updated readme 1 0 21 updated to latest version of rxjs removed subsink from the angular simple store demo to just use a normal subscription for unsubscribing just to keep it more native and require less dependencies 1 0 22 internal type additions and tests contributed by elandyg https github com elandyg 2 0 0 october 13 2019 1 added more strongly typed information for statechanged and the overall api to provide better code help while using observable store 1 rxjs is now a peer dependency rxjs 6 4 0 or higher is required this avoids reported versioning issues that have come up when a project already has rxjs in it the 1 x version of observable store added rxjs as a dependency starting with 2 0 0 this is no longer the case 1 added an observablestore globalsettings property to allow store settings to be defined once if desired for an entire application rather than per service that uses the store 1 getstate and setstate now clone when the global settings isproduction property is false observablestore globalsettings isproduction false when running in production mode no cloning is used in order to enhance performance since mutability issues would ve been detected at development time this technique is used with other store solutions as well note isproduction is no longer used see 2 0 1 below 1 changed typescript module compilation to commonjs instead of es2015 to aid with testing scenarios such as jest where the project doesn t automatically handle es2015 module conventions without extra configuration 2 0 1 october 14 2019 due to edge cases cloning is used in development and production the isproduction property is left in so builds are not broken but currently isn t used 2 1 0 october 24 2019 in order to allow statechanged to be strongly typed and also allow state changes with property changes to return a strongly typed object as well there are now 4 observable options to choose from when you want to know about changes to the store typescript access state changes made by a service interacting with the store allows access to slice of store state that service interacts with statechanged observable t access all state changes in the store regardless of where they re made in the app globalstatechanged observable any access state changes made by a service interacting with the store and include the properties that were changed statewithpropertychanges observable statewithpropertychanges t access all state changes in the store and include the properties that were changed globalstatewithpropertychanges observable statewithpropertychanges any the includestatechangesonsubscribe property is now deprecated since statewithpropertychanges or globalstatewithpropertychanges can be used directly thanks to a href https github com michaelturbe target blank michael turbe a for the feedback and discussion on these changes 2 2 3 december 10 2019 this version adds a redux devtools extension extensions a big thank you to brandonroberts https github com brandonroberts of ngrx https github com ngrx fame for helping get me started integrating with the redux devtools new apis a static allstoreservices property is now available to access all services that extend observablestore and interact with the store used by the redux devtools extension and can be useful for future extensions added static addextension function used to add the redux devtools extension extensions and any future extensions added new codewithdan observable store extensions package for the redux devtools support 2 2 4 january 23 2019 minor updates to the observable store docs fixed a bug in the redux devtools extension that would throw an error when the extension wasn t installed or available updated readme to discuss how to disable extensions for production scenarios thanks to a href https github com riscie target blank matthias langhard a for the feedback and discussion on these changes 2 2 5 february 26 2020 added observablestore initializestate api refactored unit tests 2 2 6 february 29 2020 added observablestore resetstate api added unit tests for resetstate feedback from a href https github com svehera target blank severgyn a and a href https github com luizfilipemedeira target blank luiz filipe a influenced this feature thanks folks 2 2 7 march 6 2020 fixed bug where redux devtools code for angular v8 or lower was also calling code intended for angular v9 which is still a work in progress as noted in the redux devtools section above thanks to a href https github com trentsteel84 target blank trentsteel84 a for reporting the issue 2 2 8 april 2 2020 all calls to getstate and setstate clone data now due to edge issues that can arise otherwise with external references previously it would selectively clone based on dev or prod all functions that get set state now provide an optional deepclone type of boolean property that can be used in cases where it s not desirable to clone state large amount of data being added to the store for caching for example added observablestore clearstate api to null the store across all services that use it added getstateproperty t propname string to retrieve a specific property from the store versus retrieving the entire store as getstate does 2 2 9 may 5 2020 added support for cloning map and set objects in the interal cloner service used by observable store thanks to a href https github com chrisjandrade target blank chris andrade a for the initial contribution 2 2 10 may 20 2020 external apis supported turning off cloning but internal apis still cloned which isn t optimal for people storing a lot of data in the store thanks to a href https github com steve rw target blank steve rw a for asking about it and for the pr that fixed it 2 2 13 august 31 2021 adds a getstatesliceproperty function thanks to a href https github com connorsmith pf target blank connor smith a for the contribution added strict true support into observable store library tsconfig json files updates to documentation 2 2 14 october 31 2021 update readme link to redux devtools thanks to ravi mathpal for the information 2 2 15 november 18 2022 new isstoreinitialized property added thanks to a href https github com jasonlandbridge target blank jason landbridge a for the pr building the project see the readme md file in the modules folder | front_end |
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LMsMBTI | do large language models have personalities doge in memory of labour day of 2022 mbti test https www 16personalities com free personality test got very popular recently question what if we let large language model e g gpt 3 codex and other llms take mbti test what are their personalities what is the factor of affecting its personalities let s have some attempt to run clone this repo pip install set openai api key in get answers py set the mbti prompt txt for the background word it could affect a lot and we encourage everyone to have a try run get answers py to get the decisions made by llms run fill in website py to fill the decisions made by llm to website and download the analysis we have crawled the question from test website and save in the mbti questions txt change freely if you want to use other test initial result by our one shot and default setting on text danvinci 001 a k a gpt 3 the original version code danvinci 001 a k a codex the original version code danvinci 002 a k a codex the latex version very powerful the results are as shown below model prompt personalities detailed analysis text danvinci 001 mbti prompt txt enfj a link https drive google com file d 19akl275gxl7kcpj zohpxhxguc4wby9u view usp sharing code danvinci 001 mbti prompt txt enfp a link https drive google com file d 1xwhgzfczhwx9mi4zg1qt34t5pqu 0xaq view usp sharing code danvinci 002 mbti prompt txt entj a link https drive google com file d 1ril vw9d09ugyeya3jhskyraqfcobnzk view usp sharing code danvinci 002 mbti prompt intj simple txt istj a link https drive google com file d 12z7nf9ztukdqm4py3twoj5i xfdsyr v view usp sharing code danvinci 002 mbti prompt intj txt istj a link https drive google com file d 1rbx dinndtpluwezn7ynab0dhdilayv view usp sharing citation just kidding if you find our work helpful please cite as article llms16personalities title do large language models have personalities doge author tianbao xie zhoujun cheng chen henry wu xiang gao year 2022 note happy labour day | ai |
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g10-simon-says | pshs mc grade 10 simon says pager c arduino documentation of my pshs mc grade 10 2015 16 embedded systems program design course project on c arduino | os |
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Subjective-answer-evaluation | subjective answer evaluation this is an example of using natural language processing for evaluating answer scripts i have used rake to extract keywords and nltk for natural language processing gui is built using appjar | ai |
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shinysdr | shinysdr shinysdr is the software component of a software defined radio receiver when combined with suitable hardware devices such as the rtl sdr hackrf or usrp it can be used to listen to or display data from a variety of radio transmissions more about shinysdr https shinysdr switchb org installing shinysdr https shinysdr switchb org manual installation copyright and license copyright 2013 2014 2015 2016 2017 2018 2019 kevin reid lt kpreid switchb org gt shinysdr is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation either version 3 of the license or at your option any later version shinysdr is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details you should have received a copy of the gnu general public license along with shinysdr if not see http www gnu org licenses additional information the file shinysdr i webstatic client map basemap geojson gz was derived from the natural earth data set ne 50m admin 0 countries version 2 0 0 http www naturalearthdata com downloads 50m cultural vectors this data set is in the public domain http www naturalearthdata com about terms of use the aprs symbol graphics and descriptions used are from various sources and collected by heikki hannikainen https github com hessu aprs symbols see author credits and licensing information https github com hessu aprs symbols blob master copyright md | sdr rtl-sdr gnuradio amateur-radio | front_end |
bible-nlp-framework | bible natural language processing this repository contains a framework for performing natural language processing on the books of the bible currently there is only a jupyter https jupyter org notebook and a zip archive with one translation in it nasb more will be coming soon in the notebook you can specify which book of the bible you would like to analyze and it will give output like this nlp analysis for genesis using the nasb translation sentiment polarity 0 10086 subjectivity 0 45558 most common noun phrases in genesis 199 god 182 jacob 156 joseph 141 lord 133 abraham 93 pharaoh 81 isaac 77 esau 77 egypt 58 abram polarity is measured on a scale of 1 0 1 0 and measures whether that language used by the author is negative neutral or positive subjectivity is measured on a scale of 0 0 1 0 and measures how subjective the text is 0 0 being very objective 1 0 being very subjective | bible nlp linguistic natural-language-processing | ai |
Unity-ComputerVisionSim | unity computervisionsim computer vision simulation in unity this repo contains a modified version of unity s ml imagesynthesis https bitbucket org unity technologies ml imagesynthesis src master project and can save depth segmentation and normals within the editor tutorial companion tutorials for this repo unity depth camera simulation http www immersivelimit com tutorials unity depth camera simulation unity image segmentation http www immersivelimit com tutorials unity image segmentation | ai |
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jomi-challenge-frontend | jomi code challenge front end please check instructions for challenge on https github com jomijournal jomi cms challenge backend | front_end |
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iotex-dapp-sample | iotex dapp sample v3 a href https iotex io devdiscord target blank img src https github com iotexproject halogrants blob 880eea4af074b082a75608c7376bd7a8eaa1ac21 img btn discord svg height 36px a 8861650093939 pic hd https user images githubusercontent com 448293 171796205 937711d1 e336 4770 9388 ec0b02de3b89 jpg this is a boilerplate template for making your awesome dapp on iotex and eth bsc and other possible chains request here https github com iotexproject iotex dapp sample v2 issues new technology used in this template are next https github com vercel next js react https reactjs org trpc https trpc io typescript https www typescriptlang org mobx https mobx js org readme html matine https mantine dev core theme icon cypress https www cypress io intro a starter for react with typescript with the fast vite and the beautiful matine tested with the powerful cypress cheat sheet here s a cheat sheet list to help you get started quickly ts import rootstore usestore from store index const god usestore or const god rootstore god god isconnect god currentchain god currentchain chainid for current connected chain id god currentchain coin eth bnb iotx god currentchain coin balance current balance see chainstate god currentnetwork god currentnetwork account for current connected account address see networkstate god setshowconnecter to show close the wallet selector god currentnetwork loadbalance to load chain coin balance await rpc query uniswaprouter calls address 0x95cb18889b968ababb9104f30af5b310bd007fd8 chainid 4689 swap args selltoken busd b buytoken 0xb8744ae4032be5e5ef9fab94ee9c3bf38d5d2ae0 buyamount recipient 0x2acb8663b18d8c8180783c18b88d60b86de26df2 offlineprice true amount true data true router true path address true symbol true decimals true totalsupply true generate sdk npm i smartgraph cli g pnpm dev smartgraph codegen l http localhost 3000 api graphql o src lib installation clone the repo and run pnpm install start after the successfull installation of the packages pnpm dev | server |
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Lion | lion lion kind li ng visi on intelligence within large language models codes and details will be released soon framework framework png visual encoder vit g 14 from eva clip q former chinesebert llm internlm 7b flamingo gating cross attention ffn demo a href https c510b50d6da718747b gradio live demo link a recommend using english for questioning mme benchmark mme https github com bradyfu awesome multimodal large language models tree evaluation a comprehensive benchmark for multimodal large language models evaluation mme evaluates perception and cognition abilities through 14 subtasks existence count position color poster celebrity scene landmark artwork ocr commonsense reasoning numerical calculation text translation and code reasoning we achieves sotas on overall perception cognition performance evaluation p align center overall performance p div align center rank model version score 1 ours ours 1991 5 2 internlm xcomposer vl internlm 7b 1919 5 3 qwen vl chat qwen 7b 1848 3 4 mmicl flant5xxl 1810 7 5 skywork mm skywork mm 13b 1775 5 div p align center img src evaluation mme perception png width 600 p p align center img src evaluation mme cognition png width 600 p | ai |
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nsvue-workshop | home true heroimage logo nsvue png actiontext get started actionlink docs features title learn the basics details learn how to build a mobile app for android and ios using nativescript vue a custom implementation of vue js that allows you to create mobile apps using the skills you already possess title build an app details build a mobile app using the nativescript playground an interactive learning experience title extend your knowledge details put your knowledge into practice with extra challenges footer mit licensed copyright 2018 present jen looper | front_end |
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mPaaS | mpaas h5 10 1 60 30 https img alicdn com tfs tb1v2y8vkl2gk0jszfmxxc7ixxa 2560 1000 png mpaas hybrid app web hybrid api mpaas crash anr jsapi hybrid app hybrid app html5 h5 zip html css js h5 hybrid app mpaas uc android hybrid app html5 uc webview anr webview js css app app native html5 app apk mpaas config macos https tech antfin com docs 2 99044 chrome 4 3 mpaas https www aliyun com product mpaas spm 5176 224200 h2v3icoap 455 5d716ed6zl3rpw aly as j7wb1l5q android mpaas inside mpaas inside https gw alipayobjects com mdn site comm afts file a xzilqj onfsaaaaaaaaaaabkarqnaq mpaas nebula mpaas nebula https gw alipayobjects com mdn site comm afts file a krtrram3pxaaaaaaaaaaaabkarqnaq https gw alipayobjects com mdn site comm afts file a e5pmrpunrjcaaaaaaaaaaabkarqnaq ios mpaas nebula mpaas nebula https gw alipayobjects com mdn site comm afts file a iwoxsqm6sbgaaaaaaaaaaabkarqnaq mpaas nebula mpaas nebula https gw alipayobjects com mdn site comm afts file a y02kq6c9gfkaaaaaaaaaaabkarqnaq mpaas https gw alipayobjects com mdn site comm afts file a u whq51gb6iaaaaaaaaaaabkarqnaq uc uc key https tech antfin com docs 2 112551 mpaas https tech antfin com docs 2 130789 https img alicdn com tfs tb1ajf4vuz1gk0jszlexxb9kvxa 2560 641 png issue bug issue https github com alipay mpaas demo issues mpaas https img alicdn com tfs tb1rbf9vhy1gk0jsztexxxdqvxa 2560 1000 jpg this project is under the apache 2 0 license see the license https github com alipay mpaas demo blob master license file for the full license text copyright c 2015 present ant financial services group 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 disclaimer https github com alipay mpaas demo blob master disclaimer md | paas mobile-development | front_end |
campus-guidebook-android | campus guidebook the campus guidebook app aims at helping current students and people interested in cascadia find their way to events and destinations customers new cascadia students current cascadia students heads of clubs and programs personas as new students of cascadia you may find it hard to navigate the campus grounds and buildings our application will help you find your way along with helping you get connected with the many clubs offered at cascadia like new students current students can find it hard to know where events take place because of the variety of clubs offered at cascadia it can be confusing finding the meetings our application will tell you the date time and location on the map where the event will take place you are the leader of the sustainability club you want to get the word out about an upcoming event the guidebook application will allow you to add an event and get the word out pain points and user needs lack of ability to find events is a problem events on the cascadia webpage are hard to find event locations can be hard to find sustainability features of the campus are not well known competitive analysis strength weaknesses opportunities threats summary of top competitive apps implementation user stories prioritized list of functional components that describe the implementation from an end user perspective p0 main navigation login event page event info page sustainability page p1 google maps calendar p2 notifications quiz page class navigation usability summary develop navigation adding events page adding club events pages adding sustainability features page making the campus sustainability logo more consistent throughout the app walk through link to high fidelity prototype https www figma com file qnaexrg8advsngzqpfzayz campus guidebook screens screen https github com mobileapps cascadia campus guidebook android blob add readme app src main screens1 png app icon app icon https github com mobileapps cascadia campus guidebook android blob add readme app src main ic launcher playstore png color primary color f5f5f6 secondary color 002f6c primary text color 000000 secondary text color ffffff future considerations usability summary we are planning to expand the app s features by developing location based services and event notifications we also are planning to expand the groups at cascadia that can add information and events to the application we would like to add more sustainability information pages and a page to add events on clubs we would like to add a one button click to add an event to your calendar | front_end |
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suleiman-depl | suleiman depl this deployment consists of a microservice app a simple profile web page prometheus and grafana all running in a kubernetes cluster kindly find the proof of my work in the proof of work directory current structure 1 eks cluster tf scripts contains the terraform scripts that would be used by jenkins to deploy the kubernetes cluster on aws 2 jenkinsfiles contains the three jenkinsfile three because i made my deployment run in three build which are very dependent on one another 3 jenkins server tf scripts contains the terraform scripts to deploy an ec2 instance that would run the jenkins server 4 k8s manifests contains the scripts manifest of the monitoring app alerting app i never fully configured though sock shop microservice app and my personal webpage 5 delete resources jenkinsfile is my way of deleting everything created on aws by jenkinsfile pre requisite for the build 1 a s3 bucket must be created which matches the bucketname in the eks cluster tf scripts backend tf and jenkins server tf scripts backend tf files 2 a key must be created which matches with keyname in the jenkins server tf scripts jenkins server tf file 3 awscli must be installed terraform too 4 aws access key id and secret access key must be generated in other to configure awscli to have access to the aws account how to build pre requisite duplicate the jenkins server tf elsewhere because when you run terraform init a terraform dir would be created which would make it impossible to push to github and also to make your github code clean from unwanted files 1 run the jenkins server tf sccript on any laptop that has the above pre requites all installed and set up a cd into the jenkins server tf dir and run the following commands terraform init terraform apply auto approve b wait until the deployment is complete and copy the ip address of the instance created on aws jenkins will be running would be running on that instance already but to access it you will have to append the port 8080 to the ip address you copied from the output jenkins run on port 8080 e g 10 100 256 56 8080 c when jenkins load up you will be asked for a default password which can be found in a path shown on the welcome page of jenkins you will need to ssh into the server from the terminal which is the reason for the key pem generated from aws d upon successful login you will need to set up jenkins 2 after setup you will need to create three 3 credentials a github credentials using the username with password type b aws access key using the secret text type of credentials the id here must match this aws access key id and the secret is your access key c aws secret access key using the same secret type of credentials the id must match this too aws secret access key and the secret is your aws secret access key ps navigate to manage jenkins on your dashboard scroll down and select manage credentials you will click on the little blue text clearly written as global to find the page to create credentials then click on a button with the inscription add credentials 3 after creating credentials you will need to create pipelines i have three jenkinsfile which means you will need to create three pipeline this is because there are some dependencies when deploying the monitoring app some importation of dashboard a click on new item on the dashboard enter a pipeline name choose the pipeline type of pipeline and click ok b leave all defaults scroll down to where to select the pipeline script from select pipeline from source control manager scm fill in the details that appears next ps you will need a github url here that is where jenkins would pull the scripts instructions build from you can fork this repo or make a clone c follow same steps when creating deployment 2 and deployment 3 but this time around make sure you select a build trigger deployment 2 should be triggered by successful deployment of deployment of 1 and also select quiet period and add time to me it about 300secs do same with deployment 3 but it should triggered by deployment 2 and quiet period should be about 120secs d when the deployment are completed you can check your aws gui and navigate to load balancer there you would find 6 lb 2 of which are internal and 4 internet facing it is from those 4 lb you would be able to access the sock shop app my app prometheus and grafana e you can now navigate to route53 amd set up a hosted zone then create some record set with the load balancer s dns setting it to cname type also not forget to make the changes in your domain name provider e g namecheap f doing this you should now be able to view the applications from your set sub domain domain names deleting resources a copy the content of the delete resources jenkinsfile file and replace it with the jenkinsfile1 content b push the changes to github c then disable deployment 2 and 3 d then click on build on deployment 1 and everything shall be destroyed e to destroy the jenkins server you will need to go back to the directory where you had a copy of the jenkins server tf dir open a terminal in the jenkins server tf directory and run the command terraform destroy auto approve and the jenkins server shall to be deleted | cloud |
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arabicnlp | arabic natural language processing build status https travis ci com adhaamehab arabicnlp svg branch develop https travis ci com adhaamehab arabicnlp arabic nlp is a python package that provides an implementation for natural language processing tasks for arabic language such as part of speech tagging sentiment analysis text similarity and more this projetc works as the backbone for textblob ar https github com adhaamehab textblob ar books imgs cover jpeg installation shell pip install arabicnlp usage python from arabicnlp import tags tokens stem tags part adp part num sym adp sym num sym num num adp adp num sym sym intj part sym adp tokens stem arabicnlp arabicnlp is a natural language processing package for python developer provides a minimal interface for most of basic algorithms current release has tokenization stemming and lemmatization part of speech tagger known issue tagger randomly some words that exists in word2index get msilabeled as pad blogs building the project https adhaamehab me 2019 02 01 gp docs html building an arabic part of speech based on sequence modeling https towardsdatascience com deep learning for arabic part of speech tagging 810be7278353 contact adhaamehab http github com adhaamehab license mit license | nlp ml arabic arabic-nlp keras postagging part-of-speech-tagger sequence-modeling | ai |
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transfer-learning-for-nlp | transfer learning for natural language processing companion repository to paul azunre s transfer learning for natural language processing book https tinyurl com 47p8zzrv preliminary instructions please note that this version of the repo follows a recent significant reordering of chapters if you are looking for the original outdated ordering used during most of meap please refer to this repo version https github com azunre transfer learning for nlp tree 57e3a316d51b7f7274ddff12be0fc3e0a2d77029 watch the following intro video first intro video play intro video png raw true https www youtube com watch v wierqdfc5jm rendered jupyter notebooks are organized in folders by chapter in this repo with each folder containing a corresponding kaggle image requirements txt file representing kaggle docker image pip dependency dump at the time of their latest succesful run by the author please note that this requirements file is for the purpose of documenting the environment on kaggle on which the results reported in the book were achieved if you try to configure an environment different from kaggle say your local apple machine by pip installing directly from this file you will likely run into errors in that case this requirements file should only be used as a guide and you can t expect it to work straight out of the box due to many potential architecture specific dependency conflicts most of these requirements would not be necessary for local installation and this isn t a usage mode we support even if it is certainly a good real world skill building exercise to go through if you do decide to work locally we recommend anaconda ideally run these notebooks directly on kaggle where notebooks are already hosted this will require little to no setup on your part be sure to hit copy and edit kernel at the top right of each kaggle kernel page after creating an account to get going right away warning please make sure to copy and edit notebook on kaggle i e fork it to use compatible library dependencies do not create a new notebook and copy paste the code this will use the latest pre installed kaggle dependencies at the time you create the new notebook and the code will need to be modified to make it work also make sure internet connectivity is enabled on your notebook see appendix a of book for a tutorial introduction to various features of kaggle companion notebooks the following is a list of notebooks that have been hosted their corresponding chapters and kaggle links chapter s description kaggle link 2 3 linear tree based models for email sentiment classification https www kaggle com azunre tlfornlp chapters2 3 imdb traditional 2 3 linear tree based models for imdb movie review classification https www kaggle com azunre tlfornlp chapters2 3 spam traditional 2 3 elmo for email semantic classification https www kaggle com azunre tlfornlp chapters2 3 spam elmo 2 3 elmo for imdb movie review classification https www kaggle com azunre tlfornlp chapters2 3 imdb elmo 2 3 bert for email semantic classification https www kaggle com azunre tlfornlp chapters2 3 spam bert 2 3 tensorflow 2 bert for email semantic classification https www kaggle com azunre tlfornlp chapters2 3 spam bert tf2 2 3 bert for imdb movie review classification https www kaggle com azunre tlfornlp chapters2 3 imdb bert 4 imdb review classification with word2vec and fasttext https www kaggle com azunre tlfornlp chapter4 imdb word embeddings 4 imdb review classification with sent2vec https www kaggle com azunre tlfornlp chapter4 imdb sentence embeddings 4 dual task learning of imdb and spam detection https www kaggle com azunre tlfornlp chapter4 multi task learning 4 domain adaptation of imdb classifier to new domain of book review classification https www kaggle com azunre tlfornlp chapter4 domain adaptation 5 6 using simon for column data type classification on baseball and bc library openml datasets https www kaggle com azunre tlfornlp chapters5 6 column type classification 5 6 using elmo for fake news detection classification https www kaggle com azunre tlfornlp chapters5 6 fake news elmo 7 transformer self attention visualization and english twi translation example with transformers library https www kaggle com azunre tlfornlp chapter7 transformer 7 gpt and dialogpt in transformers library application to chatbot and open ended text generation https www kaggle com azunre tlfornlp chapter7 gpt 7 11 gpt neo aims to be open gpt 3 equivalent in transformers library https www kaggle com azunre tlfornlp chapter7 gpt neo 8 applying bert to filling in the blanks and next sentence prediction https www kaggle com azunre tlfornlp chapter8 bert 8 fine tuning mbert on monolingual twi data with pre trained tokenizer https www kaggle com azunre tlfornlp chapter8 mbert tokenizer fine tuned 8 fine tuning mbert on monolingual twi data with tokenizer trained from scratch https www kaggle com azunre tlfornlp chapter8 mbert tokenizer from scratch 9 implementing ulmfit adaptation strategies with fast ai v1 https www kaggle com azunre tlfornlp chapter9 ulmfit adaptation 9 implementing ulmfit adaptation strategies with fast ai v2 https www kaggle com azunre tlfornlp chapter9 ulmfit adaptation fast aiv2 9 demonstrating advantages of knowledge distillation i e distilbert with hugging face transformers library https www kaggle com azunre tlfornlp chapter9 distillation adaptation 10 demonstrating advantages of cross layer parameter sharing and embedding factorization i e albert with hugging face transformers https www kaggle com azunre tlfornlp chapter10 albert adaptation 10 fine tuning bert on a single glue task of measuring sentence similarity i e sts b https www kaggle com azunre tlfornlp chapter10 multi task single glue 10 fine tuning bert on a multiple glue tasks sequential adaptation first to a data rich question similarity scenario qqp followed by adaptation to a sentence similarity scenario sts b https www kaggle com azunre tlfornlp chapter10 multi task glue sequential 10 using pretrained adapter modules with adapterhub instead of fine tuning https www kaggle com azunre tlfornlp chapter10 adapters appendix b using tensorflow v1 v2 and pytorch to compute a function and its gradient via automatic differentiation https www kaggle com azunre tl for nlp appendixb to reiterate just hit copy and edit kernel at the top right of each kaggle kernel page after creating an account to get going right away note that for gpu enabled notebooks your free kaggle gpu time is limited to 30 40 hours week in 2020 with the clock resetting at the end of each friday be cautious and shut such notebooks down when not needed when debugging non gpu critical parts of the code etc kaggle frequently updates the dependencies i e versions of the installed libraries on their docker images to ensure that you are using the same dependencies as we did when we wrote the code so that the code works with minimal changes out of the box please make sure to select copy and edit kernel for each notebook of interest if you copy and paste the code into a new notebook and don t follow this recommended process you may need to adapt the code slightly for the specific library versions installed for that notebook at the time you created it this also applies if you elect to install a local environment for local installation pay attention to the frozen dependency requirement list we have shared in the companion repo which will guide you on which versions of libraries you will need moreover most of the requirements will not be necessary for local installation note about tensorflow 1 versus tensorflow 2 finally please note that because elmo has not yet been ported to tensorflow 2 x at the time of this writing we are forced to use tensorflow 1 x to compare it fairly to bert to confirm whether that is still the case you can check the following link https tfhub dev s q elmo we have however provided a notebook illustrating of how to use bert with tensorflow 2 x for the spam classification example https www kaggle com azunre tlfornlp chapters2 3 spam bert tf2 we transition in later chapters from tensorflow and keras to the hugging face transformers library that library uses latest dependendies in the backend including tensorflow 2 x if you prefer it over pytorch you could view the exercise in chapters 2 and 3 as a historical record of and experience with early packages that were developed for nlp transfer learning this exercise simultaneously helps you juxtapose tensorflow 1 x with 2 x appendix b of the book will also help with this by outlining major differences between tensorflow 1 x and 2 x | ai |
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Monk_Gui | monk gui http hits dwyl io tessellate imaging monk gui svg http hits dwyl io tessellate imaging monk gui https tokei rs b1 github tessellate imaging monk gui https tokei rs b1 github tessellate imaging monk gui category files a graphical user interface for deep learning and computer vision over monk libraries br br br alt text complete gif br br backend libraries a monk https github com tessellate imaging monk v1 monk is a low code deep learning tool and a unified wrapper for computer vision b monk object detection https github com tessellate imaging monk object detection a one stop repository for low code easily installable object detection pipelines check licenses of each pipeline before using br br br installation sudo apt get install python3 6 python3 6 dev python3 7 python3 7 dev python3 pip sudo pip install virtualenv virtualenvwrapper echo e n virtualenv and virtualenvwrapper bashrc echo export workon home home virtualenvs bashrc echo export virtualenvwrapper python usr bin python3 bashrc echo source usr local bin virtualenvwrapper sh bashrc source bashrc mkvirtualenv p usr bin python3 6 monk gui workon monk gui pip install numpy pyqt5 tqdm matplotlib pillow br br br running gui workon monk gui python gui py walk throughs classification demo 1 link https 1drv ms v s arxzfug4njmtkfg uovakoc wesz e uopbeg classification demo 2 link https 1drv ms v s arxzfug4njmtknlyptnndmceu0wu e xisndj object detection demo link https 1drv ms v s arxzfug4njmtkfkgm6r5mlfrbcur e mo48y8 author tessellate imaging https www tessellateimaging com check out monk ai https github com tessellate imaging monk v1 monk features low code unified wrapper over major deep learning framework keras pytorch gluoncv syntax invariant wrapper enables developers to create manage and version control deep learning experiments to compare experiments across training metrics to quickly find best hyper parameters to contribute to monk ai or monk object detection repository raise an issue in the git repo or dm us on linkedin abhishek https www linkedin com in abhishek kumar annamraju akash https www linkedin com in akashdeepsingh01 br br br copyright copyright 2019 onwards tessellate imaging private limited licensed under the apache license version 2 0 the license you may not use this project s files except in compliance with the license a copy of the license is provided in the license file in this repository | hacktoberfest python3 deeplearning machinelearning graphical-user-interface | ai |
iotc-device-bridge | page type sample description a sample that shows how to create a device bridge to connect other iot clouds such as sigfox particle and the things network to iot central languages javascript products azure iot central azure iot urlfragment iot central device bridge sample azure iot central device bridge this repository contains everything you need create a device bridge to connect other iot clouds such as sigfox particle and the things network ttn to iot central the device bridge forwards the messages your devices send to other clouds to your iot central app in your iot central app you can build rules and run analytics on that data create workflows in microsoft flow and azure logic apps export that data and much more this solution will provision several azure resources into your azure subscription that work together to transform and forward device messages through a webhook integration in azure functions to use the device bridge solution you will need the following an azure account you can create a free azure account from here https aka ms aft iot an azure iot central application to connect the devices create a free app by following these instructions https docs microsoft com azure iot central quick deploy iot central deploy to azure http azuredeploy net deploybutton png https portal azure com create microsoft template uri https 3a 2f 2fraw githubusercontent com 2fazure 2fiotc device bridge 2fmaster 2fazuredeploy json instructions for detailed instructions on how to deploy and configure the device bridge see use the iot central device bridge to connect other iot clouds to iot central https docs microsoft com en us azure iot central core howto build iotc device bridge limitations this device bridge only forwards messages to iot central and does not send messages back to devices due to the unidirectional nature of this solution settings and commands will not work for devices that connect to iot central through this device bridge because device twin operations are also not supported it s not possible to update device properties through this setup to use these features a device must be connected directly to iot central using one of the azure iot device sdks https docs microsoft com en us azure iot hub iot hub devguide sdks package integrity the template provided here deploys a packaged version of the code in this repository to an azure function you can check the integrity of the code being deployed by verifying that the sha256 hash of the iotc bridge az function zip file in the root of this repository matches the following 0988532d85ffc8d84c1c6c65d6edd5744ed2c695af8b9481172c705d542b5af7 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 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 label 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 updating the package the code in the repository is deployed to the azure function from the iotc bridge az function zip package at the repository root when updating the source code this package also needs to be updated and tested to update simply make a zip file from the iotcintegration folder that contains your source changes make sure to exclude non source files such as node modules to test your changes use the azuredeploy json arm template in the repository root change the packageuri variable to point to your modified zip package location zip package url can be obtained from your github branch and deploy the template in the azure portal make sure that the function deploys correctly and that you re able to send device data through the test tab in the azure portal updating the readme change this readme to document any user facing changes e g changes in the incoming payload format also update the sha256 hash in the package integrity section above with the hash of your new zip package for integrity verification | iot-central iot | server |
dscKeybusInterface-RTOS | dsc keybus interface rtos this library directly interfaces esp8266 microcontrollers to dsc powerseries http www dsc com dsc security products g powerseries 4 security systems for integration with home automation notifications on alarm events and usage as a virtual keypad this enables existing dsc security system installations to retain the features and reliability of a hardwired system while integrating with modern devices and software for under 5usd in components this repository is an esp open rtos https github com superhouse esp open rtos port of the arduino dsckeybusinterface library https github com taligentx dsckeybusinterface primarily to support direct native integration with apple homekit siri https www apple com ios home as a standalone accessory using esp homekit https github com maximkulkin esp homekit without using an intermediary like homebridge https github com nfarina homebridge hap nodejs https github com khaost hap nodejs etc apple home siri https www apple com ios home homekit https user images githubusercontent com 12835671 39588413 5a99099a 4ec1 11e8 9a2e e332fa2d6379 jpg why i had a dsc security system not being monitored by a third party service i wanted notification if the alarm triggered i was interested in finding a solution that directly accessed the pair of data lines that dsc uses for their proprietary keybus protocol to send data between the panel keypads and other modules tapping into the data lines is an ideal task for a microcontroller and also presented an opportunity to work with the arduino https www arduino cc and freertos https www freertos org via esp open rtos https github com superhouse esp open rtos platforms while there has been excellent discussion about the dsc keybus protocol https www avrfreaks net forum dsc keybus protocol and a few existing projects there were major issues that remained unsolved error prone keybus data capture limited data decoding there was good progress for armed disarmed states and partial zone status for a single partition but otherwise most of the data was undecoded notably missing the alarm triggered state read only unable to control the keybus to act as a virtual keypad no implementations to do useful work with the data poking around with a logic analyzer and oscilloscope revealed that the errors capturing the keybus data were timing issues updating the existing projects to fix this turned out to be more troublesome than starting from scratch so this project was born after resolving the data errors it was possible to reverse engineer the protocol by capturing the keybus binary data as the security system handled various events at this point this interface resolves all of the earlier issues and goes beyond my initial goal of simply seeing if the alarm is triggered features monitor the alarm state of all partitions alarm triggered armed disarmed entry exit delay fire triggered keypad panic keys monitor zones status zones open closed zones in alarm monitor system status ready trouble ac power battery panel time retrieve current panel date time and set a new date time virtual keypad send keys to the panel for any partition direct keybus interface does not require the dsc it 100 serial interface https www dsc com alarm security products it 100 20 20powerseries 20integration 20module 22 supported security systems dsc powerseries https www dsc com n enduser o identify verified panels pc585 pc1555mx pc1565 pc5005 pc5015 pc1616 pc1808 pc1832 pc1864 all powerseries series are supported please post an issue https github com taligentx dsckeybusinterface issues if you have a different panel pc5020 etc and have tested the interface to update this list rebranded dsc powerseries such as some adt systems should also work with this interface supported microcontrollers esp8266 development boards nodemcu v2 or v3 wemos d1 mini etc includes arduino framework support https github com esp8266 arduino and integrated wifi for 3usd shipped designed for reliable data decoding and performance pin change and timer interrupts for accurate data capture timing data buffering helps prevent lost keybus data if the program is busy extensive data decoding the majority of keybus data as seen in the dsc it 100 data interface developer s guide https cms dsc com download php t 1 id 16238 has been reverse engineered and documented in src dsckeybusprintdata rtos c https github com taligentx dsckeybusinterface rtos blob master src dsckeybusprintdata rtos c unsupported security systems dsc classic series pc1500 etc https www dsc com n enduser o identify use a different data protocol though support is possible dsc neo series use a higher speed encrypted data protocol corbus that is not possible to support honeywell ademco and other brands that are not rebranded dsc systems use different protocols and are not supported possible features prs welcome virtual zone expander add new zones to the dsc panel emulated by the microcontroller based on gpio pin states or software based states requires decoding the dsc pc5108 zone expander data installer code unlocking requires brute force checking all possible codes and a workaround if keypad lockout is enabled possibly automatically power cycling the panel with a relay to skip the lockout time dsc it 100 https cms dsc com download php t 1 id 16238 emulation dsc classic series support this protocol is already decoded https github com dougkpowers pc1550 interface use with this library would require major changes release notes 0 1 initial release examples the included examples demonstrate how to use the library and can be used as is or adapted to integrate with other software post an issue pull request if you ve developed and would like to share a program integration that others can use homekit integrates with apple homekit including the ios home app and siri this uses esp homekit https github com maximkulkin esp homekit to directly integrate with apple homekit siri https www apple com ios home as a standalone accessory without using an intermediary like homebridge https github com nfarina homebridge hap nodejs https github com khaost hap nodejs etc this demonstrates using the armed and alarm states for the homekit securitysystem object zone states for the contactsensor and motionsensor objects and fire alarm states for the smokesensor object status processes and prints the security system status to a serial interface including reading from serial for the virtual keypad this demonstrates how to determine if the security system status has changed what has changed and how to take action based on those changes post an issue pull request if you have a use for additional system states for now only a subset of all decoded commands are being tracked for status to limit memory usage partitions ready partitions armed away stay disarmed partitions in alarm partitions exit delay in progress partitions entry delay in progress partitions fire alarm zones open closed zones in alarm get set panel date and time keypad fire auxiliary panic alarm panel ac power panel battery panel trouble keybus connected keybusreader decodes and prints data from the keybus to a serial interface including reading from serial for the virtual keypad this can be used to help decode the keybus protocol and is also handy as a troubleshooting tool to verify that data is displayed without errors see src dsckeybusprintdata rtos c https github com taligentx dsckeybusinterface rtos blob master src dsckeybusprintdata rtos c for all currently known keybus protocol commands and messages issues and pull requests with additions corrections are welcome installation ubuntu 18 04 this example installs all components to the esp directory in your home directory esp create and change to a directory to contain all components cd mkdir esp cd esp install esp open sdk https github com pfalcon esp open sdk 1 install required packages esp sudo apt install make unrar free autoconf automake libtool gcc g gperf flex bison texinfo gawk ncurses dev libexpat dev python dev python python serial sed git unzip bash help2man wget bzip2 libtool bin 2 clone the esp open sdk repository esp git clone recursive https github com pfalcon esp open sdk git 3 build the esp open sdk toolchain this can take 10 15 minutes esp cd esp open sdk esp esp open sdk make toolchain esptool libhal standalone n install esp open rtos https github com superhouse esp open rtos esp esp open sdk cd esp esp git clone recursive https github com superhouse esp open rtos git add paths to esp open sdk and esp open rtos esp nano bashrc add the following with your actual username then save and close path path home myusername esp esp open sdk xtensa lx106 elf bin export sdk path home myusername esp esp open rtos esp source bashrc install dsckeybusinterface rtos 1 clone the dsckeybusinterface rtos repository esp git clone https github com taligentx dsckeybusinterface rtos git 2 navigate to one of the examples and edit the makefile to set the esp8266 serial port baud rate and flash size esp cd dsckeybusinterface rtos examples esp8266 keybusreader keybusreader nano makefile 3 build the example and flash the esp8266 check the example for specific usage documentation keybusreader make flash installation macos 10 11 this example installs all components to an esp disk image this is necessary if macos is installed with the default case insensitive file system create a disk image to contain all components hdiutil create documents esp dmg volname esp size 5g fs case sensitive hfs hdiutil mount documents esp dmg install homebrew https brew sh usr bin ruby e curl fssl https raw githubusercontent com homebrew install master install install esp open sdk https github com pfalcon esp open sdk 1 install required packages brew install binutils coreutils automake wget gawk libtool help2man gperf gnu sed grep 2 add a path to the homebrew installation of gnu sed nano bash profile add the following then save and close export path usr local opt gnu sed libexec gnubin path source bash profile 3 clone the esp open sdk repository cd volumes esp esp git clone recursive https github com pfalcon esp open sdk git 4 build the esp open sdk toolchain this can take 10 15 minutes esp cd esp open sdk esp open sdk make toolchain esptool libhal standalone n install esp open rtos https github com superhouse esp open rtos esp open sdk cd volumes esp esp git clone recursive https github com superhouse esp open rtos git add paths to esp open sdk and esp open rtos esp nano bash profile add the following then save and close path path volumes esp esp open sdk xtensa lx106 elf bin export sdk path volumes esp esp open rtos esp source bash profile install dsckeybusinterface rtos 1 clone the dsckeybusinterface rtos repository esp git clone https github com taligentx dsckeybusinterface rtos git 2 navigate to one of the examples and edit the makefile to set the esp8266 serial port baud rate and flash size esp cd dsckeybusinterface rtos examples esp8266 keybusreader keybusreader nano makefile 3 build the example and flash the esp8266 check the example for specific usage documentation keybusreader make flash wiring dsc aux 5v voltage regulator esp8266 development board 5v pin nodemcu wemos dsc aux esp8266 ground dscclockpin esp8266 d1 d2 d8 dsc yellow 15k ohm resistor 10k ohm resistor ground dscreadpin esp8266 d1 d2 d8 dsc green 15k ohm resistor 10k ohm resistor ground virtual keypad optional dsc green npn collector npn base 1k ohm resistor dscwritepin esp8266 d1 d2 d8 ground npn emitter the dsc keybus operates at 12 6v a pair of resistors per data line will bring this down to an appropriate voltage for each microcontroller esp8266 connect the dsc lines to gpio pins that are normally low to avoid putting spurious data on the keybus d1 gpio5 d2 gpio4 and d8 gpio15 virtual keypad uses an npn transistor and a resistor to write to the keybus most small signal npn transistors should be suitable for example 2n3904 bc547 bc548 bc549 that random npn at the bottom of your parts bin my choice power esp8266 development boards should use a 5v voltage regulator lm2596 based step down buck converter modules are reasonably efficient and commonly available for under 1usd shipped ebay aliexpress etc mp2307 based step down buck converter modules aka mini360 are also available but some versions run hot with an efficiency nearly as poor as linear regulators linear voltage regulators lm7805 etc will work but are inefficient and run hot these may need a heatsink connections should be soldered breadboards can cause issues virtual keypad this allows a program to send keys to the dsc panel to emulate the physical dsc keypads and enables full control of the panel from the program or other software keys are sent to partition 1 by default and can be changed to a different partition the following keys can be sent to the panel see the examples for usage keypad 0 9 arm stay requires access code if quick arm is disabled s arm away requires access code if quick arm is disabled w arm with no entry delay requires access code n fire alarm f auxiliary alarm a panic alarm p door chime enable disable c fire reset r quick exit x change partition partition number or set dscwritepartition to the partition number examples switch to partition 2 and send keys 2 1234 switch back to partition 1 1 set directly in program dscwritepartition 8 command output 1 command output 2 command output 3 command output 4 dsc configuration panel options affecting this interface configured by 8 installer code see the dsc installation manual for your panel for configuration steps pc1555mx 5015 section 370 pc1616 pc1832 pc1864 section 377 swinger shutdown by default the panel will limit the number of alarm commands sent in a single armed cycle to 3 for example a zone alarm being triggered multiple times will stop reporting after 3 alerts this is to avoid sending alerts repeatedly to a third party monitoring service and also affects this interface as i do not use a monitoring service i disable swinger shutdown by setting this to 000 ac power failure reporting delay the default delay is 30 minutes and can be set to 000 to immediately report a power failure notes memory usage can be adjusted based on the number of partitions zones and data buffer size specified in src dsckeybusinterface rtos h https github com taligentx dsckeybusinterface rtos blob master src dsckeybusinterface rtos h default settings esp8266 up to 8 partitions 64 zones 50 buffered commands pcb layouts are available in extras pcb layouts https github com taligentx dsckeybusinterface rtos tree master extras pcb 20layouts thanks to sjlouw https github com sj louw for contributing these designs support for other platforms depends on adjusting the code to use their platform specific timers in addition to hardware interrupts to capture the dsc clock this library uses platform specific timer interrupts to capture the dsc data line in a non blocking way 250 s after the clock changes this is necessary because the clock and data are asynchronous i ve observed keypad data delayed up to 160us after the clock falls troubleshooting if you are running into issues 1 run the keybusreader example program and view the serial output to verify that the interface is capturing data successfully without reporting crc errors if data is not showing up or has errors check the clock and data line wiring resistors and all connections 2 for virtual keypad run the keybusreader example program and enter keys through serial and verify that the keys appear in the output and that the panel responds if keys are not displayed in the output verify the transistor pinout base resistor and wiring connections 3 run the status example program and view the serial output to verify that the interface displays events from the security system correctly as partitions are armed zones opened etc references avr freaks dsc keybus protocol https www avrfreaks net forum dsc keybus protocol an excellent discussion on how data is sent on the keybus stagf15 dsc panel https github com stagf15 dsc panel a library that nearly works for the pc1555mx but had timing and data errors writing this library from scratch was primarily a programming exercise otherwise it should be possible to patch the dsc panel library dougkpowers pc1550 interface https github com dougkpowers pc1550 interface an interface for the dsc classic series | esp8266 home-security home-automation homekit iot dsc esp-open-rtos esp-open-sdk freertos | os |
gatsby-starter-carbon--deprecated | code climate https codeclimate com github vagr9k gatsby material starter badges gpa svg https codeclimate com github vagr9k gatsby material starter issue count https codeclimate com github vagr9k gatsby material starter badges issue count svg https codeclimate com github vagr9k gatsby material starter codacy badge https api codacy com project badge grade 990fb54ea8094f2aa0ed77f14e859820 https www codacy com app vagr9k gatsby material starter utm source github com amp utm medium referral amp utm content vagr9k gatsby material starter amp utm campaign badge grade div align center img src docs logo png alt logo width 200px height 200px div gatsby material starter a blog starter with material design in mind for gatsby https github com gatsbyjs gatsby demo website https vagr9k github io gatsby material starter screenshot docs screenshot png gatsbyjs v1 this starter is based on gatsbyjs v1 which brings progressive web app features such as automatic code and data splitting by route prefetching with service worker offline first support and prpl pattern more information in the announcement https www gatsbyjs org blog gatsby first beta release features blazing fast loading times thanks to pre rendered html and automatic chunk loading of js files react md https github com mlaursen react md for material design integrated fontawesome support integrated material icons support sass scss styling separate components for everything high configurability user information user social profiles copyright information more author segment name location description links follow me button posts in markdown code syntax highlighting embedded youtube videos embedded tweets tags seprate page for posts under each tag categories separate page for posts under each category suggested posts segment disqus support notifications about new disqus comments google analytics support responsive design on mobile disqus is loaded only after expanding comments for better performance social features twitter tweet button facebook share share count reddit share share count google share button linkedin share button telegram share button seo sitemap generation robots txt general description tags schema org jsonld google rich snippets opengraph tags facebook google pinterest twitter tags twitter cards rss feeds loading progress for slow networks offline support web app manifest support development tools eslint for linting prettier for code style remark lint for linting markdown write good for linting english prose gh pages for deploying to github pages codeclimate configuration file and badge note take a look at gatsby advanced starter https github com vagr9k gatsby advanced starter if you prefer building ui from scratch and or only interested in fundamental features you can also visit my personal blog https vagr9k me if you want to see a fully implemented blog based on this starter article screenshot docs screenshot article png mobile screenshot docs screenshot mobile png getting started install this starter assuming gatsby https github com gatsbyjs gatsby is installed by running from your cli sh gatsby new yourprojectname https github com vagr9k gatsby material starter npm run serve or you can fork the project make your changes there and merge new features when needed alternatively sh git clone https github com vagr9k gatsby material starter yourprojectname clone the project cd yourprojectname rm rf git so you can have your own changes stored in vcs npm install or yarn npm run serve configuration edit the export object in data siteconfig js module exports blogpostdir sample posts the name of directory that contains your posts sitetitle gatsby carbon components starter site title sitetitlealt gatsbyjs carbon components starter alternative site title for seo sitelogo logos logo 1024 png logo used for seo and manifest siteurl https ibm frontend github io domain of your website without pathprefix pathprefix gatsby starter carbon prefixes all links for cases when deployed to example github io gatsby starter carbon fixedfooter false whether the footer component is fixed i e always visible sitegatrackingid ua 47311644 4 tracking code id for google analytics postdefaultcategoryid tech default category for posts username carbon components user username to display in the author segment userlocation north pole earth user location to display in the author segment useravatar https api adorable io avatars 150 test png user avatar to display in the author segment userdescription lorem ipsum dolor amet celiac unicorn air plant you probably haven t heard of them occupy cornhole swag trust fund lo fi cardigan lumbersexual dreamcatcher raclette franzen bushwick polaroid portland crucifix deep v shoreditch aesthetic hella try hard echo park semiotics sartorial normcore retro vaporware selvage before they sold out wayfarers user description to display in the author segment links to social profiles projects you want to display in the author segment navigation bar userlinks label github url https github com https github com ibm frontend iconclassname fa fa github label twitter url https twitter com carbondesign iconclassname fa fa twitter copyright copyright 2018 carbon components user copyright string for the footer of the website you can also optionally set pathprefix js module exports note it must not have a trailing slash pathprefix gatsby carbon starter prefixes all links for cases when deployed to example github io gatsby material starter note user disqusshortname and copyright are optional and won t render if omitted warning make sure to edit static robots txt to include your domain for the sitemap theming edit src layouts theme scss to suit your needs you can use material color palette https react md mlaursen com customization colors provided by react md css import react md src scss react md md primary color md grey 400 md secondary color md red 800 md tertiary color md grey 300 | os |
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Natural-Language-Processing-In-Tensorflow-Course | nlp in tensorflow course this repository contains excercise notebooks of course 3 natural language processing in tensorflow of tensorflow in practice specialization download dataset for week 1 2 exercise notebook https storage googleapis com laurencemoroney blog appspot com bbc text csv download dataset for week 3 exercise notebook https storage googleapis com laurencemoroney blog appspot com training cleaned csv download dataset for week 4 exercise notebook https storage googleapis com laurencemoroney blog appspot com sonnets txt | natural-language-processing tensorflow-course tensorflow coursera-specialization python | ai |
Sakila-NoSQL | sakila nosql bdnosql sakila database implemented in pl sql and two non relational databases neo4j and mongodb developed as a project for bases de dados nosql course in computer software engineering at university of minho contributors diana barbosa https github com eowynpt francisco oliveira https github com tibblue marco silva https github com mas97 miguel lobo https github com mlobo1997 vitor peixoto https github com vitorpeixoto97 classification 18 0 | server |
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appium-with-ai-capability | appium with ai capability boilerplate code for bdd behavior driven development style mobile automation framework it also includes example of finding element using ai capability plugin https github com testdotai appium classifier plugin prerequisite before executing tests 1 install test ai classifier https github com testdotai appium classifier plugin plugin on your system follow the steps present on their page 2 install appium version atleast 1 9 2 beta 2 execute below command to install on linux node should be installed npm install g appium 1 9 2 beta 2 unsafe perm true allow root 3 i have configured it on mac 10 14 2 with below versions node v11 4 0 bdd framework go through the selenium bdd framework https github com shankybnl selenium bdd framework readme file to know about the structure it is designed on the similar lines keeping logic elements and tests in different files running a test mvn test how to leverage ai element finding capability in your existing framework 1 install test ai classifier https github com testdotai appium classifier plugin plugin on your system 2 install appium version atleast 1 9 2 beta 2 execute below command to install on linux node should be installed npm install g appium 1 9 2 beta 2 unsafe perm true allow root 3 add the below dependency and repository in your pom xml file repositories repository id jitpack io id url https jitpack io url repository repositories dependency groupid com github appium groupid artifactid java client artifactid version 969fac6a1c6dd229bdefb02ff10e71c812701e0c version dependency 4 below are the additional capabilities that should be present while creating driver this is required capability to use with test ai classifier plugin capabilities setcapability automationname uiautomator2 this capability determines what should be the lowest confidence to consider an element by default value is 0 2 this capability should be a number between 0 and 1 where 1 means confidence must be perfect and 0 means no confidence at all is required capabilities setcapability testaiconfidencethreshold 0 1 this directs appium to include extra information about elements while they are being found which dramatically speeds up the process of getting inputs to this plugin capabilities setcapability shouldusecompactresponses false passing reference of the plugin to appium hashmap string string customfindmodules new hashmap customfindmodules put ai test ai classifier capabilities setcapability customfindmodules customfindmodules 5 find the elements with the below syntax public by cartimagewithai mobileby custom ai cart public by notificationimagewithai mobileby custom ai notifications 6 check test ai classifier lib labels js file to get the list of predefined labels 7 if you wish to add your labels which can be used for your app here s the article https medium com testdotai training data for app classifier f217dc005523 to train the data for classifier plugin | front_end |
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Vuejs-Vuetify-UI-Design-Sprint-Report-System | sprint project setup npm install compiles and hot reloads for development npm run serve compiles and minifies for production npm run build lints and fixes files npm run lint customize configuration see configuration reference https cli vuejs org config | os |
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xicons | xicons license mit https img shields io badge license mit yellow svg https opensource org licenses mit english https github com 07akioni xicons blob main readme zh cn md include vicons vue3 ricons react sicons svg v2icons vue2 svg vue react components integrated from fluentui system icons https github com microsoft fluentui system icons ionicons https github com ionic team ionicons ant design icons https github com ant design ant design icons material design icons https github com google material design icons font awesome https github com fortawesome font awesome tabler icons https github com tabler tabler icons and carbon https github com carbon design system carbon tree main packages icons util icon component for customizing color size is also provided icons preview search https www xicons org installation icons installation bash install packages on your demand for react npm i d ricons fluent npm i d ricons ionicons4 npm i d ricons ionicons5 npm i d ricons antd npm i d ricons material npm i d ricons fa font awesome npm i d ricons tabler npm i d ricons carbon for vue3 npm i d vicons fluent npm i d vicons ionicons4 npm i d vicons ionicons5 npm i d vicons antd npm i d vicons material npm i d vicons fa font awesome npm i d vicons tabler npm i d vicons carbon for vue2 npm i d v2icons fluent npm i d v2icons ionicons4 npm i d v2icons ionicons5 npm i d v2icons antd npm i d v2icons material npm i d v2icons fa font awesome npm i d v2icons tabler npm i d v2icons carbon for svg file npm i d sicons fluent npm i d sicons ionicons4 npm i d sicons ionicons5 npm i d sicons antd npm i d sicons material npm i d sicons fa font awesome npm i d sicons tabler npm i d sicons carbon icon utils installation icon utils provide a icon wrapper component for customizing color size of the inner svg icon bash npm i d ricons utils react npm i d vicons utils vue3 npm i d v2icons utils vue2 usage for vue3 vue3 demo https codesandbox io s vicons demo sfzk9 file src app vue html script import money16regular from vicons fluent or import money16regular from vicons fluent money16regular you can directly use the svg component or wrap it in an icon component from vicons utils import icon from vicons utils export default components icon money16regular script template icon money16regular icon template q a vue3 how to create a function that accepts an icon component as input in typescript ts import type component from vue function f iconcomponent component for react react demo https codesandbox io s ricons demo 05dug file src app tsx tsx import money16regular from ricons fluent or import money16regular from ricons fluent money16regular you can directly use the svg component or wrap it in an icon component from ricons utils import icon from ricons utils function app return icon money16regular icon for vue2 vue2 demo https codesandbox io s v2icons demo xoeme file src app vue html script import money16regular from v2icons fluent or import money16regular from v2icons fluent money16regular you can directly use the svg component or wrap it in an icon component from v2icons utils import icon from v2icons utils export default components icon money16regular script template icon money16regular icon template for svg html img src sicons fluent money16regular svg utils api icon api an icon component in vicons utils ricons utils v2icons utils is provided for customizing color size of the inner svg icon prop type default description size string number size of the icon color string color of the icon tag string span tag to be rendered as for example tsx import icon from ricons utils react import icon from vicons utils vue3 import icon from v2icons utils vue2 render it icon color green size 48 someicon icon iconconfigprovider api iconconfigprovider will affect all the descendant icons default prop value prop type default description size string number size of the icon color string color of the icon tag string span tag to be rendered as for example tsx import iconconfigprovider icon from ricons utils react import iconconfigprovider icon from vicons utils vue3 import iconconfigprovider icon from v2icons utils vue2 render it iconconfigprovider color green size 48 app icon someicon icon app iconconfigprovider common issues too many open files this is because the count of opened files exceeds the limit of operation system use ulimit n to check the limit you can only increase the limit or import icons by path js import money16regular from ricons fluent money16regular icon utils packages package version description ricons utils npm version https badge fury io js 40ricons 2futils svg https badge fury io js 40ricons 2futils util icon components for react vicons utils npm version https badge fury io js 40vicons 2futils svg https badge fury io js 40vicons 2futils util icon components for vue3 v2icons utils npm version https badge fury io js 40v2icons 2futils svg https badge fury io js 40v2icons 2futils util icon components for vue2 icon packages vue3 package version vicons fluent npm version https badge fury io js 40vicons 2ffluent svg https badge fury io js 40vicons 2ffluent vicons ionicons4 npm version https badge fury io js 40vicons 2fionicons4 svg https badge fury io js 40vicons 2fionicons4 vicons ionicons5 npm version https badge fury io js 40vicons 2fionicons5 svg https badge fury io js 40vicons 2fionicons5 vicons antd npm version https badge fury io js 40vicons 2fantd svg https badge fury io js 40vicons 2fantd vicons material npm version https badge fury io js 40vicons 2fmaterial svg https badge fury io js 40vicons 2fmaterial vicons fa npm version https badge fury io js 40vicons 2ffa svg https badge fury io js 40vicons 2ffa vicons tabler npm version https badge fury io js 40vicons 2ftabler svg https badge fury io js 40vicons 2ftabler vicons carbon npm version https badge fury io js 40vicons 2fcarbon svg https badge fury io js 40vicons 2fcarbon react package version ricons fluent npm version https badge fury io js 40ricons 2ffluent svg https badge fury io js 40ricons 2ffluent ricons ionicons4 npm version https badge fury io js 40ricons 2fionicons4 svg https badge fury io js 40ricons 2fionicons4 ricons ionicons5 npm version https badge fury io js 40ricons 2fionicons5 svg https badge fury io js 40ricons 2fionicons5 ricons antd npm version https badge fury io js 40ricons 2fantd svg https badge fury io js 40ricons 2fantd ricons material npm version https badge fury io js 40ricons 2fmaterial svg https badge fury io js 40ricons 2fmaterial ricons fa npm version https badge fury io js 40ricons 2ffa svg https badge fury io js 40ricons 2ffa ricons tabler npm version https badge fury io js 40ricons 2ftabler svg https badge fury io js 40ricons 2ftabler ricons carbon npm version https badge fury io js 40ricons 2fcarbon svg https badge fury io js 40ricons 2fcarbon vue2 package version v2icons fluent npm version https badge fury io js 40v2icons 2ffluent svg https badge fury io js 40v2icons 2ffluent v2icons ionicons4 npm version https badge fury io js 40v2icons 2fionicons4 svg https badge fury io js 40v2icons 2fionicons4 v2icons ionicons5 npm version https badge fury io js 40v2icons 2fionicons5 svg https badge fury io js 40v2icons 2fionicons5 v2icons antd npm version https badge fury io js 40v2icons 2fantd svg https badge fury io js 40v2icons 2fantd v2icons material npm version https badge fury io js 40v2icons 2fmaterial svg https badge fury io js 40v2icons 2fmaterial v2icons fa npm version https badge fury io js 40v2icons 2ffa svg https badge fury io js 40v2icons 2ffa v2icons tabler npm version https badge fury io js 40v2icons 2ftabler svg https badge fury io js 40v2icons 2ftabler v2icons carbon npm version https badge fury io js 40v2icons 2fcarbon svg https badge fury io js 40v2icons 2fcarbon svg package version sicons fluent npm version https badge fury io js 40sicons 2ffluent svg https badge fury io js 40sicons 2ffluent sicons ionicons4 npm version https badge fury io js 40sicons 2fionicons4 svg https badge fury io js 40sicons 2fionicons4 sicons ionicons5 npm version https badge fury io js 40sicons 2fionicons5 svg https badge fury io js 40sicons 2fionicons5 sicons antd npm version https badge fury io js 40sicons 2fantd svg https badge fury io js 40sicons 2fantd sicons material npm version https badge fury io js 40sicons 2fmaterial svg https badge fury io js 40sicons 2fmaterial sicons fa npm version https badge fury io js 40sicons 2ffa svg https badge fury io js 40sicons 2ffa sicons tabler npm version https badge fury io js 40sicons 2ftabler svg https badge fury io js 40sicons 2ftabler sicons carbon npm version https badge fury io js 40sicons 2fcarbon svg https badge fury io js 40sicons 2fcarbon credit icon set license ant design icons https github com ant design ant design icons mit https opensource org licenses mit fluentui system icons https github com microsoft fluentui system icons mit https opensource org licenses mit font awesome https github com fortawesome font awesome cc by 4 0 license https creativecommons org licenses by 4 0 ionicons https github com ionic team ionicons mit https opensource org licenses mit material design icons https github com google material design icons apache 2 https github com google material design icons blob master license tabler icons https github com tabler tabler icons mit https opensource org licenses mit carbon https github com carbon design system carbon apache 2 https github com carbon design system carbon tree main packages icons | icons react vue | os |
Swift-AI | banner https github com swift ai swift ai blob master siteassets banner png swift ai is a high performance deep learning library written entirely in swift we currently offer support for all apple platforms with linux support coming soon tools swift ai includes a collection of common tools used for artificial intelligence and scientific applications x neuralnet https github com swift ai neuralnet a flexible fully connected neural network with support for deep learning optimized specifically for apple hardware using advanced parallel processing techniques convolutional neural network recurrent neural network genetic algorithm library fast linear algebra library signal processing library example projects we ve created some example projects to demonstrate the usage of swift ai each resides in their own repository and can be built with little or no configuration neuralnet mnist https github com swift ai neuralnet mnist a neuralnet https github com swift ai neuralnet training example for the mnist http yann lecun com exdb mnist handwriting database trains a neural network to recognize handwritten digits built for macos neuralnet handwriting ios https github com swift ai neuralnet handwriting ios a demo for handwriting recognition using neuralnet https github com swift ai neuralnet pre trained just download and run built for ios usage each module now contains its own documentation we recommend that you read the docs carefully for detailed instructions on using the various components of swift ai the example projects are another great resource for seeing real world usage of these tools compatibility swift ai currently depends on apple s accelerate https developer apple com library mac documentation accelerate reference acceleratefwref framework for vector matrix calculations and digital signal processing in order to provide support for more platforms alternative blas solutions are being considered contributing contributions to the project are welcome we simply ask that you strive to maintain consistency with the structure and formatting of existing code contact collin hundley https github com collinhundley is the author and maintainer of swift ai feel free contact him directly via email mailto collinhundley gmail com if you have a question about this library or are looking for guidance we recommend opening an issue https github com swift ai swift ai issues new so a member of the community can help consulting if you re looking for for help with deep learning computer vision signal processing or other ai applications you ve come to the right place contact collin https github com collinhundley for more information about consulting contracting donating your donation to swift ai will help us continue building excellent open source tools all contributions are appreciated donate https github com swift ai swift ai blob master siteassets donate png https www paypal com cgi bin webscr cmd donations business 3fcbz7mxzjfg2 lc us item name swift 20ai currency code usd bn pp 2ddonationsbf 3adonatebutton 2epng 3fraw 3dtrue 3anonhosted | swift machine-learning deep-learning artificial-intelligence ios macos ocr | ai |
BlueBerry | blueberry university software engineering project blueberry apple ios app store | os |
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Embedded-Systems-Wacky-Races | wacky races project img src wiki wacky races png alt original series width 1200 source http www ps1fun com wacky races description this project was inspired by the infamous cartoon series wacky races which involves designing unique vehicles for each character that match their personalities this project aimed to bring the series into an embedded systems reality the purpose of this assignment was to design build and program an embedded system using atmel sam4s8 microcontroller and surface mount technology the goal of this assignment was to build the wacky racer the driver board and wacky hat the vehicle controller in altium designer to populate the board using surface mount technologies smt and to program the two boards in c to communicate with each other via nrf24 radio module this project was split into two groups one group is responsible for the hat while the other work on the racer each group needed to design their board and work concurrently when collaboration is needed project outcome participate in a wacky race and battle against other groups to eliminate them from the race project diagram img src wiki wackyhat png alt hat width 1200 img src wiki wackyracer png alt racer width 1200 requirements wacky racer must design a 4 layer pcb of dimension 85mmx64mm use a usb interface for debugging use a serial wire debug interface for mcu programming debugging must include an addequate fusing and reverse polarity protection must have a sleep button have afour jumber selected radio channels interface with wacky hat board with a nordic nrf24 smd radio module must include a low battery led indicator must be decorated with an led tape controlled by the mcu must regulate the nominal battery voltage to 5v with adp2302ardz 50 drive the motors using h briges drv8833 use usb and jtag interfaces for debugging wacky hat must design a 4 layer pcb of dimension 85mmx64mm use a usb interface for debugging use a serial wire debug interface for mcu programming debugging must include an addequate fusing and reverse polarity protection must have a sleep button have afour jumber selected radio channels interface with wacky hat board with a nordic nrf24 smd radio module must include a low battery led indicator must be decorated with an led tape controlled by the mcu must regulate the nominal battery voltage to 5v with adp2302ardz 50 use i2c imu mpu 9250 for head motion detection use usb and jtag interfaces for debugging pcb design img src wiki main1 png alt doc 1 width 400 img src wiki main2 png alt doc 1 width 400 img src wiki 20201026 060809 jpg alt doc 1 width 400 img src wiki 20201026 060742 jpg alt doc 1 width 400 contributors hassan alhujhoj https github com hassan alhujhoj marcus hughan https github com marcushughan nathan james https github com natedawgsnz abdullah naeem https eng git canterbury ac nz ana104 license licensed under mit license license | system-on-chip internet-of-things embedded-systems | os |
MasteringRTOS | masteringrtos running freertos on arduino stm32f4x and cortex m based mcus | os |
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IoT-ConnectedCar | iot realized the connected car the internet of things iot is one of the hottest buzzwords in 2015 and for good reason the promise of leveraging data generated by machines that are interconnected can provide vast operational efficiencies in virtually every industry at pivotal s springone2gx conference 2014 a team from pivotal demonstrated how components of pivotal s open source portfolio can be combined to increase speed to market of iot solutions pivotal has developed a sample application to illustrate it s open source iot platform via the use case of a connected car in this demo data from a moving vehicle is streamed real time to a server running spring xd spring xd ingests the data and makes predictions about where the vehicle is going and how far it can go an html angularjs based dashboard provides real time analytics illustrating the current position of the car some of the basic telemetry available from the car as well as the predictions provided by pivotal s data science team this code repository consists of all of the source code used in the demo application s server deployment a separate repository for the ios application used will be open sourced at a future date you can view the video of the springone2gx demo online here springone2gx iot realized the connected car https www youtube com watch v cejq46iqpui architecture connected car architecture src main resources images connectedcararchitecture jpg there are three main prongs to the architecture 1 ingestion 2 batch training 3 realtime dashboard ingestion the source of the data for this project is the obd ii data from a moving vehicle in the live demo the ios application herbie was used to send live data to the server in offline demonstrations as well as testing recorded data was replayed via a spring batch job in the iot carsimulator module the foundation of the ingestion is spring xd using spring xd an ingestion stream was developed that accepted the inbound input performed minor transformations routed the data through the realtime predictions module and then persisted the output to hdfs the stream definition itself is as follows as seen in the iot scripts stream create xd script http filter script file datafilter groovy acmeenrich shell inputtype application json command python streampredict py workingdir iot connectedcar iot data science pythonmodel hdfs the above stream accepts json posted to the http source listening on the default port 9000 it then routes the data through a filter that determines if all the required data is available if required data is missing the record is dropped and the record and reason are logged if all the required data is available the message is passed onto an enrichment transformer that performs some basic enrichment and translation for data consistency the enriched data is then passed to the shell module that is running a python module developed by pivotal s data science team that python module looks at the current journey and using historical data for that particular vehicle s vin it creates a prediction of where it s going and it s expected mpg for the journey the results of the shell processor are stored in hdfs the previously mentioned stream is tapped via the following definition tap stream iot http shell typeconversiontransformer gemfire server regionname car position keyexpression payload vin this tap copies everything coming out of the shell processor and sends it to a transformer that transformer change the type from a simple string containing json to a pojo to be persisted into gemfire the transformed object is then sent to the gemfire server sink for persistence into gemfire so that it can be made available for the dashboard the modules involved in the ingestion of data in this project are the iot datascience iot datafilter iot enrichmenttransformer iot gemfirecommons and iot gemfiretransformer modules batch training before the data science module can make a prediction with the real time data a model must be generated off of the historical data of previous journeys the batch training occurs offline with the resulting model used as input to the predictive module in the ingest phase real time dashboard the real time dashboard is an html angularjs based application that is powered by the data in gemfire the iot gemfirerest module provides a set of rest apis that are called by the iot dashboard module to provide data about the selected vehicle s current location telemetry as well as the current state of the predictions project dependencies this is intended to illustrate a complete solution and as such has a few dependencies for it s environment specifically 1 java 8 or higher http www oracle com technetwork java javase downloads index html 2 spring xd https spring io projects spring xd 1 1 0 release or higher 3 hadoop install compatible with spring xd release 4 spark 1 2 or higher 5 gemfire 8 or higher 6 anaconda http continuum io downloads distribution of python 2 1 0 or higher 7 grunt cli v0 1 13 or higher building from source there are two main pieces needed to build this project from source 1 the dashboard 2 everything else building the dashboard the html angularjs based dashboard was originally created via a yeoman generator and therefore uses it s standard stack to perform builds given the rate of change with these tools we can only assure that the build works on the following versions at the time of the writing of this document node v0 10 31 npm 1 4 24 grunt v0 4 5 bower 1 4 1 the best place to get the build setup is via the yeoman website referenced in the dashboard s documentation with the various build tools installed installed you can build the dashboard via the following from the root of the iot dashboard module grunt clean build the output of this process will end up in iot dashboard src main resources public for spring boot packaging building everything else once you ve built the dashboard building the rest of the code is executed simply by executing gradlew clean build from the root of this project that will build all modules and package them up apropriately for deployment spring boot jars for the most part contributing to iot connectedcar here are some ways for you to get involved in the community create github https github com pivotal field engineering iot connectedcar issues tickets for bugs and new features and comment and vote on the ones that you are interested in github is for social coding if you want to write code we encourage contributions through pull requests from forks of this repository http help github com forking if you want to contribute code this way please familiarize yourself with the process outlined for contributing to pivotal projects here contributor guidelines https github com pivotal field engineering iot connectedcar blob master contributing md watch for upcoming articles on pivotal by subscribing http blog pivotal io feed to spring io | server |
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SAComputerVisionMachineLearning | sdc computer vision and machine learning projects udacity self driving car nanodegree https s3 amazonaws com udacity sdc github shield carnd svg http www udacity com drive computer vision and machine learning related projects of udacity s self driving car nanodegree program lane detection lanedetection camera based vehicle tracking camerabasedvehicletracking traffic sign classification trafficsignclassification behavioral cloning behavioralcloning object detection with ssd objectdetection semantic segmentation semanticsegmentation lane detection p align center img src lanedetection lane detection gif p camera based vehicle tracking p align center img src camerabasedvehicletracking vehicle tracking gif p traffic sign classification p align center img src trafficsignclassification traffic sign classification png p behavioral cloning p align center img src behavioralcloning behavioral cloning gif p object detection with ssd p align center img src objectdetection ssd gif p semantic segmentation p align center img src semanticsegmentation imgs test image png p license with the standards of the mit license license md you are more than welcomed to contribute to this repository | udacity udacity-nanodegree udacity-self-driving-car lane-detection vehicle-tracking traffic-sign-classification behavioral-cloning semantic-segmentation python notebook computer-vision deep-learning | ai |
dso-560-nlp-and-text-analytics | dso560 natural language processing text analytics week 1 cloning forking and syncing github repositories https www youtube com watch v vrxughmyhgq feature youtu be in class regex exercises answers https www youtube com watch v yddhl3ukuko feature youtu be lecture 1 https usc marshall panopto demo hosted panopto com panopto pages viewer aspx id 96340048 8fe1 46f3 adc7 ab7b001c3e6a week 2 pandas overview lecture 2 https usc zoom us rec share 51ti6ns3v9jfinq40t7wo5 eqjax6a81xdn8vifxb0 rpwlbutr0bthelxqrwu week 3 lecture 3 https usc zoom us rec share xmy2ppj59xnisynn u7iczx8h8f6x6a8gxnp qdzn0y pwmy4ho7kwao2edo8srh week 4 lecture 4 https usc zoom us rec share utmpcb7fzmflaa rzephuo0cayvhx6a8hcyy vvyyet5ipbqpmbbsdo88hgj5b9e | ai |
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MicroEmbeddedFileSystem | microembeddedfilesystem this file system is designed particularly for embedded system the model of file system is based on linux it consists of full fledged file system optional shell this file system is well optimized for embedded system | os |
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MSU-ECE-DSD | ece 4743 6743 digital systems design this site contains lab files for ece 4743 6743 digital systems design https github com bjones1 msu ece dsd if you see any bugs please report them https github com bjones1 msu ece dsd issues | os |
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Discord-BOT-Dashboard-V2 | h1 align center br p discord bot dashboard v2 p img src content headerimage png github all releases https img shields io github downloads lachlandev discord bot dashboard v2 total svg style for the badge https github com lachlandev discord bot dashboard v2 releases github release https img shields io github release lachlandev discord bot dashboard v2 svg style for the badge https github com lachlandev discord bot dashboard v2 releases github issues https img shields io github issues lachlandev discord bot dashboard v2 svg style for the badge https github com lachlandev discord bot dashboard v2 issues discordserver https img shields io discord 587842272167723028 label discord 20server logo discord colorb 5865f2 style for the badge logocolor white https discord com invite w7b5nkb h1 about discord bot dashboard v2 is the successor of a href https github com lachlandev discord bot dashboard target blank discord bot dashboard a discord bot dashboard v2 is made to make discord bot development easy designed to create applications without having to write a single line of code while using a user friendly web dashboard dashboard preview img src content dashprev jpg installation setup head over to the docs to find all the instructions for setting up and running discord bot dashboard v2 this can be found here https dbd lachlan dev com docs installing requirements download the latest version from releases https github com lachlandev discord bot dashboard v2 releases open up the root directory and run the following command bash npm install setting up bot rename config default json to config json and open up the file this can be found found in the config folder and input the required fields more info on these fields can be found on the docs page here https dbd lachlan dev com docs json clientid botclientid clientsecret botclientsecret callbackurl botcallbackurl admin useradminid token bottoken prefix port 3000 make sure to enable both privileged gateway intents on the discord developer dashboard https discord com developers this is to fix errors with kick ban commands starting the application open up the root directory and run the following command bash node index js you should now be able to access the dashboard at http localhost 3000 features a list of some of the features that are included in discord bot dashboard v2 authentication discord bot dashboard is locked with a secure authentication method that only allows users who are added into the config file to access the dashboard security discord bot dashboard ensures that your application is secure modern ui discord bot dashboard is built with a modern ui to ensure its ease of use for anyone open source discord bot dashboard is an open source project meaning anyone can contribute to make it even better stability running your application using discord bot dashboard ensures that it is stable and you wont have any errors 24 7 uptime running you application using discord bot dashboard allows you to have 24 7 uptime multiple tools discord bot dashboard is packed with multiple tools that are easy to use plugins develop and share plugins that can be imported into your project contribute if you would like to contribute to the project please open a pr pull request clearly showing your changes requirements node js https nodejs org en v12 3 1 or later issues if you have any issues feel free to open an issue or join the discord server https discord com invite w7b5nkb extra created by lachlandev 8014 website https lachlan dev com twitter https twitter com lachlandev instagram https www instagram com lachlandev discord server https discord com invite w7b5nkb marketplace https github com lachlandev discord bot dashboard marketplace br | bot statistics dashboard discord discord-js discordjs discord-bot discord-api discord-server discordbot discord-bot-dashboard discord-moderation-bot discord-bot-development authentication | front_end |
Awesome-LLM-Uncertainty-Reliability-Robustness | awesome llm uncertainty reliability robustness awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com hee9joon awesome diffusion models license mit https img shields io badge license mit green svg https opensource org licenses mit made with love https img shields io badge made 20with love red svg https github com chetanraj awesome github badges this repository called ur2 llms contains a collection of resources and papers on uncertainty reliability and robustness in large language models large language models have limited reliability limited understanding limited range and hence need human supervision michael osborne professor of machine learning in the dept of engineering science university of oxford january 25 2023 welcome to share your papers thoughts and ideas in this area contents resources resources introductory posts introductory posts technical reports technical reports tutorial tutorial papers papers evaluation and survey evaluation and survey uncertainty uncertainty uncertainty estimation uncertainty estimation calibration calibration ambiguity ambiguity confidence confidence active learning active learning reliability reliability hallucination hallucination truthfulness truthfulness reasoning reasoning prompt tuning optimization and design prompt tuning optimization and design instruction and rlhf instruction and rlhf tools and external apis tools and external apis robustness robustness invariance invariance distribution shift distribution shift out of distribution out of distribution adaptation and generalization adaptation and generalization adversarial adversarial attribution attribution causality causality safety safety bias and fairness bias and fairness privacy privacy resources introductory posts gpt is an unreliable information store noble ackerson link https towardsdatascience com chatgpt insists i am dead and the problem with language models db5a36c22f11 20 feb 2023 comments large language models are unreliable information stores what can we do about this by design these systems do not know what they do or don t know gpt is trained on massive amounts of text data without any inherent ability to verify the accuracy or truthfulness of the information presented in that data so should we build on top of factually unreliable gpts yes though when we do we must ensure we add the appropriate trust and safety checks and the practical constraints through techniques i ll share below when building atop these foundational models we can minimize inaccuracy using proper guardrails with techniques like prompt engineering and context injection or if we have our own larger datasets more advanced approaches such as transfer learning fine tuning and reinforcement learning are areas to consider nice blog misusing large language models and the future of mt arle lommel link https csa research com blogs events blog misusing large language models and the future of mt 20 dec 2022 1 large language models make the trust problem worse despite the expectation that large language models would lead to the next wave of dramatic improvement in mt they introduce some serious risks one of the biggest challenges for mt now is that it is not reliable although the development of responsive and responsible mt should improve this large language models that can produce convincing sounding output that is nonsense are likely to increase the risk of dangerous or harmful translation errors my experiments showed that users should not trust what galactica says at face value but instead need to examine it carefully to verify everything note that this problem will be worse in languages with relatively little training data in these models 6 quality estimation will become key as the output of mt becomes more fluent detecting problems will become increasingly difficult which can a raise the risk that content can pose and b increase the cognitive load for mt editors and thereby decrease their efficiency this means that quality estimation will become more important requiring breakthroughs in this area when the technology can reliably identify problems and risk it will address the trust problem large language models the basics and their applications margo poda link https www moveworks com insights large language models strengths and weaknesses 9 feb 2023 reliability needs human supervison which is the key prompt engineering improving responses reliability peter foy link https www mlq ai prompt engineering techniques improve reliability 19 mar 2023 nice blog openai s cookbook on techniques to improve reliability openai github https github com openai openai cookbook 18 mar 2023 gpt calibration tag gwern branwen link https gwern net doc ai nn transformer gpt calibration index link bibliography prompt engineering lilian weng link https lilianweng github io posts 2023 03 15 prompt engineering llm powered autonomous agents lilian weng link https lilianweng github io posts 2023 06 23 agent reliability in learning prompting link https learnprompting org docs category ef b8 8f reliability building llm applications for production chip huyen link https huyenchip com 2023 04 11 llm engineering html 11 apr 2023 technical reports gpt 4 technical report openai arxiv 2023 paper https cdn openai com papers gpt 4 pdf cookbook https github com openai evals 16 mar 2023 gpt 4 system card openai arxiv 2023 paper https cdn openai com papers gpt 4 system card pdf github https github com openai evals 15 mar 2023 tutorial uncertainty estimation for natural language processing adam fisch robin jia tal schuster colling 2022 website https sites google com view uncertainty nlp prompt engineering papers promptpapers link https github com thunlp promptpapers awesome prompt engineering link https github com promptslab awesome prompt engineering papers evaluation survey wider and deeper llm networks are fairer llm evaluators xinghua zhang bowen yu haiyang yu yangyu lv tingwen liu fei huang hongbo xu yongbin li arxiv 2023 paper https aps arxiv org abs 2308 01862 github https github com alibabaresearch damo convai tree main widedeep 3 aug 2023 a survey on evaluation of large language models yupeng chang xu wang jindong wang yuan wu kaijie zhu hao chen linyi yang xiaoyuan yi cunxiang wang yidong wang wei ye yue zhang yi chang philip s yu qiang yang xing xie arxiv 2023 paper https arxiv org abs 2307 03109 github https github com mlgroupjlu llm eval survey 6 jul 2023 decodingtrust a comprehensive assessment of trustworthiness in gpt models boxin wang weixin chen hengzhi pei chulin xie mintong kang chenhui zhang chejian xu zidi xiong ritik dutta rylan schaeffer sang t truong simran arora mantas mazeika dan hendrycks zinan lin yu cheng sanmi koyejo dawn song bo li arxiv 2023 paper https arxiv org abs 2306 11698 github https github com ai secure decodingtrust website https decodingtrust github io 20 jun 2023 in chatgpt we trust measuring and characterizing the reliability of chatgpt xinyue shen zeyuan chen michael backes yang zhang arxiv 2023 paper https arxiv org abs 2304 08979 18 apr 2023 harnessing the power of llms in practice a survey on chatgpt and beyond jingfeng yang hongye jin ruixiang tang xiaotian han qizhang feng haoming jiang bing yin xia hu arxiv 2023 paper https arxiv org abs 2304 13712 github https github com mooler0410 llmspracticalguide 27 apr 2023 how robust is gpt 3 5 to predecessors a comprehensive study on language understanding tasks xuanting chen junjie ye can zu nuo xu rui zheng minlong peng jie zhou tao gui qi zhang xuanjing huang arxiv 2023 paper https arxiv org abs 2303 00293 github https github com textflint textflint 1 mar 2023 holistic evaluation of language models percy liang rishi bommasani tony lee dimitris tsipras dilara soylu michihiro yasunaga yian zhang deepak narayanan yuhuai wu ananya kumar benjamin newman binhang yuan bobby yan ce zhang christian cosgrove christopher d manning christopher r diana acosta navas drew a hudson eric zelikman esin durmus faisal ladhak frieda rong hongyu ren huaxiu yao jue wang keshav santhanam laurel orr lucia zheng mert yuksekgonul mirac suzgun nathan kim neel guha niladri chatterji omar khattab peter henderson qian huang ryan chi sang michael xie shibani santurkar surya ganguli tatsunori hashimoto thomas icard tianyi zhang vishrav chaudhary william wang xuechen li yifan mai yuhui zhang yuta koreeda arxiv 2022 paper https arxiv org abs 2211 09110 website https crfm stanford edu helm latest github https github com stanford crfm helm blog https crfm stanford edu 2022 11 17 helm html 16 nov 2022 prompting gpt 3 to be reliable chenglei si zhe gan zhengyuan yang shuohang wang jianfeng wang jordan boyd graber lijuan wang iclr 2023 paper https arxiv org abs 2210 09150 github https github com noviscl gpt3 reliability 17 oct 2022 plex towards reliability using pretrained large model extensions dustin tran 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find them overcoming few shot prompt order sensitivity yao lu max bartolo alastair moore sebastian riedel pontus stenetorp acl 2022 paper https arxiv org abs 2104 08786 active example selection for in context learning yiming zhang shi feng chenhao tan emnlp 2022 paper https arxiv org abs 2211 04486 github https github com chicagohai active example selection 8 nov 2022 selective annotation makes language models better few shot learners hongjin su jungo kasai chen henry wu weijia shi tianlu wang jiayi xin rui zhang mari ostendorf luke zettlemoyer noah a smith tao yu iclr 2023 paper https arxiv org abs 2209 01975 github https github com hkunlp icl selective annotation 5 sep 2022 learning to retrieve prompts for in context learning ohad rubin jonathan herzig jonathan berant naacl hlt 2022 paper https arxiv org abs 2112 08633 github https github com ohadrubin epr 16 dec 2021 instruction and rlhf lamini lm a diverse herd of distilled models from large scale instructions minghao wu abdul waheed chiyu zhang muhammad abdul mageed alham fikri aji arxiv 2023 paper https arxiv org abs 2304 14402 github https github com mbzuai nlp lamini lm 27 apr 2023 self refine iterative refinement with self feedback aman madaan niket tandon prakhar gupta skyler hallinan luyu gao sarah wiegreffe uri alon nouha dziri shrimai prabhumoye yiming yang sean welleck bodhisattwa prasad majumder shashank gupta amir yazdanbakhsh peter clark arxiv 2023 paper https arxiv org abs 2303 17651 github https github com madaan self refine website https selfrefine info 30 mar 2023 is prompt all you need no a comprehensive and broader view of instruction learning renze lou kai zhang wenpeng yin arxiv 2023 paper https arxiv org abs 2303 10475 github https github com renzelou awesome instruction learning 18 mar 2023 self instruct aligning language model with self generated instructions yizhong wang yeganeh kordi swaroop mishra alisa liu noah a smith daniel khashabi hannaneh hajishirzi arxiv 2022 paper https arxiv org abs 2212 10560 github https github com yizhongw self instruct 20 dec 2022 constitutional ai harmlessness from ai feedback yuntao bai et al anthropic arxiv 2022 paper https arxiv org abs 2212 08073 15 dec 2022 discovering language model behaviors with model written evaluations ethan perez et al arxiv 2022 paper https arxiv org abs 2212 09251 19 dec 2022 in context instruction learning seonghyeon ye hyeonbin hwang sohee yang hyeongu yun yireun kim minjoon seo arxiv 2023 paper https arxiv org abs 2302 14691 github https github com seonghyeonye icil 28 feb 2023 tools and external apis internet augmented language models through few shot prompting for open domain question answering angeliki lazaridou elena gribovskaya wojciech stokowiec nikolai grigorev arxiv 2023 paper https arxiv org abs 2203 05115 10 mar 2023 program of thoughts prompting disentangling computation from reasoning for numerical reasoning tasks wenhu chen xueguang ma xinyi wang william w cohen arxiv 2022 paper https arxiv org abs 2211 12588 github https github com wenhuchen program of thoughts 22 nov 2022 pal program aided language models luyu gao aman madaan shuyan zhou uri alon pengfei liu yiming yang jamie callan graham neubig arxiv 2022 paper https arxiv org abs 2211 10435 github https github com reasoning machines pal project https reasonwithpal com 18 nov 2022 talm tool augmented language models aaron parisi yao zhao noah fiedel arxiv 2022 paper https arxiv org abs 2205 12255 24 may 2022 toolformer language models can teach themselves to use tool timo schick jane dwivedi yu roberto dess roberta raileanu maria lomeli luke zettlemoyer nicola cancedda thomas scialom arxiv 2023 paper https arxiv org abs 2302 04761 9 feb 2023 fine tuning distilling step by step outperforming larger language models with less training data and smaller model sizes cheng yu hsieh chun liang li chih kuan yeh hootan nakhost yasuhisa fujii alexander ratner ranjay krishna chen yu lee tomas pfister arxiv 2023 paper https arxiv org abs 2305 02301 3 may 2023 https github com shm007g llama cult and more freelm fine tuning free language model xiang li1 xin jiang xuying meng aixin sun yequan wang arxiv 2023 paper https arxiv org pdf 2305 01616v1 pdf 2 may 2023 robustness invariance invariant language modeling maxime peyrard sarvjeet singh ghotra martin josifoski vidhan agarwal barun patra dean carignan emre kiciman robert west emnlp 2022 paper https arxiv org abs 2110 08413 github https github com epfl dlab invariant language models 16 oct 2021 distribution shift exploring distributional shifts in large language models for code analysis shushan arakelyan rocktim jyoti das yi mao xiang ren arxiv 2023 paper https arxiv org abs 2303 09128 16 mar 2023 out of distribution out of distribution detection and selective generation for conditional language models jie ren jiaming luo yao zhao kundan krishna mohammad saleh balaji lakshminarayanan peter j liu iclr 2023 paper https arxiv org abs 2209 15558 30 sep 2022 adaptation and generalization on the domain adaptation and generalization of pretrained language models a survey xu guo han yu arxiv 2022 paper https arxiv org abs 2211 03154 6 nov 2022 adversarial promptbench towards evaluating the robustness of large language models on adversarial prompts kaijie zhu jindong wang jiaheng zhou zichen wang hao chen yidong wang linyi yang wei ye neil zhenqiang gong yue zhang xing xie arxiv 2023 paper https arxiv org abs 2306 04528 github https github com microsoft promptbench 7 jun 20223 on the robustness of chatgpt an adversarial and out of distribution perspective jindong wang xixu hu wenxin hou hao chen runkai zheng yidong wang linyi yang haojun huang wei ye xiubo geng binxin jiao yue zhang xing xie arxiv 2023 paper https arxiv org abs 2302 12095 github https github com microsoft robustlearn 22 feb 2023 reliability testing for natural language processing systems samson tan shafiq joty kathy baxter araz taeihagh gregory a bennett min yen kan acl ijcnlp 2021 paper https arxiv org abs 2105 02590 06 may 2021 attribution attributed question answering evaluation and modeling for attributed large language models bernd bohnet vinh q tran pat verga roee aharoni daniel andor livio baldini soares massimiliano ciaramita jacob eisenstein kuzman ganchev jonathan herzig kai hui tom kwiatkowski ji ma jianmo ni lierni sestorain saralegui tal schuster william w cohen michael collins dipanjan das donald metzler slav petrov kellie webster arxiv 2022 paper https arxiv org abs 2212 08037 15 dec 2022 causality can large language models infer causation from correlation zhijing jin jiarui liu zhiheng lyu spencer poff mrinmaya sachan rada mihalcea mona diab bernhard sch lkopf arxiv 2023 paper https arxiv org abs 2306 05836 github https github com causalnlp corr2cause 9 jun 2023 selection inference exploiting large language models for interpretable logical reasoning antonia creswell murray shanahan irina higgins iclr 2023 paper https arxiv org abs 2205 09712 19 may 2022 investigating causal understanding in llms marius hobbhahn tom lieberum david seiler neurips 2022 workshop paper https openreview net forum id st6jtgdw8ke blog https www lesswrong com posts yzb5efvdoaqb337x5 investigating causal understanding in llms 3 oct 2022 blog https zhuanlan zhihu com p 608856445 new metric to evaluate the prompt safety bias and fairness more papers can be found via https github com uclanlp awesome fairness papers logic against bias textual entailment mitigates stereotypical sentence reasoning hongyin luo james glass eacl 2023 paper https arxiv org abs 2303 05670 news https news mit edu 2023 large language models are biased can logic help save them 0303 10 mar 2023 measuring fairness with biased rulers a survey on quantifying biases in pretrained language models pieter delobelle ewoenam kwaku tokpo toon calders bettina berendt arxiv 2021 paper https arxiv org abs 2112 07447 14 dec 2021 towards understanding and mitigating social biases in language models paul pu liang chiyu wu louis philippe morency ruslan salakhutdinov icml 2021 paper https arxiv org abs 2106 13219 github https github com pliang279 lm bias 24 jun 2021 fairness guided few shot prompting for large language models huan ma changqing zhang yatao bian lemao liu zhirui zhang peilin zhao shu zhang huazhu fu qinghua hu bingzhe wu arxiv 2023 paper https arxiv org abs 2303 13217 github https github com mahuanaaa g fair prompting 23 mar 2023 improve in context learning research team reseachers owain evans https owainevans github io research associate in artificial intelligence university of oxford tong zhang sebastian farquhar https sebastianfarquhar com deepmind oxford elias stengel eskin https esteng github io | awesome-list calibration gpt-3 gpt-4 llms reliability robustness safety uncertainty-estimation uncertainty-quantification chatgpt prompt-engineering prompting chain-of-thought in-context-learning large-language-models hallucination | ai |
CRUD-APP-Using-Node.js-MySQL | crud app using node js mysql using node js as backend development env mysql as a database this is a crud app prerequisites before you join this tutorial it is assumed that you meet the requirements listed below node js installed on your pc express js mysql phpmyadmin xampp templating engines ejs this application lets you to adding players to a database and also display their details from the database you can also delete and edit player details install required modules the following modules are going to be needed to successfully build the app express used to create handle routing and process requests from the client express fileupload simple express file upload middleware that wraps around busboy body parser used to parse incoming request from the client mysql node js driver for mysql ejs templating engine to render html pages for the app req flash used to send flash messages to the view nodemon installed globally it is used to watch for changes to files and automatically restart the server creating the database for the app copy the command below and navigate to your phpmyadmin dashboard and execute the following query in the console usually found at the bottom of the page in order to create database and table for the app create database socka create table if not exists players id int 5 not null auto increment first name varchar 255 not null last name varchar 255 not null position varchar 255 not null number int 11 not null image varchar 255 not null user name varchar 20 not null primary key id engine innodb default charset latin1 auto increment 1 | server |
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EPN | epn blockchain exchange | blockchain |
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blockchain-courses | blockchain courses blockchain courses applied to the ethereum blockchain | blockchain |
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yet-another-llm-curated-list | yet another llm curated list yet another llm large language models curated list but with chinese document i also want to build a language model as a curated list but i need some help build applications langchain https github com hwchase17 langchain symbolicai https github com xpitfire symbolicai | ai |
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Gaston.jl | gaston julia plotting using gnuplot gaston is a julia https julialang org package for plotting it provides an interface to gnuplot http gnuplot info a powerful plotting package available on all major platforms current stable release is v1 1 0 and it has been tested with julia lts 1 6 and stable 1 8 on linux gaston should work on any platform that runs gnuplot ci status ci https github com mbaz gaston jl actions workflows ci yml badge svg https github com mbaz gaston jl actions workflows ci yml documentation gaston jl s documentation can be found here https mbaz github io gaston jl stable why use gaston why use gaston when there are powerful alternatives such as plots jl and makie jl these are some gaston features that may be attractive to you gaston can plot using graphical windows and keeping multiple plots active at a time with mouse interaction directly to the repl using text ascii or sixels in jupyter juno and other ides supports popular 2 d plots regular function plots stem step histograms images etc supports surface contour and heatmap 3 d plots can save plots to multiple formats including pdf png and svg provides a simple interface for knowledgeable users to access gnuplot features fast time to load package plot and save to pdf is around six seconds knowledge of gnuplot is not required users familiar with gnuplot however will be able to take advantage of gaston s facilities to access gnuplot s vast feature set installation gaston requires gnuplot to be installed in your system it has been tested with versions 5 4 and above but it should work with any 5 x version gaston also requires julia v1 6 0 or above to install gaston using julia s packaging system enter julia s package manager prompt with and run v1 4 pkg add gaston tests gaston includes many tests wich you can run to make sure your installation is working properly to run the tests enter julia s package manager with and run v1 6 pkg test gaston all tests should pass although some tests may be labeled as broken | plotting julia gnuplot visualization | front_end |
maml | img src https github com materialsvirtuallab maml blob master resources logo horizontal png raw true alt maml width 50 github license https img shields io github license materialsvirtuallab maml https github com materialsvirtuallab maml blob main license linting https github com materialsvirtuallab maml workflows linting badge svg https github com materialsvirtuallab maml workflows linting badge svg testing https github com materialsvirtuallab maml workflows testing badge svg https github com materialsvirtuallab maml workflows testing badge svg downloads https pepy tech badge maml https pepy tech project maml codecov https codecov io gh materialsvirtuallab maml branch master graph badge svg token qnl1crlvvl https codecov io gh materialsvirtuallab maml maml materials machine learning is a python package that aims to provide useful high level interfaces that make ml for materials science as easy as possible the goal of maml is not to duplicate functionality already available in other packages maml relies on well established packages such as scikit learn and tensorflow for implementations of ml algorithms as well as other materials science packages such as pymatgen http pymatgen org and matminer http hackingmaterials lbl gov matminer for crystal molecule manipulation and feature generation official documentation at https materialsvirtuallab github io maml features 1 convert materials crystals and molecules into features in addition to common compositional site and structural features we provide the following fine grain local environment features a bispectrum coefficients b behler parrinello symmetry functions c smooth overlap of atom position soap d graph network features composition site and structure 2 use ml to learn relationship between features and targets currently the maml supports sklearn and keras models 3 applications a pes for modelling the potential energy surface constructing surrogate models for property prediction i neural network potential nnp ii gaussian approximation potential gap with soap features iii spectral neighbor analysis potential snap iv moment tensor potential mtp b rfxas for random forest models in predicting atomic local environments from x ray absorption spectroscopy c bowsr for rapid structural relaxation with bayesian optimization and surrogate energy model installation pip install via pypi bash pip install maml to run the potential energy surface pes lammps installation is required you can install from source or from conda bash conda install c conda forge label cf202003 lammps the snap potential comes with this lammps installation the gap package for gap and mlip package for mtp are needed to run the corresponding potentials for fitting nnp potential the n2p2 package is needed install all the libraries from requirement txt file bash pip install r requirements txt for all the requirements above bash pip install r requirements ci txt pip install r requirements optional txt pip install r requirements dl txt pip install r requirements txt usage many jupyter notebooks are available on usage see notebooks notebooks we also have a tool and tutorial lecture at nanohub https nanohub org resources maml api documentation see api docs https materialsvirtuallab github io maml maml html citing txt misc maml author chen chi and zuo yunxing ye weike ji qi and ong shyue ping title maml materials machine learning package year 2020 publisher github journal github repository howpublished url https github com materialsvirtuallab maml for the ml iap package maml pes please cite txt zuo y chen c li x deng z chen y behler j cs nyi g shapeev a v thompson a p wood m a ong s p performance and cost assessment of machine learning interatomic potentials j phys chem a 2020 124 4 731 745 https doi org 10 1021 acs jpca 9b08723 for the bowsr package maml bowsr please cite txt zuo y qin m chen c ye w li x luo j ong s p accelerating materials discovery with bayesian optimization and graph deep learning materials today 2021 51 126 135 https doi org 10 1016 j mattod 2021 08 012 for the atomsets model maml models atomsets please cite txt chen c ong s p atomsets as a hierarchical transfer learning framework for small and large materials datasets npj comput mater 2021 7 173 https doi org 10 1038 s41524 021 00639 w | materials-science machine-learning deep-learning machine-learning-force-field spectroscopy materials-discovery | ai |
Python-Web-Development-with-Sanic | python web development with sanic a href https www packtpub com product python web development with sanic 9781801814416 utm source github utm medium repository utm campaign 9781801814416 img src https static packt cdn com products 9781801814416 cover smaller alt python web development with sanic height 256px align right a this is the code repository for python web development with sanic https www packtpub com product python web development with sanic 9781801814416 utm source github utm medium repository utm campaign 9781801814416 published by packt an in depth guide for python web developers to improve the speed and scalability of web applications what is this book about today s developers need something more powerful and customizable when it comes to web app development they require effective tools to build something unique to meet their specific needs and not simply glue a bunch of things together built by others this is where sanic comes into the picture built to be unopinionated and scalable sanic is a next generation python framework and server tuned for high performance this book covers the following exciting features understand the difference between wsgi async and asgi servers discover how sanic organizes incoming data why it does it and how to make the most of it implement best practices for building reliable performant and secure web apps explore useful techniques for successfully testing and deploying a sanic web app create effective solutions for the modern web including task management bot integration and graphql identify security concerns and understand how to deal with them in your sanic apps if you feel this book is for you get your copy https www amazon com dp 1801814414 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following app exception pinkelephanterror async def handle pink elephants request request exception exception following is what you need for this book this book is for python web developers who have basic to intermediate level knowledge of how web technologies work and are looking to take their applications to the next level using the power of the sanic framework working knowledge of python web development along with frameworks such as django and or flask will be helpful but is not required a basic to intermediate level understanding of python 3 http restful api patterns and modern development practices and tools such as type annotations pytest and virtual environments will also be beneficial with the following software and hardware list you can run all code files present in the book chapter 1 11 software and hardware list chapter software required os required 1 11 sanic v21 12 lts windows mac os x and linux any 1 11 python 3 8 windows mac os x and linux any we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https static packt cdn com downloads 9781801814416 colorimages pdf related products django 3 by example third edition packt https www packtpub com product django 3 by example third edition 9781838981952 utm source github utm medium repository utm campaign 9781838981952 amazon https www amazon com dp 1838981950 angular projects second edition packt https www packtpub com product angular projects second edition 9781800205260 utm source github utm medium repository utm campaign 9781800205260 amazon https www amazon com dp 1800205260 get to know the author adam hopkins is a self taught programmer he started programming in high school and has been using python and building websites for more than 25 years he is a licensed attorney and practiced law before transitioning into software engineering as a second career adam is the core developer and project maintainer of sanic a popular asynchronous python framework and web server currently he is a software engineering manager at packetfabric where he leads a team building backend web services he is passionate about open source contributions and helping other developers to grow and learn adam lives in his desert home in the south of israel with his wife and five children updates 2022 05 07 update to autodiscovery py https github com packtpublishing python web development with sanic blob main chapter02 booktracker src utilities autodiscovery py in chapter 2 to resolve 11 https github com packtpublishing python web development with sanic issues 11 related to a difference in behaviors between sanic v21 3 and v21 6 download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781801814416 https packt link free ebook 9781801814416 a p | front_end |
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OpenLabeling | openlabeling open source image and video labeler github stars https img shields io github stars cartucho openlabeling svg style social label stars https github com cartucho openlabeling image labeling in multiple annotation formats pascal voc darkflow https github com thtrieu darkflow yolo darknet https github com pjreddie darknet ask for more create a new issue img src https media giphy com media l49jdgdsygjn369vw giphy gif width 40 img src https media giphy com media 3ohc1csrs9podgceuk giphy gif width 40 img src https media giphy com media 3o752fxkwtjjkhxp32 giphy gif width 40 img src https media giphy com media 3ohc11t9auzso6fwls giphy gif width 40 citation this project was developed for the following paper please consider citing it bibtex inproceedings 8594067 author j cartucho and r ventura and m veloso booktitle 2018 ieee rsj international conference on intelligent robots and systems iros title robust object recognition through symbiotic deep learning in mobile robots year 2018 pages 2336 2341 latest features jun 2019 deep learning object detection model may 2019 eccv2018 distractor aware siamese networks for visual object tracking jan 2019 easy and quick bounding boxe s resizing jan 2019 video object tracking with opencv trackers todo label photos via google drive to allow team online labeling new features discussion https github com cartucho openlabeling issues 3 table of contents quick start quick start prerequisites prerequisites run project run project gui usage gui usage authors authors quick start to start using the yolo bounding box tool you need to download the latest release https github com cartucho openlabeling archive v1 3 zip or clone the repo git clone recurse submodules git github com cartucho openlabeling git prerequisites you need to install python https www python org downloads opencv https opencv org version 3 0 1 python mpip install u pip 1 python mpip install u opencv python 1 python mpip install u opencv contrib python numpy tqdm and lxml 1 python mpip install u numpy 1 python mpip install u tqdm 1 python mpip install u lxml alternatively you can install everything at once by simply running python mpip install u pip python mpip install u r requirements txt pytorch https pytorch org get started locally visit the link for a configurator for your setup run project step by step 1 open the main directory 2 insert the input images and videos in the folder input 3 insert the classes in the file class list txt one class name per line 4 run the code 5 you can find the annotations in the folder output python main py h i o t tracker tracker type n n frames optional arguments h help show this help message and exit i input path to images and videos input folder default input o output path to output folder if using the pascal voc format it s important to set this path correctly default output t thickness bounding box and cross line thickness int default t 1 tracker tracker type tracker type being used csrt kcf mosse mil boosting medianflow tld goturn dasiamrpn n n frames number of frames to track object for to use dasiamrpn tracker 1 install the dasiamrpn https github com foolwood dasiamrpn submodule and download the model vot from google drive https drive google com drive folders 1btikp5pb6aqepqglmb2 z7bfpy6xej6h 2 copy it into dasiamrpn code 3 set default tracker in main py or run it with tracker dasiamrpn how to use the deep learning feature download one or some deep learning models from https github com tensorflow models blob master research object detection g3doc detection model zoo md and put it into object detection models directory you need to create the models folder by yourself the outline of object detection looks like that tf object detection py utils py models ssdlite mobilenet v2 coco 2018 05 09 download the pre trained model by clicking this link http download tensorflow org models object detection ssdlite mobilenet v2 coco 2018 05 09 tar gz and put it into object detection models create the models folder if necessary make sure to extract the model note default model used in main auto py is ssdlite mobilenet v2 coco 2018 05 09 we can set graph model path in file main auto py to change the pretrain model using main auto py to automatically label data first todo explain how the user can gui usage keyboard press img src https github com cartucho openlabeling blob master keyboard usage jpg key description a d previous next image s w previous next class e edges h help q quit video key description p predict the next frames labels mouse use two separate left clicks to do each bounding box right click quick delete use the middle mouse to zoom in and out use double click to select a bounding box authors jo o cartucho feel free to contribute github contributors https img shields io github contributors cartucho openlabeling svg https github com cartucho openlabeling graphs contributors | darknet yolo gui training-yolo opencv labeling-tool bounding-boxes object-detection darkflow pascal-voc | ai |
CouponAcceptance | this is an assignment for berkley engineering to understand the likelihood of drivers accepting coupons on the road based off an amazon database practical application assignment 5 1 uc berkeley ai ml prof certification this file contains a summary of findings from exploration of a dataset from the uci machine learning repository the data was collected via a survey on amazon mechanical turk the purpose of the exploration was to determine characteristics of drivers who are more likely to accept coffee house coupons brief description of the dataset the dataset is based on a survey describing different driving scenarios including the destination current time weather passenger etc and then asking people whether they will accept the coupon if they are the driver answers given that the users will drive there right away or later before the coupon expires are labeled as y 1 and answers no i do not want the coupon are labeled as y 0 there are five different types of coupons less expensive restaurants under 20 coffee houses carry out and take away bars and more expensive restaurants 20 50 the current work focuses on identifying attributes that provide a higher likelihood of drivers accepting coffee house coupons summary and findings a brief cleaning of the data was included before assessing workable data rows that were rendered unusable by having nan data were dropped after data was cleaned the following was discovered the acceptance rate is most impressive for males likely students in the age group below 21 acceptance rate 81 9 compared to an acceptance rate of 0 483 for all others drivers with monthly coffee house visits of 1 3 times or more had a higher coupon acceptance rate acceptance rate 65 9 vs 34 for all others drivers with no urgent destination had a higher coupon acceptance rate acceptance rate 57 8 vs 40 1 for all others mid morning 10 am and mid afternoon 2 pm times are associated with better acceptance rate of 59 3 versus an acceptance rate of 42 5 earlier in the day 7 am or later in the evening night 6 pm 10 pm we found that indeed when all five of the high likelihood acceptance criteria age below 50 travelling with friend s partner number of monthly coffee house visits 1 3 times or more 1 day expiration of coupons and time of the day being 10 am or 2 pm are simultaneously applied the acceptance rate is very high 82 4 vs an acceptance rate of only 47 8 for all others however there are certain other age groups or demographics that have a high acceptance rate under certain conditions for example for drivers who reported their monthly frequency of coffee house visits as less than 1 coupons targeted to male drivers in this category may have a better acceptance rate than females other important insights identified from the above analysis even though acceptance rate of coupons on cold 30 degrees or less days is lower 44 there are demographic groups with age over 50 years or male drivers below 21 years where acceptance rate of coupons is much higher 60 on these cold days early morning 7 am acceptance rate of coffee house coupons is low 44 however the 7 am acceptance rate for the age group below 21 years is relatively high 69 4 observations which drivers should be targeted for coffee house coupons based on attributes associated with higher acceptance rates based off findings coffee house coupons should be targeted to drivers below 50 years in age drivers who are travelling with friend s or partner who report 1 or more monthly visits to coffee houses the best time of the day for getting a higher acceptance rate is mid morning 10 am or mid afternoon 2pm which attributes are associated with a low likelihood of acceptance of coffee house coupons those drivers who report never frequenting a coffee shop have an obviously low likelihood of accepting coffee house coupons cold temperature 30f is also associated in general with a lower acceptance of coupons for most drivers excluding males below 21 years and the 50 plus age group certain times of day e g early in the morning 7 am or later in the evening night 6 pm 10 pm are also associated with a lower likelihood of acceptance which drivers should be targeted for getting a better acceptance rate for certain contextual or coupon attributes that are generally associated with an overall lower acceptance rate on cold temperature 30 degree days coupons should be targeted to the following demographic groups age over 50 or males below 21 years similarly 2 hour expiry coupons should be targeted to the particular demographic group of males below 21 years coupons in the early morning 7 am should be targeted to the age group below 21 years for a better acceptance rate at this time of the day for drivers who report visiting coffee house less than once a month coupons should be targeted to male drivers for a better acceptance rate | server |
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BlockChain | 120 2018 10 9 helloworld github star 1 2 java javascript 3 cto 4 5 6 1 2 pull request 3 https www boxuegu com web subject 002 index html a yqvplg b x7w7n9 utm source cebianlan java java https pan baidu com s 1miare7nvi5ubmloninv fg gsvr java kotlin dsl gradle git lambda kotlin 100 java java scala groovy c go javascript corda kotlin kotlin javascrpit nodejs npm javaee gradle git solidity hyperledger kotlin 1 kotlin java 2 3 4 dsl 5 gradle 6 7 git 8 https pan baidu com s 1gkxo3 upv0njzxhxxnre7g dapp web2 0 html css javascript html javascript css 1 javascrpit html css 2 3 4 5 6 kotlin https pan baidu com s 1zx9nm3fs bw69s6o8qrcdw java springboot springcloud springboot javaee springboot javaee 1 springboot 2 mysql h2 3 4 xml json 5 gradle 6 restful 7 spring java https pan baidu com s 1ci4pis7lw8zzwlif45 nsg springboot springboot 1 2 3 4 5 springcloud 6 https pan baidu com s 1hvagmqaayji5km ier1z w nodejs vue react hyperledger nodejs vue react hyperledger nodejs nodejs 1 nodejs web 2 express 3 vue react 4 bug vue react nodejs https pan baidu com s 15wm4ufwx9mlgmr mhje62w ipfs ipfs http ipfs 1 ipfs 2 ipfs 3 ipfs 4 ipfs 5 ipfs js api 6 ipfs docker 7 ipfs cors ipfs https pan baidu com s 1kjhbcsob9zrf6pxd94 c7a 1 evm web3 0 2 wallet geth metamask remix turffle ganache mocha 3 pow pos dpos 4 web3 v1 0 ganache truffle mocha solidity 5 ethers gas pow pos node 6 7 web3 vue react 8 ethereum dapp 9 10 solidity 11 12 13 ethereum 14 erc20 token 15 ico daico 16 oracle 17 https pan baidu com s 18uvdemrnou kepf1rq7ulw 1 mrd 2 3 4 prd 5 6 7 8 9 10 11 https pan baidu com s 1ddw eevkl mknyoiiivwhw hyperledger hyperledger ibm linux hyperledger hyperledger 1 hyperledger 2 3 0 3 hyperledger fabric technology peers orderer msp ca 4 hyperledger febric 5 hyperledger febric 6 nodejs go 7 hyperledger febric 8 hyperledger febric hyperledger https pan baidu com s 1mfmpixtsr1 wuralm7y6hw 1 kotlin android 2 android 3 android things 4 5 gpio 6 gps 7 8 9 10 gps iota go go hyperledger kotlin 10 go 1 go 2 go kotlin 3 4 go 5 go 6 go 7 go hyperledger go nodejs cc by sa 4 0 email 2698900570 qq com go 20 go https pan baidu com s 1jjpsthk go https pan baidu com s 1s93rm 6pnk qa1rnns6hha go gui https pan baidu com s 1 gmi2nkpaww4lshos60hng linux https pan baidu com s 1ioqryi1c4f yrm9mwnx7sq https pan baidu com s 1c9q1iinabkkj6axayzz13q mysql https pan baidu com s 1kvvl53l redis https pan baidu com s 1mgo0qtyj0f32ojjsqcvt8w https pan baidu com s 1jicd84e github https pan baidu com s 1c1kv5w beego https pan baidu com s 17ujce fd9cdwgntnzjrs0q https pan baidu com s 11orefkpmf2pjk z6xtb9fg https pan baidu com s 16pgs7lbzc inx8vg6xakkq node js https pan baidu com s 1hz2fbzves toz6sgw0t aa docker https pan baidu com s 1borwelh hyperledger https pan baidu com s 1tfqiziojmcjvde8y vvr4a | blockchain hyperledger cryptocurrency bitcion itheima | blockchain |
m- | p align center a href http m docs org img src http m docs org img m logo png alt mdash logo width 192 a p h3 align center a design system that fully embraces web standards h3 p align center mdash seeks to leverage html not replace it or try to outsmart it br this makes mdash ideal for all web projects and skill levels p p align center strong linkable tiny 6kb responsive accessible zero dependencies strong p hr mdash ui elements are built with 100 web standards following the tac css methodology https jordanbrennan hashnode dev tac a new css methodology this makes mdash extremely light https m docs org performance very fast and compatible with any type of web project mdash can work with any framework client side and server side or no framework at all because it s made from native html custom html tags and custom elements https developer mozilla org en us docs web api window customelements be it ssr spa pwa static site and even some email templates whatever type of project you have mdash will work this is especially useful to organizations looking to share a design system across products try mdash right now by simply linking to the cdn files below and visiting the doc site https m docs org for code samples and full api documentation to apply your own design language fork and customize mdash it s 100 vanilla html css and javascript quick start this is the web include these files in head and you re all set html link href https unpkg com m 3 2 0 dist m woff2 rel preload as font crossorigin link href https unpkg com m 3 2 0 dist m css rel stylesheet script src https unpkg com m 3 2 0 dist m js defer script or install via npm and bundle with your own assets npm install m built files are located in dist then try some mdash html m alert type success success m alert browser support mdash works with the latest versions of all mainstream browsers working on this project pre reqs node https nodejs org and gulp cli https gulpjs com docs en getting started quick start 1 clone the repo or fork 1 cd m 1 npm install 1 gulp watch 1 cd docs 1 npm install 1 npm start that builds mdash watches for changes and starts the doc site start coding developer notes custom element constructors have strict rules about what you can safely do inside them please get familiar with requirements for custom element constructors and reactions https html spec whatwg org multipage custom elements html custom element conformance some components are custom html tags that require no javascript other components are custom elements and for these the styles are still maintained in a separate css file the tac css methodology https jordanbrennan hashnode dev tac a new css methodology is followed some ides complain about unknown html tags if that s the case add this list to make it happy m accordion m alert m autocomplete m badge m box m breadcrumb m col m container m crumb m dot m icon m loader m menu m row m tab m tabs m tag m vbar | html css javascript painless 6kb standards fun not-react ssr spa pwa static-site the-m-is-for-freedom custom-elements better-code less-code no-code more-winning cures-coding-migraines | os |
awesome-adversarial-machine-learning | warning deprecated i no longer include up to date papers but the list is still a good reference for starters awesome adversarial machine learning awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome a curated list of awesome adversarial machine learning resources inspired by awesome computer vision https github com jbhuang0604 awesome computer vision table of contents blogs blogs papers papers talks talks blogs breaking linear classifiers on imagenet http karpathy github io 2015 03 30 breaking convnets a karpathy et al breaking things is easy http www cleverhans io security privacy ml 2016 12 16 breaking things is easy html n papernot i goodfellow et al attacking machine learning with adversarial examples https blog openai com adversarial example research n papernot i goodfellow s huang y duan p abbeel j clark robust adversarial examples https blog openai com robust adversarial inputs anish athalye a brief introduction to adversarial examples http people csail mit edu madry lab blog adversarial 2018 07 06 adversarial intro a madry et al training robust classifiers part 1 http people csail mit edu madry lab blog adversarial 2018 07 11 robust optimization part1 a madry et al adversarial machine learning reading list https nicholas carlini com writing 2018 adversarial machine learning reading list html n carlini recommendations for evaluating adversarial example defenses https nicholas carlini com writing 2018 evaluating adversarial example defenses html n carlini papers general intriguing properties of neural networks https arxiv org abs 1312 6199 c szegedy et al arxiv 2014 explaining and harnessing adversarial examples https arxiv org abs 1412 6572 i goodfellow et al iclr 2015 motivating the rules of the game for adversarial example research https arxiv org abs 1807 06732 j gilmer et al arxiv 2018 wild patterns ten years after the rise of adversarial machine learning https arxiv org abs 1712 03141 b biggio pattern recognition 2018 attack image classification deepfool a simple and accurate method to fool deep neural networks https arxiv org abs 1511 04599 s moosavi dezfooli et al cvpr 2016 the limitations of deep learning in adversarial settings https arxiv org abs 1511 07528 n papernot et al essp 2016 transferability in machine learning from phenomena to black box attacks using adversarial samples https arxiv org abs 1605 07277 n papernot et al arxiv 2016 adversarial examples in the physical world https arxiv org pdf 1607 02533v3 pdf a kurakin et al iclr workshop 2017 delving into transferable adversarial examples and black box attacks https arxiv org abs 1611 02770 liu et al iclr 2017 towards evaluating the robustness of neural networks https arxiv org abs 1608 04644 n carlini et al ssp 2017 practical black box attacks against deep learning systems using adversarial examples https arxiv org abs 1602 02697 n papernot et al asia ccs 2017 privacy and machine learning two unexpected allies http www cleverhans io privacy 2018 04 29 privacy and machine learning html i goodfellow et al reinforcement learning adversarial attacks on neural network policies https arxiv org abs 1702 02284 s huang et al iclr workshop 2017 tactics of adversarial attacks on deep reinforcement learning agents https arxiv org abs 1703 06748 y lin et al ijcai 2017 delving into adversarial attacks on deep policies https arxiv org abs 1705 06452 j kos et al iclr workshop 2017 segmentation object detection adversarial examples for semantic segmentation and object detection https arxiv org pdf 1703 08603 pdf c xie iccv 2017 vae gan adversarial examples for generative models https arxiv org abs 1702 06832 j kos et al arxiv 2017 speech recognition audio adversarial examples targeted attacks on speech to text https arxiv org abs 1801 01944 n carlini et al arxiv 2018 questiona answering system adversarial examples for evaluating reading comprehension systems https arxiv org abs 1707 07328 r jia et al emnlp 2017 defence adversarial training adversarial machine learning at scale https arxiv org pdf 1611 01236 pdf a kurakin et al iclr 2017 ensemble adversarial training attacks and defenses https arxiv org abs 1705 07204 f tram r et al arxiv 2017 defensive distillation distillation as a defense to adversarial perturbations against deep neural networks https arxiv org pdf 1511 04508 pdf n papernot et al ssp 2016 extending defensive distillation https arxiv org abs 1705 05264 n papernot et al arxiv 2017 generative model pixeldefend leveraging generative models to understand and defend against adversarial examples https arxiv org abs 1710 10766 y song et al iclr 2018 detecting adversarial attacks on neural network policies with visual foresight https arxiv org abs 1710 00814 y lin et al nips workshop 2017 regularization distributional smoothing with virtual adversarial training https arxiv org abs 1507 00677 t miyato et al iclr 2016 adversarial training methods for semi supervised text classification https arxiv org abs 1605 07725 t miyato et al iclr 2017 others deep neural networks are easily fooled high confidence predictions for unrecognizable images https arxiv org abs 1412 1897 a nguyen et al cvpr 2015 talks do statistical models understand the world https www youtube com watch v pq4a2mpcb0y i goodfellow 2015 classifiers under attack https www usenix org conference enigma2017 conference program presentation evans david evans 2017 adversarial examples in machine learning https www usenix org conference enigma2017 conference program presentation papernot nicolas papernot 2017 poisoning behavioral malware clustering http pralab diee unica it en node 1121 biggio b rieck k ariu d wressnegger c corona i giacinto g roli f 2014 is data clustering in adversarial settings secure http pralab diee unica it en node 955 bbiggio b pillai i rota bul s ariu d pelillo m roli f 2015 poisoning complete linkage hierarchical clustering http pralab diee unica it en node 1089 biggio b rota bul s pillai i mura m zemene mequanint e pelillo m roli f 2014 is feature selection secure against training data poisoning https pralab diee unica it en node 1191 xiao h biggio b brown g fumera g eckert c roli f 2015 adversarial feature selection against evasion attacks https pralab diee unica it en node 1188 zhang f chan ppk biggio b yeung ds roli f 2016 licenses license cc0 http i creativecommons org p zero 1 0 88x31 png http creativecommons org publicdomain zero 1 0 to the extent possible under law yen chen lin http yclin me has waived all copyright and related or neighboring rights to this work | ai |
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modeapk.html | modeapk html informations technology gaming | server |
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2019-5Binf-visit-civitavecchia | 2019 5binf visit civitavecchia year wide project for the last year of the high school computer science and information technologies verifica verifica 1 md | server |
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ppplyn | gas measurement in paralelni polis why paralelni polis has 3 hungry gas boilers and we want to keep an eye on amount of gas they eat doing gas reading in 2016 with pen and paper is more than cumbersome getting things ready sudo apt get install ipython python opencv python scipy python numpy python pygame python setuptools python pip python sklearn python cssselect python lxml sudo pip install https github com sightmachine simplecv zipball develop sudo pip install svgwrite git clone git github com paralelnipolis ppplyn git cd ppplyn test image recognition on images included in repository test py images input camera 1449323843 png 1474 652 images input camera 1449323833 png 1474 648 images input camera 1449323854 png 1474 657 images input camera 1449323901 png 1474 678 images input camera 1449323870 png 1474 664 images input camera 1449323822 png 1474 643 images input camera 1449323906 png 1474 680 images input camera 1450650840 png 1947 815 images input camera 1449323880 png 1474 669 images input camera 1449323838 png 1474 650 images input camera 1449323864 png 1474 662 images input camera 1449323917 png 1474 685 images input camera 1449323912 png 1474 683 images input camera 1449323896 png 1474 675 images input camera 1449323875 png 1474 665 images input camera 1449323828 png 1474 645 images input camera 1449323848 png 1474 655 images input camera 1449323859 png 1474 635 images input camera 1449323891 png 1474 673 images input camera 1449323885 png 1474 671 tools for camera settings v4l2 ctl set ctrl brightness 100 this needs to be done after reboot v4l2 ctl list ctrls list options for current webcam v4l2 ctl info detecting digits source image this is raw image taken by the camera camera is positioned slightly above the meter to prevent any reflections gas meter docs board7 png marker detection we have 4 oragne markers in each corner of the meter this color can be easily detected on the image gas meter with markers docs image with markers png gas meter with fixed perspective knowing position of each corner we can transform image to rectangle this helps us estimate position of the digits we are looking for fixed perspective docs fixed perspective png digits detection now we can assume in which area of the image is each digit these coordinates are hardcoded in the detector fixed perspective docs fixed perspective boxes png single digits now we are ready find blobs digits in each rectangle with some black magic we can ignore blobs which are not digits reflections and crap we are not interested in digits 5 docs 5 png digit 7 docs 7 png digit 3 docs 3 png digit 9 docs 9 png machine learning currently we have two classificators used for digit recognition svcdigitdetector svcdigitdetector py detector based on linearsvc from scikit templatedigitdetector templatedigitdetector py dumb detector substracting two images and measuring the difference feel free to implement any other detector it just needs to have method detect digit at some point i want to implement ideas mentioned in this article http joshmontague com posts 2016 mnist scikit learn training dataset this dataset images dataset is used for training of image recognition algoritm for i in 0 9 do echo n digit i echo ls i png wc l samples done digit 0 481 samples digit 1 452 samples digit 2 99 samples digit 3 79 samples digit 4 409 samples digit 5 142 samples digit 6 102 samples digit 7 294 samples digit 8 153 samples digit 9 420 samples testing dataset testing dataset images dataset test contains images which are not prsent in the training dataset for i in 0 9 do echo n digit i echo ls i png wc l samples done digit 0 68 samples digit 1 77 samples digit 2 31 samples digit 3 65 samples digit 4 100 samples digit 5 94 samples digit 6 56 samples digit 7 91 samples digit 8 70 samples digit 9 99 samples testing digit detectors currently we have two ways how to detect digits svcdigitdetector linearsvc best and templatedigitdetector template substraction poor there is a simple tool which tests detectors with the testing dataset test detectors py svc digit 0 template digit 0 svc digit 0 template digit 0 svc digit 0 template digit 0 svc digit 0 template digit 0 svc digit 0 template digit 0 svc digit 0 template digit 0 svc digit 0 template digit 0 svc digit 9 template digit 0 svc digit 9 template digit 0 svc digit 9 template digit 8 svc digit 9 template digit 0 svc digit 9 template digit 0 svc digit 9 template digit 0 svc digit 9 template digit 8 svc digit 9 template digit 0 svc digit 9 template digit 8 svcdigitdetector 99 3342210386 templatedigitdetector 76 0319573901 physical setup odroid u3 microsoft lifecam hd 3000 3d printed cover from makerlab http makerslab cz tbc led strips tons of epoxy glue camera from the front side docs camera front jpg camera from the back side docs camera back jpg | ai |
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5-python-projects | 5 python projects in 5 days what is this this is a collection of 5 beginner python projects a few weeks ago i challenged myself to get out of a coding rut by creating a new python project every single day the purpose of this exercise was to show how versatile python can be especially for beginners to create vastly different projects without spending too much time how does it work each day of the challenge by 7am pst i added a new project to this repository https github com harshibar 5 python projects in the following categories 1 kbd game development flappy bird ft yogi https github com harshibar 5 python projects tree master 1 game kbd 2 kbd machine learning teaching an ai to play ping pong https github com harshibar 5 python projects tree master 2 ml kbd 3 kbd web development tinder for netflix https github com harshibar 5 python projects tree master 3 webdev kbd 4 kbd data visualization animal crossing analysis https github com harshibar 5 python projects tree master 4 dataviz kbd 5 kbd scripting common intern pt 2 https github com harshibar common intern tree 7947ed6f151e5b9b1792c4bc7a7623212fa3f8bc kbd each project folder includes the code itself usage instructions and additional resources i ve also uploaded corresponding videos on my youtube channel https youtube com c harshibar each 4 6 minute video shows each project s inspiration and basic building blocks why does this exist ever since i was learning how to code and starting my career in tech one piece of advice i often heard and now give is to create as much as possible through independent projects hackathons etc but to this day whenever i sit down wanting to start a new project my mind goes blank i always ask myself but what do i make i must not be the only one facing this idea block so i wanted to create a beginner friendly collection of python projects that are accessible fun to learn and easy to clone and expand who created this that would be me harshibar i am a recent college graduate and spent a year working at a silicon valley startup before quitting to launch my own startup meanwhile i ve been launching my youtube channel https youtube com c harshibar where i create content about tech https www youtube com playlist list plgxijxced6mdo5zbmmcktbnecjvolpkfs career https www youtube com playlist list plgxijxced6mdam s5yyklubljffv16ver productivity https www youtube com playlist list plgxijxced6mdg w2pkxbiysbj6ublxdit and lifestyle https www youtube com playlist list plgxijxced6mc3yghcqmf9hkxtjpkyykzj when was this made this project was released the week of may 18 2020 | front_end |
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PHP-and-MySQL-Web-Development-5th-Edition | php and mysql web development 5th edition the source files of luke welling and laura thomson s php and mysql web development 5th edition book | front_end |
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dabr | dabr dabr is a php web interface to the twitter api for mobile devices the project is running live at http dabr co uk | php twitter | front_end |
webpack-frontend-template | webpack 5 div a href https github com webpack webpack img width 150 height 150 src https webpack js org assets icon square big svg a div br br github release https img shields io github release brovkin webpack frontend template svg github stars https img shields io github stars brovkin webpack frontend template svg style social github watchers https img shields io github watchers brovkin webpack frontend template svg style social babel https babeljs io javascript es6 sass scss less css js webpack dev server typescript https www typescriptlang org webpack bundle analyzer https www npmjs com package webpack bundle analyzer npm run stats eslint https eslint org csv xml webpack frontend template dist src assets fonts images styles index html index ts webpack config js package json editorconfig eslintignore eslintrc json gitignore prettierrc src yarn run dev development yarn run build production yarn start yarn run stats package json bash yarn install bash yarn start artembrovkin https vk com artembrovkin instagram brovkin artem https www instagram com brovkin artem | webpack5 typescript | front_end |
Full-Stack-Web-Development-with-GraphQL-and-React-Second-Edition | full stack web development with graphql and react second edition a href https www packtpub com product full stack web development with graphql and react second edition 9781801077880 img src https static packt cdn com products 9781801077880 cover smaller alt full stack web development with graphql and react second edition height 256px align right a this is the code repository for full stack web development with graphql and react https www packtpub com product full stack web development with graphql and react second edition 9781801077880 published by packt taking react from frontend to full stack with graphql and apollo what is this book about react and graphql combined would provide a very dynamic efficient and stable tech stack to build web based applications graphql is a modern solution for querying an api which represents an alternative to rest and is the next evolution in web development this book covers the following exciting features build a graphql api by implementing models and schemas with apollo and sequelize set up an apollo client and build front end components using react write reusable react components and use react hooks authenticate and query user data using graphql use mocha to write test cases for your full stack application deploy your application to aws using docker and circlecirs if you feel this book is for you get your copy https www amazon com dp 1801077886 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following import react from react import reactdom from react dom import app from app reactdom render app document getelementbyid root following is what you need for this book the book is for web developers familiar with react and graphql who want to enhance their skills and build full stack applications using industry standards like react apollo nodejs and sql at scale while learning to solve complex problems with graphql with the following software and hardware list you can run all code files present in the book chapter 1 12 software and hardware list chapter software required os required 1 12 node js 14 windows mac os x and linux any 1 12 react 17 windows mac os x and linux any 1 12 sequelize 6 windows mac os x and linux any 1 12 mysql 5 or 8 windows mac os x and linux any we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https static packt cdn com downloads 9781801077880 colorimages pdf errata page 17 code snippet 2 line 6 initialposts map post i should be posts map post i related products other books you may enjoy full stack react typescript and node packt https www packtpub com product full stack react typescript and node 9781839219931 amazon https www amazon com dp 1839219939 react 17 design patterns and best practices third edition packt https www packtpub com product react 17 design patterns and best practices third edition 9781800560444 amazon https www amazon com dp 1800560443 get to know the author sebastian grebe is a verified computer science expert for application development he is a young entrepreneur working on a variety of products targeting the consumer market he specializes in web development using the newest technologies such as react the phoenix framework kubernetes and many more furthermore he has experience in merging old and new applications developing cross platform apps with react native and writing efficient apis and backends with node js and elixir currently he works as an engineering manager on a microservice oriented architecture using micro frontends to power a scalable e commerce platform other books by the authors hands on full stack web development with graphql and react https www packtpub com product hands on full stack web development with graphql and react 9781789134520 download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781801077880 https packt link free ebook 9781801077880 a p | front_end |
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wallet | img src https github com bitpay wallet blob master resources bitpay windows icon wide310x150logo scale 100 png alt bitpay wallet circleci https img shields io circleci project github bitpay wallet master svg https circleci com gh bitpay wallet codecov https img shields io codecov c github bitpay wallet svg https codecov io gh bitpay wallet crowdin https d322cqt584bo4o cloudfront net copay localized png https crowdin com project copay please note we have detected some fake copay wallets on the google play store for android please be sure to install bitpay wallet only from the app stores app s developer should be bitpay inc only bitpay wallet formerly copay is a secure bitcoin bitcoin cash ethereum and erc20 wallet platform for both desktop and mobile devices bitpay wallet uses bitcore wallet service https github com bitpay bitcore tree master packages bitcore wallet service bws for peer synchronization and network interfacing binary versions of bitpay wallet are available for download at https bitpay com wallet this project was created by bitpay inc and it is maintained by bitpay and hundreds of contributors main features bitcoin ethereum bitcoin cash and xrp support multiple wallet creation btc bch and eth and management in app intuitive multisignature security for personal or shared wallets easy spending proposal flow for shared wallets and group payments bip32 https github com bitcoin bips blob master bip 0032 mediawiki hierarchical deterministic hd address generation and wallet backups device based security all private keys are stored locally not in the cloud support testnet wallets for all supported coins synchronous access across all major mobile and desktop platforms payment protocol bip70 bip73 support easily identifiable payment requests and verifiable secure bitcoin payments support for over 150 currency pricing options and unit denomination in btc mnemonic bip39 support for wallet backups paper wallet sweep support bip38 email for payments transfers confirmations etc push notifications only available for ios and android versions customizable wallet naming and background colors multiple languages supported see more details and download links at https bitpay com wallet coin specific features bitcoin segwit and native segwit addresses bech32 for sending and receiving cpfp child pays for parent transaction acceleration available after 4 hours of unconfirmed txs transaction fee adjustment using 4 preset levels using bitcoin core estimations or custom fee rate setting bitcoin cash schnorr signature support ethereum wallet connect multisig wallet using gnosis multisig contract mainnet contract address 0x6e95c8e8557abc08b46f3c347ba06f8dc012763f kovan testnet contract address 0x2c992817e0152a65937527b774c7a99a84603045 gas price adjustment using 4 preset levels using custom estimation algoritm or custom gas price setting testing in a browser note this method should only be used for development purposes when running bitpay wallet in a normal browser environment browser extensions and other malicious code might have access to internal data and private keys for production use see the latest official releases https github com bitpay wallet releases clone the repo and open the directory sh git clone https github com bitpay wallet git cd wallet ensure you have node https nodejs org installed then install and start wallet sh npm install npm run apply bitpay npm run start visit localhost 8100 http localhost 8100 to view the app unit e2e tests karma protractor to run the tests run npm run test testing on real devices it s recommended that all final testing be done on a real device both to assess performance and to enable features that are unavailable to the emulator e g a device camera android follow the cordova android platform guide https cordova apache org docs en latest guide platforms android to set up your development environment when your development environment is ready run the start android package script sh npm run apply bitpay npm run prepare bitpay npm run start android ios follow the cordova ios platform guide https cordova apache org docs en latest guide platforms ios to set up your development environment when your development environment is ready run the start ios package script sh npm run apply bitpay npm run prepare bitpay npm run start ios desktop linux macos and windows the desktop version of bitpay wallet currently uses electron to get started first install electron on your system from the electron website https electronjs org when electron is installed run the start desktop package script sh npm run apply wallet npm run start desktop build bitpay wallet app bundles before building the release version for a platform run the clean all command to delete any untracked files in your current working directory be sure to stash any uncommitted changes you ve made this guarantees consistency across builds for the current state of this repository the final commands build the production version of the app and bundle it with the release version of the platform being built android sh npm run clean all npm install npm run apply bitpay npm run prepare bitpay npm run final android ios sh npm run clean all npm install npm run apply bitpay npm run prepare bitpay npm run final ios desktop linux macos and windows sh npm run clean all npm install npm run apply bitpay npm run final desktop desktop data path per user application data directory for bitpay distribution sh library containers com bitpay wallet desktop data bitpay configuration enable external services to enable external services set the bitpay external services config location environment variable to the location of your configuration before running the apply task sh bitpay external services config location bitpay externalservices json npm run apply bitpay about bitpay wallet general bitpay wallet formerly copay implements a multisig wallet using p2sh https en bitcoin it wiki pay to script hash addresses it supports multiple wallets each with its own configuration such as 3 of 5 3 required signatures from 5 participant peers or 2 of 3 to create a multisig wallet shared between multiple participants bitpay wallet requires the extended public keys of all the wallet participants those public keys are then incorporated into the wallet configuration and combined to generate a payment address where funds can be sent into the wallet conversely each participant manages their own private key and that private key is never transmitted anywhere to unlock a payment and spend the wallet s funds a quorum of participant signatures must be collected and assembled in the transaction the funds cannot be spent without at least the minimum number of signatures required by the wallet configuration 2 of 3 3 of 5 6 of 6 etc once a transaction proposal is created the proposal is distributed among the wallet participants for each to sign the transaction locally finally when the transaction is signed the last signing participant will broadcast the transaction to the bitcoin network bitpay wallet also implements bip32 https github com bitcoin bips blob master bip 0032 mediawiki to generate new addresses for peers the public key that each participant contributes to the wallet is a bip32 extended public key as additional public keys are needed for wallet operations to produce new addresses to receive payments into the wallet for example new public keys can be derived from the participants original extended public keys once again it s important to stress that each participant keeps their own private keys locally private keys are not shared and are used to sign transaction proposals to make payments from the shared wallet for more information regarding how addresses are generated using this procedure see structure for deterministic p2sh multisignature wallets https github com bitcoin bips blob master bip 0045 mediawiki bitpay wallet backups and recovery since v1 2 bitpay wallet uses bip39 mnemonics for backing up wallets the bip44 standard is used for wallet address derivation multisig wallets use p2sh addresses while non multisig wallets use p2pkh information about backup and recovery procedures is available at https github com bitpay wallet blob master backuprecovery md previous versions of bitpay wallet used files as backups see the following section it is possible to recover funds from a bitpay wallet wallet without using bitpay wallet or the wallet service check the copay recovery tool https github com bitpay copay recovery tree master wallet export format bitpay wallet encrypts the backup with the stanford js crypto library http bitwiseshiftleft github io sjcl to extract the private key of your wallet you can go to settings choose your wallet click in more options then wallet information scroll to the bottom and click in extended private key that information is enough to sign any transaction from your wallet so be careful when handling it the backup also contains the key publickeyring that holds the extended public keys of the copayers depending on the key derivationstrategy addresses are derived using bip44 https github com bitcoin bips blob master bip 0044 mediawiki or bip45 https github com bitcoin bips blob master bip 0045 mediawiki wallets created in copay v1 2 and forward always use bip44 all previous wallets use bip45 also note that since copay version v1 2 non multisig wallets use address types pay to publickeyhash p2pkh while multisig wallets still use pay to scripthash p2sh key addresstype at the backup copay version wallet type derivation strategy address type 1 2 all bip45 p2sh 1 2 non multisig bip44 p2pkh 1 2 multisig bip44 p2sh 1 5 multisig hardware wallets bip44 root m 48 p2sh using a tool like bitcore playground http bitcore io playground all wallet addresses can be generated tip use the address section for p2pkh address type wallets and multisig address for p2sh address type wallets for multisig addresses the required number of signatures key m on the export is also needed to recreate the addresses bip45 note all addresses generated at bws with bip45 use the shared cosigner index 2147483647 so copay address indexes look like m 45 2147483647 0 x for main addresses and m 45 2147483647 1 y for change addresses since version 1 5 copay uses the root m 48 for hardware multisignature wallets this was coordinated with ledger and trezor teams while the derivation path format is still similar to bip44 the root was in order to indicate that these wallets are not discoverable by scanning addresses for funds address generation for multisignature wallets requires the other copayers extended public keys bitcore wallet service bitpay wallet depends on bitcore wallet service https github com bitpay bitcore wallet service bws for blockchain information networking and copayer synchronization a bws instance can be setup and operational within minutes or you can use a public instance like https bws bitpay com switching between bws instances is very simple and can be done with a click from within bitpay wallet bws also allows bitpay wallet to interoperate with other wallets like bitcore wallet cli https github com bitpay bitcore wallet please note that bitpay wallet v5 3 0 and above use csp to restrict network access to use a custom bws see csp announcement https github com bitpay copay blob master csp md translations bitpay wallet uses standard gettext po files for translations and crowdin https crowdin com project copay as the front end tool for translators to join our team of translators please create an account at crowdin https crowdin com and translate the bitpay wallet documentation and application text into your native language to download and build using the latest translations from crowdin please use the following commands sh cd i18n node crowdin download js this will download all partial and complete language translations while also cleaning out any untranslated ones translation credits japanese dabura667 french kirvx portuguese pmichelazzo spanish cmgustavo german saschad russian vadim0 gracias totales release schedules bitpay wallet uses the major minor batch convention for versioning any release that adds features should modify the minor or major number bug fixing releases we release bug fixes as soon as possible for all platforms usually around a week after patches a new release is made with language translation updates like 1 1 4 and then 1 1 5 there is no coordination so all platforms are updated at the same time minor and major releases t 0 tag the release 1 2 and text lock meaning only non text related bug fixes though this rule is sometimes broken it s good to make a rule t 7 testing for 1 2 is finished translation is also finished and 1 2 1 is tagged with all translations along with bug fixes made in the last week t 7 ios is submitted for 1 2 1 all other platforms are submitted with auto release off t 17 all platforms 1 2 1 are released when apple approves the ios application update anyone and everyone is welcome to contribute please take a moment to review the guidelines for contributing contributing md bug reports contributing md bugs feature requests contributing md features pull requests contributing md pull requests current active developers gpg keys id 15edad8d9f2eb1af cmgustavo fc283098da862864 gabrielbazan7 dd6d7eaade12280d gamboster d87947cc8a32d91c msalcala11 612c9c4ddac47b61 rastajpa f8fc1d9b1b46486d matiu support please see support requests contributing md support license bitpay wallet is released under the mit license please refer to the license https github com bitpay wallet blob master license file that accompanies this project for more information including complete terms and conditions | bitcoin wallet hardware-wallet wallet-service blockchain copay bitpay | blockchain |
Data-Modeling-with-Cassandra | data engineering nano degree programm of udacity project 2 h1 project data modeling with cassandra h1 a startup called sparkify wants to analyze the data they ve been collecting on songs and user activity on their new music streaming app the analysis team is particularly interested in understanding what songs users are listening to currently there is no easy way to query the data to generate the results since the data reside in a directory of csv files on user activity on the app br they d like a data engineer to create an apache cassandra database which can create queries on song play data to answer the questions and wish to bring you on the project your role is to create a database for this analysis you ll be able to test your database by running queries given to you by the analytics team from sparkify to create the results br h2 project overview h2 br in this project you ll apply what you ve learned on data modeling with apache cassandra and complete an etl pipeline using python to complete the project you will need to model your data by creating tables in apache cassandra to run queries you are provided with part of the etl pipeline that transfers data from a set of csv files within a directory to create a streamlined csv file to model and insert data into apache cassandra tables br we have provided you with a project template that takes care of all the imports and provides a structure for etl pipeline you d need to process this data br h2 datasets h2 for this project you ll be working with one dataset event data the directory of csv files partitioned by date here are examples of filepaths to two files in the dataset br event data 2018 11 08 events csv br event data 2018 11 09 events csv br h2 project template h2 br to get started with the project go to the workspace on the next page where you ll find the project template a jupyter notebook file you can work on your project and submit your work through this workspace br the project template includes one jupyter notebook file in which br you will process the event datafile new csv dataset to create a denormalized dataset br you will model the data tables keeping in mind the queries you need to run br you have been provided queries that you will need to model your data tables for br you will load the data into tables you create in apache cassandra and run your queries br br h2 project steps h2 br below are steps you can follow to complete each component of this project br modeling your nosql database or apache cassandra database br design tables to answer the queries outlined in the project template br write apache cassandra create keyspace and set keyspace statements br develop your create statement for each of the tables to address each question br load the data with insert statement for each of the tables br include if not exists clauses in your create statements to create tables only if the tables do not already exist we recommend you also include drop table statement for each table this way you can run drop and create tables whenever you want to reset your database and test your etl pipeline br test by running the proper select statements with the correct where clause br build etl pipeline br implement the logic in section part i of the notebook template to iterate through each event file in event data to process and create a new csv file in python br make necessary edits to part ii of the notebook template to include apache cassandra create and insert statements to load processed records into relevant tables in your data model br test by running select statements after running the queries on your database br you can run project with using just start py and check all results in terminal br result https user images githubusercontent com 16669517 130688919 9950dc91 7198 49bf a0fd 51853f79911f png | database cassandra-database etl | server |
de_bootcamp_coursework | de bootcamp coursework my course work participating in the data engineering bootcamp from wizeline academy and google cloud | cloud |
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HackerBooks | hackerbooks pr ctica fundamentos de programacion ios con swift keepcoding startup engineering master iii respuestas a las preguntas y comentarios mirar en la ayuda iskindofclass y como usarlo para saber qu devuelve la clase iskindofclass sirve para saber si una clase es de un tipo determinado en nuestro caso la utilizar amos para saber si recibimos un jsondictionary o un jsonarray en qu otros modos podemos trabajar is as podemos trabajar con el operador de cast as en esta pr ctica lo he utilizado como en el ejemplo de clase con una funci n decode sobrecargada una para operar sobre las ocurrencias de jsondictionary para parsear la clase book y otro para el caso un jsondictionary optional que lanza un error en el caso de no recibirlo en otro caso se utiliza en la funci n de decodificaci n para ver si se ha recibido un jsonarray con un opcional maybearray d nde guardar as las im genes de portada y los pdfs en la carpeta cache de la sandbox de la aplicaci n c mo har as para persistir la propiedad isfavorite de un libro se te ocurre m s de una forma de hacerlo he optado por guardar un array en nsuserdefaults con el t tulo de los libros que son favoritos una vez que arranca la aplicaci n y al inicializar el set de libros se mira si el libro est en el array y se marca como favorito otra forma de hacerlo es guardarlo como un diccionario de pares clave valor en nsuserdefaults como en el curso online de fundamentos ios donde cada entrada sea un libro etiquetado como favorito con valor true de la misma forma que antes se guardan restauran los favoritos una forma alternativa de guardar los datos de favoritos es utilizar un fichero que se guarda en la carpeta documentos de la sandbox de la aplicaci n para guardar los favoritos si se cierra la aplicaci n los datos de favoritos estar n guardados en disco y al abrirla de nuevo se restaura la situaci n anterior tambi n se puede hacer con frameworks de persistencia siendo uno de ellos core data c mo har as para notificar que la propiedad isfavorite de un libro ha cambiado se pueden utilizar varios m todos entre otros 1 target action 2 delegado 3 notificaciones en el caso de la pr ctica librarytableviewcontroller es delegado de bookviewcontroller y cuando la informaci n de un libro ha cambiado se hace por las dos v as v a delegado y v a notificaciones c mo enviar as informaci n desde el controlador de un agtbook a un agtlibrarytableviewcontroller cu l te parece mejor pues como ya se ha comentado en la pregunta anterior sas son las posibilidades la decisi n queda a manos del criterio que adopte dise ador de la aplicaci n en este caso he decidido hacerlo con el m todo de notificaciones creo que el m todo de notificaciones es el mejor ya que permite avisar a todos los objetos que se suscriban a la misma el patr n de dise o de delegado est m s limitado explica el m todo reloaddata de uitableview es una aberraci n desde el punto de vista de rendimiento volver a cargar datos que en su mayor a ya estaban correctos explica por qu no es as hay una forma alternativa cu ndo merece la pena usarlo adem s de que el data source modelo que alimenta la uitableview est en local y no hay que descargarlo tengo entendido que las clase de cocoa solamente actualizan las celdas que caben en una vista por ambos motivos no parece desde luego ninguna aberraci n c mo har as para notificar que un usuario ha cambiado en la tabla el libro seleccionado al agtsimplepdfviewcontroller para su actualizaci n en el m todo viewwillappear de la clase agtsimplepdfviewcontroller se suscribe a las notificaciones con el selector de que un libro haya cambiado y as se actualiza por tanto la vista del pdf comentarios sobre la pregunta acerca de c mo informar de cambios entre los controladores de agtbookviewcontroller y agtlibrarytableviewcontroller en el c digo an logo de clase el controlador de la tabla informa a su delegado que es el controlador de agtbook y adem s manda una notificaci n de que ha cambiado el foco del libro seleccionado me gustar a conocer cu l es la mejor soluci n si dejar ambas llamadas quitar la notificaci n o condicionar a si su delegado est suscrito a notificaciones en la parte de implementaci n del diccionario de libros con la estructura de datos multimap he visto que existen librer as como buckets que permiten incluirla con cocoapods pero he preferido hacerlo por m mismo para aprender y por hacerlo con swift eso s me ha costado un poco por el tema de los opcionales y el casting array set no est implementado cuando tenga tiempo implementar el bookdictionary como una clase multimap k v al estar acostumbrado a programar en lenguajes que manejan excepciones notar s que cualquier funci n que devuelva una tiene su try y su do supongo que es una buena pr ctica en swift y que costar mucho cambiar desde objective c en el init de asyncimage no llamo a la funci n getimage que hace la gesti n de la descarga y renderizado de las im genes se hace a demanda a medida que la aplicaci n cargue las vistas como es requisito tambi n he le do que la mejor manera de suscribir da de baja de notificaciones se hace en los m todos init o viewdidload para el alta y deinit para la baja en la pr ctica y en clase lo hemos hecho en viewdidappear viewdiddisappear quisiera saber cu l es la mejor creo que al instanciar desinstanciar ser a mejor otra reflexi n es que se intuye que a partir de un cierto n mero de horas trabajadas con xcode y swift se ver n los frutos ya que al final es siempre aplicar patr n delegado notificaciones por ejemplo la gesti n de favoritos la he hecho como recomendaste teniendo en cuenta que favorito es una tag m s a partir del cambio en el modelo ste informa de su cambio a los suscriptores de su notificaci n pues bien a la hora de hacer la lista por t tulo no me daba cuenta de que es muy parecido creamos una variable en el modelo de library para gestionar el sort y en funci n de su cambio el data source nutre a la vista en consecuencia lo que quiero decir es que al principio no aplicamos certeramente siempre lo aprendido es una tarea m s dentro de todas las que hay que hacer en l neas generales estoy muy contento del trabajo realizado he aprendido much simo con esta pr ctica y aunque creo que joven padawan llego a ser queda el largo y tortuoso camino de la fuerza para alcanzar la meta de llegar a ser maestro jedi app universal hacer la versi n iphone del lector en principio esta tarea la he dejado para cuando suba suba la aplicaci n a la app store en serio ir subiendo a medida que lo vaya haciendo ya que como se ha comentado en el canal de dudas se valora sobre todo la funcionalidad y estructura del c digo extras a qu funcionalidades le a adir as antes de subirla a la app store se me ocurre la posibilidad de poder gestionar la librer a a adiendo y quitando libros adem s podemos poner un uitabbarcontroller en el navigator que contiene la uiview de la tabla para a adir funcionalidades b ponerle otro nombre con una plantilla c usando la plantilla anteriormente descrita qu otras versiones se te ocurren alguna que se pueda monetizar | os |
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CryptoList | cryptolist curated collection of blockchain amp cryptocurrency links sponsor img src https github com coinpride cryptolist blob master join cryptomemes png raw true width 467 https t me join cryptomemes contents media media magazines and blogs magazines and blogs medium medium telegram channels telegram channels news aggregators news aggregators price trackers price trackers ico trackers ico trackers exchanges exchanges communities communities chats chats forums forums other other twitter users and lists twitter users and lists jobs jobs wallets wallets altfolio altfolio useful tools useful tools video video youtube channels youtube channels movies movies learning learning books books courses courses other other trading tools trading tools for developers for developers api api other other podcasts podcasts mining mining games games sponsors sponsors contribute contribute license license media magazines and blogs cryptohackers https cryptohackers party cryptoworld 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cryptagon https cryptagon io powerful app to track portfolio automatic imports from exchanges cryptonomy https www cryptonomynow com portfolio tracking social forum chat ico listings news aggregator crypto academy android ios wallmine https wallmine com stocks and cryptocurrency portfolio tracker track both cryptocurrencies and stocks screeners heatmaps news push notifications cointracker https www cointracker io cryptocurrency asset tracker that automatically pulls all balances and transactions from exchanges and cryptocurrency wallets coin beat http www coinbeatapp com crypto tracker for managing over 1300 crypto currencies get instant push notification on price activity ios and android useful tools qr code generator https bitcoinqrcodegenerator win with this free tool you can instantly generate qr code for your bitcoin litecoin ethereum dogecoin address block explorer https tradeblock com bitcoin discover the bitcoin blockchain blocks online includes fees and miners information etherscan https etherscan io block explorer and analytics platform for ethereum eth gas station https ethgasstation info consumer oriented metrics for the ethereum gas market metamask https metamask io run ethereum dapps right in your browser without running full node atm map https bitcoinatmmap com find bitcoin atm s in your country statoshi https statoshi info realtime bitcoin node stats made by jameson lopp rsihunter https rsihunter com find oversold and overbought cryptocurrencies cryptofinance https cryptofinance ai google sheets add on to get cryptocurrencies rate in spreadsheets vanitygen plus https github com exploitagency vanitygen plus generate custom key addresses for 90 coins coinpop me https coinpop me create shareable page for all your cryptocurrency donation addresses if you had bought crypto https ifyouhadboughtcrypto com how rich would you be if you had invested in your favourite cryptocurrency vaulty io https vaulty io accept any cryptocurrency for your website blog or for general personal use with just one link bitcoin flip trading simulator https bitcoinflip app 101 realistic and fun crypto trading simulator for beginners qwallet https qwallet io search view the value of any ethereum wallet create your own token portfolio ethplorer https ethplorer io block explorer and analytics platform for ethereum etherchain https etherchain org ethereum blockchain explorer trivial co https trivial co network explorer and analytics platform for ethereum and erc 20 tokens happydapps https happydapps net web 3 0 browser compatibility check video youtube channels the cryptoverse https www youtube com channel uclnq34zbsjy2jqjerudfedw your cryptocurrency news dose world crypto network https www youtube com user worldcryptonetwork informative news digests crypt0 https www youtube com user obham001 omar is one of the most popular crypto bloggers he creates fascinating interviews ameer rosic https www youtube com user ameerrosic news interviews podcasts and reviews aantonop https www youtube com user aantonop presentations discussions qa by andreas the author of mastering bitcoin coinsummit https www youtube com channel ucr4qrczdxrcecav2w4nuleq coinsummit records startups showcases discussions and presentations bitcoinfilm https www youtube com channel uc42y8ajczq rjs wvtamglq short films about people using bitcoin bitcoin and cryptocurrency course https www youtube com channel ucncssleedtfyduhbvoqzfzq videos bitcoin and cryptocurrency technologies course 12 videos each 45 90 mins cryptoportfolio https www youtube com channel uci9poyyp f93jhfkhr2ma2g youtube channel reviewing icos and various cryptocurrencies cryptobud https www youtube com channel ucaektd4wejd n4apydpd3zw technical and fundamental analysis on how to invest in cryptocurrencies cryptotips https www youtube com cryptotips heidi provides you with insights into projects icos and ecosystem as well as creating videos for beginners ethercasts https www youtube com user ethercasts ancient ethereum news reviews and interviews channel bitcoin playlist https www youtube com playlist list pl05dep7gognikwydie4nurepfmmf9xpzf great list of 55 bitcoin talks decentralized tv https www youtube com channel ucuelj4vlhtwmpyilmdbjrlg blockchain bitcoin and cryptocurrency news culture and entertainment crypto daily https www youtube com channel uc67aeeecqfec92nvvcqkdha crypto news on mostly daily basis movies ulterior states http www iamsatoshi com real life conversations with some thought leaders within the bitcoin ecosystem the film took 3 years to complete the bitcoin doco https vimeo com 112223859 fascinating story focusing on the emergence of new world wide currency life inside secret chinese bitcoin mine https www youtube com watch v k8kua5b5k3i interesting story about the miners of the new millenium learning books mastering bitcoin https bitcoinbook info mostly for developers the second edition was published in june 2017 the internet of money https www amazon com internet money andreas m antonopoulos dp 1537000454 significance of bitcoin through series of essays spanning the exhilarating maturation of this technology ethereum https www amazon com ethereum blockchains decentralized autonomous organizations dp 1523930470 non technical guide to understand blockchains mostly focused on ethereum the age of cryptocurrency https www amazon com age cryptocurrency blockchain challenging economic dp 1250081556 insight into the modern financial system and the bitcoin industry programming cryptocurrencies and blockchains http yukimotopress github io blockchains learn to build your own blockchains and peer to peer central bank nodes from scratch courses coursera course https www coursera org learn cryptocurrency telling what is special about bitcoin and how it works at technical level created by princeton university udemy course https www udemy com bitcoin or how i learned to stop worrying and love crypto the definitive guide to understand what the bitcoin is and why we should care about them khan s academy course https www khanacademy org economics finance domain core finance money and banking bitcoin v bitcoin what is it the very basics of how blockchain bitcoin and encryption works pluralsight course https www pluralsight com courses bitcoin decentralized technology introduction to bitcoin and decentralized technology for beginners university of nicosia mooc https digitalcurrency unic ac cy free introductory mooc university of nicosia s free mooc massive open online course dfin 511 introduction to digital currencies other coinpride newsletter https coinpride com the most important crypto news and events one handcrafted email per week bitcoin wiki https en bitcoin it wiki main page technical specifications of the protocol as well as more basic information about how to buy sell or use bitcoins blockchain demo https blockchaindemo io this interactive demo will guide you through each component of blockchain step by step mycryptoguide https mycrypto guide the guide is meant to serve as both easy to understand introduction to the world of cryptocurrencies bad bitcoin https www badbitcoin org list of known scams hyips and ponzi schemes ethlist https github com scanate ethlist the crowdsourced ethereum reading list satoshi nakamoto whitepaper https bitcoin org bitcoin pdf bitcoin fundamentals described by the creator bitcoin developer docs https bitcoin org en developer guide detailed information about the bitcoin protocol and related specifications bitcoin forks https mapofcoins com bitcoin all bitcoin forks visualisation coin demo https coindemo io visual demonstration of how bitcoin transactions work rhymes with fiat https www rhymeswithfiat com bitcoin webcomic with educational notes whitepaper io https whitepaper io all whitepapers in one place trading tools quadency https quadency com crypto trading and real time portfolio analytics for developers api shapeshift https docs shapeshift io embed exchange in your app trusted by hundreds of apps bittrex https bittrex github io almighty api you can do anything with it poloniex https docs poloniex com the biggest exchange api php and python wrapper cryptowatch https cryptowat ch docs api public rest api providing basic information about all markets on cryptowatch microsoft baas https azure microsoft com en in solutions blockchain blockchain as service baas from microsoft azure coinmarketcap https coinmarketcap com api powerful json api covering 1000 coins limit 1 request per 6 sec coincap https docs coincap io simple and informative lots of altcoins history data no limits changenow https changenow io api docs easy integration of the cryptocurrency exchange limitless and fast coin swaps free of custody other bitpay https bitpay com use bitpay s retail ecommerce billing and donation tools to accept payments gekko https github com askmike gekko free and open source trading bot gui cli nodejs nice docs 18 exchanges including bitfinex bitstamp and poloniex zenbot https github com deviavir zenbot free and open source trading bot cli nodejs gdax poloniex kraken bittrex quadriga and gemini web3 js https github com ethereum web3 js ethereum js api requires nodejs npm and running node openzeppelin https openzeppelin org openzeppelin is open framework of reusable and secure smart contracts in the solidity language 1500 slack community members geth https www ethereum org cli cli tools for ethereum python c go implementations embark https github com iurimatias embark framework framework for serverless decentralized applications using ethereum ipfs and other platforms js truffle http truffleframework com the most popular framework for ethereum js ropsten ethereum faucet http faucet bitfwd xyz instantly get ether to experiment on the ropsten testnet kovan ethereum faucet https gitter im kovan testnet instantly get ether to experiment on the kovan testnet ccxt https github com ccxt ccxt js python php library for cryptocurrency trading and e commerce with support for many exchanges and merchant apis quiknode https quiknode io cloud hosted ethereum nodes for developers and investors consensys academy https consensys net academy resources getting started resources by consensys portis https portis io javascript sdk that lets people run your ethereum dapp in the browser using the same account on all their devices podcasts updated october 2019 epicenter https epicenter tv the podcast at the forefront of the decentralized technology revolution bitcoin knowledge http www bitcoin kn learn about blockchain and fintech blockchannel https soundcloud com blockchannelshow dedicated to educating the world on the power of blockchain based technologies like bitcoin ethereum and zcash off the chain https blockworksgroup io off the chain podcast host anthony pomp pompliano talks to some of the most respected names in crypto and wall street to find out how intelligent investors from the new and old financial system are thinking about digital assets unchained your no hype resource for all things crypto https unchainedpodcast com this weekly hour long podcast with host laura shin dives deep into the people building the decentralized internet the details of this technology that could underpin our future and some of the thorniest topics in crypto such as regulation security and privacy what grinds my gears https blockworksgroup io what grinds my gears podcast from meltem demirors and jill carlson what grinds my gears is a podcast about the bizarre and buzzworthy happenings in the world of cryptocurrency each week they delve into one key theme in crypto and examine this theme through a broader financial political and cultural lens what bitcoin did https www whatbitcoindid com podcast in this podcast you will hear host peter mccormack speak with crypto traders miners venture capitalist investors technical developers ceos journalist and other people driving forward the growth of bitcoin and other cryptocurrencies untold stories with charlie shrem https blockworksgroup io untold stories podcast host charlie shrem dives deep into the lives and personal histories of some of crypto s most influential leaders tales from the crypt https anchor fm tales from the crypt tales from the crypt is a podcast hosted by marty bent about bitcoin join marty editor in chief of the best newsletter in crypto as he sits down to discuss bitcoin with interesting people the token daily with soona amhaz https blockworksgroup io the token daily podcast host soona amhaz sits down with the movers and shakers of the crypto industry to discuss the big ideas they spend their days thinking about soona and her guests examine everything from industry trends to what books they re reading to human psychology and investing the flippening https blog nomics com category flippening flippening is for cryptocurrency investors each week host clay collins discusses the cryptocurrency economy new investment strategies for maximizing returns and stories from the front lines of financial disruption the chain reaction podcast https fiftyonepercent podbean com host tom shaughnessy of delphi digital converses with the top names in crypto and blockchain unconfirmed insights and analysis from the top minds in crypto https unchainedpodcast com category unconfirmed this weekly crypto podcast reveals how the marquee names in crypto are reacting to the week s top headlines with host laura shin the guests also discuss what they re thinking about these days and reveal what they believe is on the horizon in crypto the unhashed podcast https www spreaker com show the unhashed podcast bitcoin blockchain unhashed breaks down the latest in bitcoin news and developments and puts them into terms everyone can understand expect to be both entertained and educated about cryptocurrencies and blockchain the scoop https www theblockcrypto com the scoop podcast the block s team led by frank chaparro draw out the freshest and deepest insights about digital assets from traditional wall street crypto native fortune 500 and many other crypto ecosystem leaders base layer https acrabaselayer podbean com base layer with host david nage will be providing insights from founders and investors in the base layer of cryptoassets blockchain innovation interviewing the brightest minds in blockchain https blockchain global blockchain innovation blockchain innovation is where host frederick munawa interviews the brightest minds in blockchain and cryptocurrency entrepreneurs executives and top academics to discuss present and future applications of blockchain technology blockchain insider https bi 11fs com blockchain insider hosted by simon taylor and colin platt is a dedicated podcast specializing in bitcoin blockchain and distributed ledger technology dlt simon and colin break down the week s news with expertise and enthusiasm for the blockchain and digital currency sector let s talk crypto https schoolofcrypto com let s talk crypto with barry moore and tom galeski breaks it all down in easy to understand terms and helps you learn and earn in the age of cryptocurrencies blockchain 2025 https podcast bitcoin com s9 blockchain 2025 blockchain is a technology that will disrupt nearly every industry host matt aaron and blake moore explore one industry in every episode ibm blockchain pulse https www ibm com blogs blockchain category podcast host and blockchain evangelist matt hooper engages with the planet s most dynamic blockchain thought leaders explorers and innovators to discover the countless new ways blockchain is leaping from theory to reality from entertainment to identity from payments to secure supply chain transparency messari s unqualified opinions https blockworksgroup io unqualified opinions unqualified opinions is a podcast hosted by messari s ceo ryan selkis featuring candid fast paced interviews with crypto s top builders and investors the bitcoin podcast network https thebitcoinpodcast com category podcast one of the originals and hands down one of the best sources of information on the crypto market available online today crypto street podcast https cryptostreetpod podbean com the crypto street podcast is a cryptocurrency podcast hosted by the three twitter influencers k1llerwh4le 13prince31 and cryptodale with a lot of humor they address current market trends mixed with opinion based discussions on the crypto ecosystem ledger cast https ledgerstatus com topic ledgercast ledger cast covers the cryptocurrency industry including technical and trading analysis fundamental analysis and anything else crypto and blockchain related the podcast is initiated by ledger status ledgerstatus who is known for his in depth technical analysis of cryptoassets mining coinwarz https www coinwarz com mining profitability calculators help choosing perfect coin to mine whattomine https whattomine com find the most profitable cryptocurrencies to mine bitcoin difficulty https bitcoinwisdom com bitcoin difficulty realtime mining statistics mining pools https blockchain info pools mining pool hashrate distribution whatismyhashrate http www whatismyhashrate com measure your browser s hashrate coinhive https coinhive com install js miner on your website bfgminer https github com luke jr bfgminer multi threaded miner for asics built in c games cryptokitties https www cryptokitties co collect breed and trade rare kitties in one of the world s first blockchain games sponsors img src https raw githubusercontent com coinpride cryptolist master sponsors findbitcoinatm sponsor logo png https www findbitcoinatm com au img height 70 src https raw githubusercontent com coinpride cryptolist master sponsors crypto weekly list png https cryptoweekly co list ref coinpride img width 240 src https raw githubusercontent com coinpride cryptolist master sponsors quadency jpg https quadency com utm source githubcl utm medium sponsorship utm campaign cryptolist contribute found a nice link noticed a bug feel free to contribute you are so much welcome but read the contributing md https github com coinpride cryptolist blob master contributing md first license a rel license href https creativecommons org licenses by sa 4 0 img alt creative commons license style border width 0 src https licensebuttons net l by sa 4 0 88x31 png a br this work is licensed under a rel license href https creativecommons org licenses by sa 4 0 creative commons attribution sharealike 4 0 international license a | bitcoin cryptocurrency ethereum altcoins telegram-channel blockchain news-aggregator list btc eth api resourses links directory folder collection | blockchain |
dictionary-app | dut dictionary android app table of contents about about features features screenshots screenshots collaborators collaborators license license about p dut dictionary is a free dictionary app with english language learning tools and free word games built for every level of learner p p providing access to information about over 300 000 english words including part of speech definitions synonyms examples and pronunciation this english dictionary and thesaurus for android is optimized with your mobile device in mind to help you learn english or imporve your english vocabulary p p using this app we provides vocabulary tests with groups of related words for example beer related to bar alcohol coffee whereas coffee is related to hot drink cup word associations is very effective to vocabulary learning because it helps people to understand words quickly and memorize them effectively just select which word is most closely related to the given group of words p features the android app lets you look up words using wordsapi save words to favourites show list of favourite words remove words from favourites filter words in favourites vocabulary test with levels and exam specific words sat gmat etc using word quiz api customized word association quiz for game and e learning software completely ad free screenshots table tr td main activity td td detail activity td tr tr td img src https i imgur com 2det7zt jpeg width 360 height 640 td td img src https i imgur com nncg6g4 jpg width 360 height 640 td tr tr td choose level and area for quiz td td quiz activity td tr tr td img src https i imgur com sqjcdlu jpg width 360 height 640 td td img src https i imgur com vuv2ogg jpg width 360 height 640 td tr table collaborators a href https github com hoanguyendn61 dictionary app graphs contributors img src https contrib rocks image repo hoanguyendn61 dictionary app a license this project is licensed under the terms of a href https github com hoanguyendn61 dictionary app blob master license md the mit license a | android-app android-dictionary dictionary-app dictionary-application | front_end |
Professional-JavaScript | github issues https img shields io github issues trainingbypackt professional javascript svg https github com trainingbypackt professional javascript issues github forks https img shields io github forks trainingbypackt professional javascript svg https github com trainingbypackt professional javascript network github stars https img shields io github stars trainingbypackt professional javascript svg https github com trainingbypackt professional javascript stargazers prs welcome https img shields io badge prs welcome brightgreen svg https github com trainingbypackt professional javascript pulls professional javascript in depth knowledge of javascript makes it easier to learn a variety of other frameworks including react angular and related tools and libraries this course is designed to help you cover the core javascript concepts you need to build modern applications you ll start by learning how to represent an html document in the document object model dom next you ll combine your knowledge of the dom and node js to create a web scraper for practical situations as you read through further lessons you ll create a node js based restful api using the express library for node js you ll also understand how modular designs can be used for better reusability and collaboration with multiple developers on a single project later chapters will guide you through building unit tests which ensure that the core functionality of your program is not affected over time the course will also demonstrate how constructors async await and events can load your applications quickly and efficiently finally you ll gain useful insights into functional programming concepts such as immutability pure functions and higher order functions by the end of this course you ll have the skills you need to tackle any real world javascript development problem using a modern javascript approach both for the client and server sides what you will learn apply the core concepts of functional programming build a node js project that uses the express js library to host an ap create unit tests for a node js project to validate it use the cheerio library with node js to create a basic web scraper develop a react interface to build processing flows use callbacks as a basic way to bring control back the examples of this title has been implemented in the windows mac linux operating system software requirement we also recommend that you have the following software installed in advance os windows 7 sp1 64 bit windows 8 1 64 bit windows 10 64 bit linux ubuntu 16 04 or later debian red hat or suse or the latest version of macos browser google chrome mozilla firefox latest version node js 10 16 3 lts https nodejs org en | javascript rest-api nodejs api graphql htmldom document-object-model redux react arrays | front_end |
cgc-multicloud-madness | cgc multicloud madness https acloudguru com blog engineering cloudguruchallenge multi cloud madness | cloud |
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CovenantSQL | p align center img src logo covenantsql horizontal png width 760 p p align center a href https goreportcard com report github com covenantsql covenantsql img src https goreportcard com badge github com covenantsql covenantsql style flat square alt go report card a a href https codecov io gh covenantsql covenantsql img src https codecov io gh covenantsql covenantsql branch develop graph badge svg alt coverage a a href https travis ci org covenantsql covenantsql img src https travis ci org covenantsql covenantsql png branch develop alt build status a a href https opensource org licenses apache 2 0 img src https img shields io badge license apache 202 0 blue svg alt license a a href https godoc org github com covenantsql covenantsql img src https img shields io badge godoc reference blue svg alt godoc a a href https formulae brew sh formula cql img src https img shields io homebrew v cql svg color blue label brew 20install 20cql alt homebrew a p https github com covenantsql covenantsql blob develop readme zh md covenantsql cql is a byzantine fault tolerant relational database built on sqlite serverless free high availabile auto sync database service for serverless app sql most sql 92 support decentralize running on open internet without central coordination privacy access with granted permission and encryption pass immutable query history in cql is immutable and trackable permission column level acl and sql pattern whitelist what is cql open source alternative of amazon qldb low cost dbaas just like filecoin ipfs is the decentralized file system cql is the decentralized database quick start cql client supports macos x 10 9 linux 2 6 23 x86 x86 64 armeabi v7a arm64 v8a details summary developer guide summary p macos homebrew users can just run bash brew install cql non homebrew users can run bash sudo bash c curl l https mac gridb io cql tar xzv c usr local bin strip components 1 linux just run bash sudo bash c curl l https linux gridb io cql tar xzv c usr local bin strip components 1 to continue testnet quickstart https developers covenantsql io docs en quickstart sdks covenantsql testnet is already released have a try https developers covenantsql io docs quickstart golang client java https github com covenantsql cql java driver nodejs https github com covenantsql covenantsql proxy js python https github com covenantsql cql python driver microsoft excel by community https github com melancholiaforever cql excel p details how cql works 3 layers arch covenantsql 3 layer design https cdn jsdelivr net gh covenantsql docs b7143254adb804dff0e3bc1f2f6ab11ad9cd44f5 website static img 2layers svg layer 1 global consensus layer the main chain the middle ring in the architecture diagram there will only be one main chain throughout the network mainly responsible for database miner and the user s contract matching transaction settlement anti cheating shard chain lock hash and other global consensus matters layer 2 sql consensus layer shard chain rings on both sides each database will have its own separate shard chain mainly responsible for the signature delivery and consistency of the various transactions of the database the data history of the permanent traceability is mainly implemented here and the hash lock is performed in the main chain layer 3 datastore layer database engine with sql 92 support each database has its own independent distributed engine mainly responsible for database storage encryption query processing signature efficient indexing details summary for more details summary p consensus algorithm cql supports 2 kinds of consensus algorithm 1 dpos delegated proof of stake is applied in eventually consistency mode database and also layer 1 global consensus layer in blockproducer cql miners pack all sql queries and its signatures by the client into blocks thus form a blockchain we named the algorithm xenomint https github com covenantsql covenantsql tree develop xenomint 2 bft raft byzantine fault toleranted raft sup bft raft bft raft sup is applied in strong consistency mode database we named our implementation kayak https github com covenantsql covenantsql tree develop kayak the cql miner leader does a two phase commit with kayak to support transaction sup transaction transaction sup cql database consistency mode and node count can be selected in database creation with command cql create useeventualconsistency true node 3 comparison ethereum hyperledger fabric amazon qldb covenantsql dev language solidity ewasm chaincode go nodejs python golang java php nodejs matlab dev pattern smart contract chaincode sql sql open source y y n y nodes for ha 3 15 3 column level acl n y y data format file key value document file sup fuse fuse sup key value structured storage encryption n api y y data desensitization n n n y multi tenant diy diy n y throughput 1s delay 15 10 tx s 3500 tx s 11065 tx s eventually consistency br 1866 tx s strong consistency consistency delay 2 6 min 1 s 10 ms secure for open internet y n only in aws y consensus pow pos casper cft dpos eventually consistency br bft raft strong consistency footnotes a name bft raft bft raft a a cql leader offline needs cql block producer to decide whether to wait for leader online for data integrity or promote a follower node for availability this part is still under construction and any advice is welcome a name transaction transaction a talking about acid cql has full consistency isolation durability and a limited atomicity support that is even under strong consistency mode cql transaction is only supported on the leader node if you want to do read v v write v back parallelly and atomically then the only way is read v from the leader v write v back to leader a name fuse fuse a cql has a simple fuse https github com covenantsql covenantsql tree develop cmd cql fuse support adopted from cockroachdb the performance is not very ideal and still has some issues but it can pass fio test like bash fio debug io loops 1 size 8m filename mnt fiotest tmp stonewall direct 1 name seqread bs 128k rw read name seqwrite bs 128k rw write name 4krandread bs 4k rw randread name 4krandwrite bs 4k rw randwrite network stack dh rpc rpc tls cert dht layer implementation rpc net rpc naming c onsistent s ecure dht https godoc org github com covenantsql covenantsql consistent pooling session pool multiplex smux https github com xtaci smux transport security e nhanced tls https github com covenantsql research wiki etls enhanced transport layer security network tcp or kcp for optional later test tools we use g lobal n etwork t opology e mulator https github com covenantsql gnte is used for network emulating liner consistency test https github com anishathalye porcupine papers our team members published thunder crystal a novel crowdsourcing based content distribution platform https dl acm org citation cfm id 2736085 analyzing streaming performance in crowdsourcing based video service systems https ieeexplore ieee org abstract document 7114727 performance analysis of thunder crystal a crowdsourcing based video distribution platform https ieeexplore ieee org abstract document 7762143 that inspired us bitcoin a peer to peer electronic cash system https bitcoin org bitcoin pdf s kademlia https github com thunderdb research wiki secure kademlia s kademlia a practicable approach towards secure key based routing https ieeexplore ieee org document 4447808 vsql verifying arbitrary sql queries over dynamic outsourced databases https ieeexplore ieee org abstract document 7958614 p details performance strong consistency bench result 2 miners 8 core aws c5 2xlarge covenantsql bench logo bench png as seen above the concurrency pressure on the database increased gradually in the first 5 hours and the write tps also increased when the tps no longer grows the concurrent pressure is maintained for 100 hours demos covenantforum https demo covenantsql io forum weibo bot blockpin https weibo com blockpin markdown editor with covenantsql sync https github com covenantsql stackedit web admin for covenantsql https github com covenantsql adminer how covenantsql works video https youtu be 2mz5poxxaqm t 106 use cases details summary traditional app app use cases summary p traditional app privacy data if you are a developper of password management tools just like 1password https 1password com or lastpass https www lastpass com you can use cql as the database to take benefits 1 serverless no need to deploy a server to store your user s password for sync which is the hot potato 2 security cql handles all the encryption work decentralized data storage gives more confidence to your users 3 regulation cql naturally comply with gdpr https en wikipedia org wiki general data protection regulation iot storage cql miners are deployed globally iot node can write to nearest cql miner directly 1 cheaper without passing all the traffic through a gateway you can save a large bandwidth fee and cql is a shared economic database which makes storage cheaper 2 faster cql consensus protocol is designed for internet where network latency is unavoidable open data service for example you are the most detailed bitcoin ohlc data maintainer you can directly expose an online sql interface to your customers to meet a wide range of query needs 1 cql can limit specific sql query statements to meet the needs while also balancing data security 2 cql can record sql query records on the blockchain which is very convenient for customers to check their bills for long tail customers and billing like this https explorer dbhub org dbs 7a51191ae06afa22595b3904dc558d41057a279393b22650a95a3fc610e1e2df requests f466f7bf89d4dd1ece7849ef3cbe5c619c2e6e793c65b31966dbe4c7db0bb072 3 for customers with high performance requirements slave nodes can be deployed at the customer to meet the needs of customers with low latency queries while enabling almost real time data updates secure storage thanks to the cql data history is immutable cql can be used as a storage for sensitive operational logs to prevent hacking and erasure access logs app storing data on bitcoin or ethereum is quite expensive 4305 mb on ethereum 2018 05 15 programming is very complicated due to the lack of support for structured data storage cql gives you a low cost structured sql database and also provides more room for app to exchange data with real world p details testnet quick start https developers covenantsql io mainchain explorer http scan covenantsql io sqlchain explorer https explorer dbhub org demo forum https demo covenantsql io forum contact blog https medium com covenant labs youtube https www youtube com channel uce9p tmiexshw2ggv5qbmzw mail mailto webmaster covenantsql io forum https demo covenantsql io forum a href https twitter com intent follow screen name covenantlabs img src https img shields io twitter url https twitter com fold left svg style social label follow 20 40covenantlabs alt follow on twitter a join the chat at https gitter im covenantsql covenantsql https badges gitter im covenantsql covenantsql svg https gitter im covenantsql covenantsql utm source badge utm medium badge utm campaign pr badge utm content badge add covenantsql to join wechat group p align left img src logo wechat jpeg height 100 p | database blockchain sql bft p2p crypto decentralized qldb covenantsql cql sql-database dbaas | blockchain |
moko-test | moko test https user images githubusercontent com 5010169 128706360 5b66ad24 a732 4e20 8e7f feb8f98e997f png github license https img shields io badge license apache 20license 202 0 blue svg style flat http www apache org licenses license 2 0 download https img shields io maven central v dev icerock moko test core https repo1 maven org maven2 dev icerock moko test core kotlin version https kotlin version aws icerock dev kotlin version group dev icerock moko name test core mobile kotlin test utils this is a kotlin multiplatform library that provides utilities for run tests table of contents features features requirements requirements installation installation usage usage samples samples set up locally set up locally contributing contributing license license features requirements gradle version 6 8 android api 16 ios version 11 0 installation root build gradle groovy allprojects repositories mavencentral project build gradle groovy dependencies commontestapi dev icerock moko test core 0 6 1 commontestapi dev icerock moko test roboelectric 0 6 1 for android roboelectric tests support usage runblocking kotlin import dev icerock moko test runblocking fun test runblocking some suspend functions testcases kotlin class mytests testcases override val rules list rule listof instanttaskrule apply https developer android com reference android arch core executor testing instanttaskexecutorrule for android test fun my test also available creation of own rules by inherit dev icerock moko test cases testcases rule kotlin class instanttaskrule testcases rule override fun setup do some action before each test override fun teardown do some action after each test roboelectric support kotlin class mytests roboelectrictestcases override val rules list rule listof test fun my test samples please see more examples in the sample directory sample set up locally the test directory test contains the test library in sample directory sample contains sample mpp library with tests contributing all development both new features and bug fixes is performed in the develop branch this way master always contains the sources of the most recently released version please send prs with bug fixes to the develop branch documentation fixes in the markdown files are an exception to this rule they are updated directly in master the develop branch is pushed to master on release for more details on contributing please see the contributing guide contributing md license copyright 2021 icerock mag inc 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 | kotlin-multiplatform kotlin-native kotlin android ios | front_end |
dtube | p align center img align center src https github com akhileshthite dtube blob main src logo png width 200 height 200 img p h1 align center dtube h1 p aign center p align center decentralized video sharing social media platform on ethereum blockchain p p align center website a href https dtube eth on fleek co a https dtube eth on fleek co p p div align center img src https visitor badge glitch me badge page id akhileshthite dtube alt visitors img src https img shields io github v release akhileshthite dtube color 1fc71f alt github release div contracts https goerli etherscan io address 0x678af4458950d0aefbc427e663050f50ebdab52a https mumbai polygonscan com address 0xffded6c68ccf4ed30d2b9c85ba21a926cccc8e0e deprecated chains learn more https blog ethereum org 2022 06 21 testnet deprecation https rinkeby etherscan io address 0xcda87299367d6b29fef13ca08448bfaaf2e4a175 https ropsten etherscan io address 0xc6eb38be0949a63f7c3ac36a053de209970fa19b about use this dapp for educational purposes only dtube is not responsible for the harm caused by the content you re uploading dtube uploads the video files to ipfs https ipfs tech by using web3 storage https web3 storage and stores those ipfs cid https docs ipfs tech concepts content addressing s to the blockchain network read the step by step tutorial build a social media dapp deploy it on polygon https learn figment io tutorials build a social media dapp and deploy it on polygon to learn how to buidl it from scratch if you have any queries then please create a discussion thread https github com akhileshthite dtube discussions this dapp is inspired by the dapp university https www youtube com channel ucy0xl8v6nzzfcwzhcgb8orq youtube channel demo div align center img src https github com akhileshthite dtube blob main demo gif div development instructions installation setup make sure you have truffle installed on your computer sh install truffle globally npm install g truffle ensure you create an env file in root directory then to access the ethereum network node create a project on infura https infura io and copy paste the infura project id url in env with a variable name react app infura rinkeby or any network you like sh react app infura rinkeby https rinkeby infura io v3 your project id paste the 12 word secret recovery phrase of your preferably newly generated and testnet only metamask wallet in env with the variable name react app mnemonic this will be loaded by truffle at runtime and the environment variable can then be accessed with process env react app mnemonic sh react app mnemonic for example put your twelve word bip39 secret recovery phrase here for development and testing you have to create your own web3 storage api token to do that login to web3 storage https web3 storage create a new api token copy the api token then create a env file in the root directory bash react app api token paste your api token or to develop on ganache blockchain open ganache and import the accounts by adding your ganache private keys in metamask sh ganache cli deployment to deploy the smart contracts on blockchain networks follow the given truffle command below sh truffle migrate network network name truffle migrate network rinkeby reset run all migrations from the beginning instead of running from the last completed migration for more information read truffle docs https trufflesuite com docs truffle react client start react app sh npm start starting the development server if dealing with javascript heap out of memory error after npm start then use the following command to solve it export node options max old space size 8192 note i cannot update this repo main branch with react hooks because the initial educational tutorial https learn figment io tutorials build a social media dapp and deploy it on polygon was written with react classes however if you want to work on this issue then please fork the main branch and push your changes to react hooks branch and send a pull request https github com akhileshthite dtube pulls for the same license dtube is licensed under the mit license https github com akhileshthite dtube blob main license hr hope you ve learned something new don t forget to leave a and a href https twitter com cryptoroots xyz target blank img src https img shields io twitter follow akhileshthite style social alt twitter a | ethereum blockchain solidity dapp web3 hacktoberfest social-media hactoberfest2022 | blockchain |
oyente | oyente an analysis tool for smart contracts gitter gitter badge gitter url license gpl v3 license badge license badge url build status https travis ci org melonproject oyente svg branch master https travis ci org melonproject oyente this repository is currently maintained by xiao liang yu yxliang01 https github com yxliang01 if you encounter any bugs or usage issues please feel free to create an issue on our issue tracker https github com melonproject oyente issues quick start a container with required dependencies configured can be found here https hub docker com r luongnguyen oyente the image is however outdated we are working on pushing the latest image to dockerhub for your convenience if you experience any issue with this image please try to build a new docker image by pulling this codebase before open an issue to open the container install docker and run docker pull luongnguyen oyente docker run i t luongnguyen oyente to evaluate the greeter contract inside the container run cd oyente oyente python oyente py s greeter sol and you are done note if need the version of oyente https github com melonproject oyente tree 290f1ae1bbb295b8e61cbf0eed93dbde6f287e69 referred to in the paper run the container from here https hub docker com r hrishioa oyente to run the web interface execute docker run w oyente web p 3000 3000 oyente latest bin rails server custom docker image build docker build t oyente docker run it p 3000 3000 e oyente oyente oyente oyente latest open a web browser to http localhost 3000 for the graphical interface installation execute a python virtualenv python m virtualenv env source env bin activate install oyente via pip pip2 install oyente dependencies the following require a linux system to fufill macos instructions forthcoming solc https github com melonproject oyente solc evm https github com melonproject oyente evm from go ethereum full installation install the following dependencies solc sudo add apt repository ppa ethereum ethereum sudo apt get update sudo apt get install solc evm from go ethereum https github com ethereum go ethereum 1 https geth ethereum org downloads or 2 by from ppa if your using ubuntu sudo apt get install software properties common sudo add apt repository y ppa ethereum ethereum sudo apt get update sudo apt get install ethereum z3 https github com z3prover z3 releases theorem prover version 4 5 0 download the source code of version z3 4 5 0 https github com z3prover z3 releases tag z3 4 5 0 install z3 using python bindings python scripts mk make py python cd build make sudo make install requests https github com kennethreitz requests library pip install requests web3 https github com pipermerriam web3 py library pip install web3 evaluating ethereum contracts evaluate a local solidity contract python oyente py s contract filename evaluate a local solidity with option a to verify assertions in the contract python oyente py a s contract filename evaluate a local evm contract python oyente py s contract filename b evaluate a remote contract python oyente py ru https gist githubusercontent com loiluu d0eb34d473e421df12b38c12a7423a61 raw 2415b3fb782f5d286777e0bcebc57812ce3786da puzzle sol and that s it run python oyente py help for a list of options paper the accompanying paper explaining the bugs detected by the tool can be found here https www comp nus edu sg prateeks papers oyente pdf miscellaneous utilities a collection of the utilities that were developed for the paper are in misc utils use them at your own risk they have mostly been disposable 1 generate graphs py contains a number of functions to get statistics from contracts 2 get source py the get contract code function can be used to retrieve contract source from etherscan https etherscan io 3 transaction scrape py contains functions to retrieve up to date transaction information for a particular contract benchmarks note this is an improved version of the tool used for the paper benchmarks are not for direct comparison to run the benchmarks it is best to use the docker container as it includes the blockchain snapshot necessary in the container run batch run py after activating the virtualenv results are in results json once the benchmark completes the benchmarks take a long time and a lot of ram in any but the largest of clusters beware some analytics regarding the number of contracts tested number of contracts analysed etc is collected when running this benchmark contributing checkout out our contribution guide https github com melonproject oyente blob master contributing md and the code structure here https github com melonproject oyente blob master code md gitter badge https img shields io gitter room melonproject oyente js svg style flat square gitter url https gitter im melonproject oyente utm source badge utm medium badge utm campaign pr badge utm content badge license badge https img shields io badge license gpl 20v3 blue svg style flat square license badge url license | ethereum blockchain smart-contracts security-analyzers | blockchain |
greenhouse_gas_emissions_de_pipeline | codefactor grade https img shields io codefactor grade github dylanzenner greenhouse gas emissions de pipeline code style black https img shields io badge code 20style black 000000 svg https github com psf black python 3 8 https img shields io badge python 3 8 blue svg https www python org downloads release python 360 github last commit https img shields io github last commit dylanzenner greenhouse gas emissions de pipeline github repo size https img shields io github repo size dylanzenner greenhouse gas emissions de pipeline sf greenhouse gas emissions de pipeline tracking greenhouse gas emissions in san francisco across department source type consumption units co2 emissions and fiscal year completly hosted in the aws ecosystem including a dashboard built with amazon quicksight if you would like to replicate this project follow the readme md file in the cfn directory architecture architecture greenhouse gas emissions architecture diagram png data is sourced from san francisco s open data api https dev socrata com foundry data sfgov org pxac sadh as json documents containing information on greenhouse gas emissions throughout san francisco a series of lambda functions integrated with sqs orchestrate the data movement and transformation throughout the pipeline the presentation layer is created using amazon quicksight infrastructure the project is housed in the aws ecosystem packaged into two cloudformation templates and utilizes the following resources vpc custom built vpc with two subnets 1 private 1 public igw natgw and route tables security groups dynamodb on demand capacity partition key on the uuid field 3 lambda functions 1 for pulling data from the api and sending it in batches to an sqs queue 1 for transforming the data from the sqs queue and sending it to dynamodb 1 for exporting the data to s3 secrets manager for storing connection variables and api tokens s3 bucket with versioning enabled for storing the exported dynamodb table data in json format sqs with a deadletter queue for receiving data in batches from the api deadletter queue for automatic retry of failed messages sns for sending failure messages to an email address cloudwatch time based events for invoking the first lambda function and triggering the pipeline amazon quicksight for the visualization layer dashboard dashboard images dashboard1 png dashboard images dashboard4 png dashboard images dashboard5 png dashboard images dashboard2 png dashboard images dashboard3 png dashboard images dashboard6 png points moving forward just like my previous project i had a ton of fun working on this project i received a lot of excellent feedback on my previous project and implemented some of the suggestions which i had received also just like last time i have some things i would like to point out the biggest change from my last project to this one is the utilization of cloudformation this was probably the most suggested implementation from the previous project i have to say it was definitly a great idea being able to spin up and spin down the entire infrastructure in a matter of minutes was amazing and saved me a ton of cost when i was done working on the project for the day i could spin down the infrastructure and not have to worry about getting it to work the next time i wanted to work on it another big change from the last project is the architecture diagram the previous diagram was cluttered and could be difficult to follow i tried my best to address those issues with a better looking diagram utilizing space for the subnets and vpc allowed me to space things out a bit more and in turn made the diagram much easier to follow i also implemented some event driven architecture with the automatic invocation of the lambda function which processes the data and sends it to dynamodb this was another recommendation from my last project so i wanted to implement it to get a feel for it and so i can work on implementing more of it in my next project the biggest issue i faced during this project was getting the data from the sqs queue i thought that the data for the messages was in the location of event messages 0 body at first but it is actually in event records 0 body the most confusing thing about this is if you print out the format of the message in your code editor the data will be in event messages 0 body but it is actually in event records 0 body this is also buried within the sqs documentation which was kind of frustrating development was much easier this time due to the utilization of cloudfomation being able to spin up down the entire project in a matter of minutes also saved me a ton of cost i implemented unit tests again but this time i think it was significantly harder to come up with good unit tests i did not want to test for connections or secret variables which wiped out almost all of the pull data py the only other place i could think of writing test functions was against the database so i implemented those i still utilized amazon quicksight for the dashboard this time i did want to go the tableau route but i have not worked with jdbc drivers before and that is what i needed in order to connect tableau to s3 that is something on my to do list i did not use athena because i was not trying to use sql to query the dataset i did want to send error messages to a slack channel with an sns integration origionally but the slack email integration which gives you a special email address in order to subscribe to notificatioin services i e sns is only available with the paid version of slack thus i decided to go with just sending emails to my personal address all in all it was another fun project and i learned a lot i already have the next project planned out it will consist of all the technologies which have been utilized in this project and my last one there will also be some suggestions from the last project which i did not implement in this one features that should be added i think the most notable feature which should be added is a way to check the dynamodb table for any duplicate records before the project is run a second time i was thinking of writing a script to scan the dynamodb table and put those scan results in a list and drop the id field then check that list before putting any item to dynamodb this is something i would love to get some feedback on before i implement it either way something needs to be done otherwise when the project is run a second third fourth etc time there will be duplicate data with the only difference being the id field for each record | data-engineering data-engineering-pipeline aws dynamodb aws-ecosystem cloudformation aws-lambda aws-secretsmanager aws-sns aws-sqs aws-s3 aws-cloudwatch-events | server |
color-stacks | color stacks a color palette generator for design systems the next step on the project will be to provide contextual help in the ui and allow the ability to vet the colors in the context of a ui playground development bootstrapped with vue cli deployed via sftp to dreamhost and served up from lokeshdhakar com projects color stacks | os |
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hello-rails-react | https img shields io badge microverse blueviolet project name description the project built with major languages frameworks technologies used live demo if available live demo link https livedemo com getting started this is an example of how you may give instructions on setting up your project locally modify this file to match your project remove sections that don t apply for example delete the testing section if the currect project doesn t require testing to get a local copy up and running follow these simple example steps prerequisites setup install usage run tests deployment authors author1 github githubhandle https github com githubhandle twitter twitterhandle https twitter com twitterhandle linkedin linkedin https linkedin com in linkedinhandle author2 github githubhandle https github com githubhandle twitter twitterhandle https twitter com twitterhandle linkedin linkedin https linkedin com in linkedinhandle contributing contributions issues and feature requests are welcome feel free to check the issues page issues show your support give a if you like this project acknowledgments hat tip to anyone whose code was used inspiration etc license this project is mit mit md licensed | server |
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ML-From-Scratch | machine learning from scratch about python implementations of some of the fundamental machine learning models and algorithms from scratch the purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way table of contents machine learning from scratch machine learning from scratch about about table of contents table of contents installation installation examples examples polynomial regression polynomial regression classification with cnn classification with cnn density based clustering density based clustering generating handwritten digits generating handwritten digits deep reinforcement learning deep reinforcement learning image reconstruction with rbm image reconstruction with rbm evolutionary evolved neural network evolutionary evolved neural network genetic algorithm genetic algorithm association analysis association analysis implementations implementations supervised learning supervised learning unsupervised learning unsupervised learning reinforcement learning reinforcement learning deep learning deep learning contact contact installation git clone https github com eriklindernoren ml from scratch cd ml from scratch python setup py install examples polynomial regression python mlfromscratch examples polynomial regression py p align center img src http eriklindernoren se images p reg gif width 640 p p align center figure training progress of a regularized polynomial regression model fitting br temperature data measured in link ping sweden 2016 p classification with cnn python mlfromscratch examples convolutional neural network py convnet input shape 1 8 8 layer type parameters output shape conv2d 160 16 8 8 activation relu 0 16 8 8 dropout 0 16 8 8 batchnormalization 2048 16 8 8 conv2d 4640 32 8 8 activation relu 0 32 8 8 dropout 0 32 8 8 batchnormalization 4096 32 8 8 flatten 0 2048 dense 524544 256 activation relu 0 256 dropout 0 256 batchnormalization 512 256 dense 2570 10 activation softmax 0 10 total parameters 538570 training 100 time 0 01 55 accuracy 0 987465181058 p align center img src http eriklindernoren se images mlfs cnn1 png width 640 p p align center figure classification of the digit dataset using cnn p density based clustering python mlfromscratch examples dbscan py p align center img src http eriklindernoren se images mlfs dbscan png width 640 p p align center figure clustering of the moons dataset using dbscan p generating handwritten digits python mlfromscratch unsupervised learning generative adversarial network py generator input shape 100 layer type parameters output shape dense 25856 256 activation leakyrelu 0 256 batchnormalization 512 256 dense 131584 512 activation leakyrelu 0 512 batchnormalization 1024 512 dense 525312 1024 activation leakyrelu 0 1024 batchnormalization 2048 1024 dense 803600 784 activation tanh 0 784 total parameters 1489936 discriminator input shape 784 layer type parameters output shape dense 401920 512 activation leakyrelu 0 512 dropout 0 512 dense 131328 256 activation leakyrelu 0 256 dropout 0 256 dense 514 2 activation softmax 0 2 total parameters 533762 p align center img src http eriklindernoren se images gan mnist5 gif width 640 p p align center figure training progress of a generative adversarial network generating br handwritten digits p deep reinforcement learning python mlfromscratch examples deep q network py deep q network input shape 4 layer type parameters output shape dense 320 64 activation relu 0 64 dense 130 2 total parameters 450 p align center img src http eriklindernoren se images mlfs dql1 gif width 640 p p align center figure deep q network solution to the cartpole v1 environment in openai gym p image reconstruction with rbm python mlfromscratch examples restricted boltzmann machine py p align center img src http eriklindernoren se images rbm digits1 gif width 640 p p align center figure shows how the network gets better during training at reconstructing br the digit 2 in the mnist dataset p evolutionary evolved neural network python mlfromscratch examples neuroevolution py model summary input shape 64 layer type parameters output shape dense 1040 16 activation relu 0 16 dense 170 10 activation softmax 0 10 total parameters 1210 population size 100 generations 3000 mutation rate 0 01 0 best individual fitness 3 08301 accuracy 10 5 1 best individual fitness 3 08746 accuracy 12 0 2999 best individual fitness 94 08513 accuracy 98 5 test set accuracy 96 7 p align center img src http eriklindernoren se images evo nn4 png width 640 p p align center figure classification of the digit dataset by a neural network which has br been evolutionary evolved p genetic algorithm python mlfromscratch examples genetic algorithm py ga description implementation of a genetic algorithm which aims to produce the user specified target string this implementation calculates each candidate s fitness based on the alphabetical distance between the candidate and the target a candidate is selected as a parent with probabilities proportional to the candidate s fitness reproduction is implemented as a single point crossover between pairs of parents mutation is done by randomly assigning new characters with uniform probability parameters target string genetic algorithm population size 100 mutation rate 0 05 0 closest candidate cjqljguplqzvpojmb fitness 0 00 1 closest candidate mcxzxdr nlfiwwgek fitness 0 01 2 closest candidate mcxzxdm nlfiwwgcx fitness 0 01 3 closest candidate smdsaklmhn kbiwkn fitness 0 01 4 closest candidate lotneajoaswfu z fitness 0 01 292 closest candidate geneticaalgorithm fitness 1 00 293 closest candidate geneticaalgorithm fitness 1 00 294 answer genetic algorithm association analysis python mlfromscratch examples apriori py apriori minimum support 0 25 minimum confidence 0 8 transactions 1 2 3 4 1 2 4 1 2 2 3 4 2 3 3 4 2 4 frequent itemsets 1 2 3 4 1 2 1 4 2 3 2 4 3 4 1 2 4 2 3 4 rules 1 2 support 0 43 confidence 1 0 4 2 support 0 57 confidence 0 8 1 4 2 support 0 29 confidence 1 0 implementations supervised learning adaboost mlfromscratch supervised learning adaboost py bayesian regression mlfromscratch supervised learning bayesian regression py decision tree mlfromscratch supervised learning decision tree py elastic net mlfromscratch supervised learning regression py gradient boosting mlfromscratch supervised learning gradient boosting py k nearest neighbors mlfromscratch supervised learning k nearest neighbors py lasso regression mlfromscratch supervised learning regression py linear discriminant analysis mlfromscratch supervised learning linear discriminant analysis py linear regression mlfromscratch supervised learning regression py logistic regression mlfromscratch supervised learning logistic regression py multi class linear discriminant analysis mlfromscratch supervised learning multi class lda py multilayer perceptron mlfromscratch supervised learning multilayer perceptron py naive bayes mlfromscratch supervised learning naive bayes py neuroevolution mlfromscratch supervised learning neuroevolution py particle swarm optimization of neural network mlfromscratch supervised learning particle swarm optimization py perceptron mlfromscratch supervised learning perceptron py polynomial regression mlfromscratch supervised learning regression py random forest mlfromscratch supervised learning random forest py ridge regression mlfromscratch supervised learning regression py support vector machine mlfromscratch supervised learning support vector machine py xgboost mlfromscratch supervised learning xgboost py unsupervised learning apriori mlfromscratch unsupervised learning apriori py autoencoder mlfromscratch unsupervised learning autoencoder py dbscan mlfromscratch unsupervised learning dbscan py fp growth mlfromscratch unsupervised learning fp growth py gaussian mixture model mlfromscratch unsupervised learning gaussian mixture model py generative adversarial network mlfromscratch unsupervised learning generative adversarial network py genetic algorithm mlfromscratch unsupervised learning genetic algorithm py k means mlfromscratch unsupervised learning k means py partitioning around medoids mlfromscratch unsupervised learning partitioning around medoids py principal component analysis mlfromscratch unsupervised learning principal component analysis py restricted boltzmann machine mlfromscratch unsupervised learning restricted boltzmann machine py reinforcement learning deep q network mlfromscratch reinforcement learning deep q network py deep learning neural network mlfromscratch deep learning neural network py layers mlfromscratch deep learning layers py activation layer average pooling layer batch normalization layer constant padding layer convolutional layer dropout layer flatten layer fully connected dense layer fully connected rnn layer max pooling layer reshape layer up sampling layer zero padding layer model types convolutional neural network mlfromscratch examples convolutional neural network py multilayer perceptron mlfromscratch examples multilayer perceptron py recurrent neural network mlfromscratch examples recurrent neural network py contact if there s some implementation you would like to see here or if you re just feeling social feel free to email mailto eriklindernoren gmail com me or connect with me on linkedin https www linkedin com in eriklindernoren | machine-learning deep-learning deep-reinforcement-learning machine-learning-from-scratch data-science data-mining genetic-algorithm | ai |
mobile-course-shenkar-s19 | software development for mobile devices environment software development for mobile devices environment by ziv levy shenkar engineering school spring 2019 submission guidlines guides guides | front_end |
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flair | alt text resources docs flair logo 2020 final day dpi72 png gh light mode only alt text resources docs flair logo 2020 final night dpi72 png gh dark mode only pypi version https badge fury io py flair svg https badge fury io py flair github issues https img shields io github issues flairnlp flair svg https github com flairnlp flair issues contributions welcome https img shields io badge contributions welcome brightgreen svg contributing md license mit https img shields io badge license mit brightgreen svg https opensource org licenses mit a very simple framework for state of the art nlp developed by humboldt university of berlin https www informatik hu berlin de en forschung en gebiete ml en and friends flair is a powerful nlp library flair allows you to apply our state of the art natural language processing nlp models to your text such as named entity recognition ner sentiment analysis part of speech tagging pos special support for biomedical data resources docs hunflair md sense disambiguation and classification with support for a rapidly growing number of languages a text embedding library flair has simple interfaces that allow you to use and combine different word and document embeddings including our proposed flair embeddings https www aclweb org anthology c18 1139 and various transformers a pytorch nlp framework our framework builds directly on pytorch https pytorch org making it easy to train your own models and experiment with new approaches using flair embeddings and classes now at version 0 12 2 https github com flairnlp flair releases state of the art models flair ships with state of the art models for a range of nlp tasks for instance check out our latest ner models language dataset flair best published model card demo english conll 03 4 class 94 09 94 3 yamada et al 2020 https doi org 10 18653 v1 2020 emnlp main 523 flair english 4 class ner demo https huggingface co flair ner english large english ontonotes 18 class 90 93 91 3 yu et al 2020 https www aclweb org anthology 2020 acl main 577 pdf flair english 18 class ner demo https huggingface co flair ner english ontonotes large german conll 03 4 class 92 31 90 3 yu et al 2020 https www aclweb org anthology 2020 acl main 577 pdf flair german 4 class ner demo https huggingface co flair ner german large dutch conll 03 4 class 95 25 93 7 yu et al 2020 https www aclweb org anthology 2020 acl main 577 pdf flair dutch 4 class ner demo https huggingface co flair ner dutch large spanish conll 03 4 class 90 54 90 3 yu et al 2020 https www aclweb org anthology 2020 acl main 577 pdf flair spanish 4 class ner demo https huggingface co flair ner spanish large many flair sequence tagging models named entity recognition part of speech tagging etc are also hosted on the hugging face model hub https huggingface co models library flair sort downloads you can browse models check detailed information on how they were trained and even try each model out online quick start requirements and installation in your favorite virtual environment simply do pip install flair flair requires python 3 8 example 1 tag entities in text let s run named entity recognition ner over an example sentence all you need to do is make a sentence load a pre trained model and use it to predict tags for the sentence python from flair data import sentence from flair nn import classifier make a sentence sentence sentence i love berlin load the ner tagger tagger classifier load ner run ner over sentence tagger predict sentence print the sentence with all annotations print sentence this should print console sentence i love berlin berlin loc this means that berlin was tagged as a location entity in this sentence to learn more about ner tagging in flair check out our ner tutorial https flairnlp github io docs tutorial basics tagging entities example 2 detect sentiment let s run sentiment analysis over an example sentence to determine whether it is positive or negative same code as above just a different model python from flair data import sentence from flair nn import classifier make a sentence sentence sentence i love berlin load the ner tagger tagger classifier load sentiment run ner over sentence tagger predict sentence print the sentence with all annotations print sentence this should print console sentence 4 i love berlin positive 0 9983 this means that the sentence i love berlin was tagged as having positive sentiment to learn more about sentiment analysis in flair check out our sentiment analysis tutorial https flairnlp github io docs tutorial basics tagging sentiment tutorials on our new fire flair documentation page https flairnlp github io docs intro you will find many tutorials to get you started in particular tutorial 1 basic tagging https flairnlp github io docs category tutorial 1 basic tagging how to tag your text tutorial 2 training models https flairnlp github io docs category tutorial 2 training models how to train your own state of the art nlp models tutorial 3 embeddings https flairnlp github io docs category tutorial 3 embeddings how to produce embeddings for words and documents there is also a dedicated landing page for our biomedical ner and datasets resources docs hunflair md with installation instructions and tutorials more documentation another great place to start is the book natural language processing with flair https www amazon com natural language processing flair understanding dp 1801072310 and its accompanying code repository https github com packtpublishing natural language processing with flair though it was written for an older version of flair and some examples may no longer work there are also good third party articles and posts that illustrate how to use flair training an ner model with flair https medium com thecyphy training custom ner model using flair df1f9ea9c762 training a text classifier with flair https towardsdatascience com text classification with state of the art nlp library flair b541d7add21f zero and few shot learning https towardsdatascience com zero and few shot learning c08e145dc4ed visualisation tool for highlighting the extracted entities https github com lunayach visner flair functionality and how to use in colab https www analyticsvidhya com blog 2019 02 flair nlp library python benchmarking ner algorithms https towardsdatascience com benchmark ner algorithm d4ab01b2d4c3 clinical nlp https towardsdatascience com clinical natural language processing 5c7b3d17e137 how to build a microservice with flair and flask https shekhargulati com 2019 01 04 building a sentiment analysis python microservice with flair and flask a docker image for flair https towardsdatascience com docker image for nlp 5402c9a9069e practical approach of state of the art flair in named entity recognition https medium com analytics vidhya practical approach of state of the art flair in named entity recognition 46a837e25e6b training a flair text classifier on google cloud platform gcp and serving predictions on gcp https github com robinvanschaik flair on gcp model interpretability for transformer based flair models https github com robinvanschaik interpret flair citing flair please cite the following paper https www aclweb org anthology c18 1139 when using flair embeddings inproceedings akbik2018coling title contextual string embeddings for sequence labeling author akbik alan and blythe duncan and vollgraf roland booktitle coling 2018 27th international conference on computational linguistics pages 1638 1649 year 2018 if you use the flair framework for your experiments please cite this paper https www aclweb org anthology papers n n19 n19 4010 inproceedings akbik2019flair title flair an easy to use framework for state of the art nlp author akbik alan and bergmann tanja and blythe duncan and rasul kashif and schweter stefan and vollgraf roland booktitle naacl 2019 2019 annual conference of the north american chapter of the association for computational linguistics demonstrations pages 54 59 year 2019 if you use our new flert models or approach please cite this paper https arxiv org abs 2011 06993 misc schweter2020flert title flert document level features for named entity recognition author stefan schweter and alan akbik year 2020 eprint 2011 06993 archiveprefix arxiv primaryclass cs cl if you use our tars approach for few shot and zero shot learning please cite this paper https kishaloyhalder github io pdfs tars coling2020 pdf inproceedings halder2020coling title task aware representation of sentences for generic text classification author halder kishaloy and akbik alan and krapac josip and vollgraf roland booktitle coling 2020 28th international conference on computational linguistics year 2020 contact please email your questions or comments to alan akbik http alanakbik github io contributing thanks for your interest in contributing there are many ways to get involved start with our contributor guidelines contributing md and then check these open issues https github com flairnlp flair issues for specific tasks license license the mit license mit flair is licensed under the following mit license the mit license mit copyright 2018 zalando se https tech zalando com 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 | pytorch nlp named-entity-recognition sequence-labeling semantic-role-labeling word-embeddings natural-language-processing machine-learning | ai |
pixel_mapper | pixel mapper a small collection of python scripts for semi automatic mapping of rgb pixels in 3d space using computer vision designed particularly for mapping out trees that have been wrapped with pixels final output is an xlights file format 3d model of the pixels br br example of a pixel wrapped tree and the resulting 3d model in xlights br img alt wrapped tree src https github com aaknitt pixel mapper blob main images tree jpg width 40 br some poorly shot video of the tree in action on halloween during trick or treating https www youtube com watch v ifu ymddcyu and red white blue on election night https www youtube com watch v pfob23fctpq prerequisites opencv python https pypi org project opencv python lumos library https github com ptone lumos to control pixels via e1 31 sacn use this fork https github com tonyazzolino lumos for python 3 compatibility numpy https numpy org imutils https pypi org project imutils scikit image https scikit image org playsound https pypi org project playsound matplotlib https matplotlib org workflow overview 1 locate camera positions equilateral triangle around pixel array 2 for each camera location level align camera capture data using computer vision with manual assist 3 after data from all three cameras is captured combine three 2d data files into one xlights 3d model file locate camera positions the three camera locations must be exactly 120 degrees apart around the object to be mapped in my case a tree forming the three vertices of an equilateral triangle the camera should be as close to the object tree as possible while still fully capturing the entire object within the field of view once the required distance from the object has been determined based on its size and the camera fov by moving the camera around and viewing the image the length of the edges of the equilateral triangle can be determined once these lengths are known a simple alignment method is to cut three strings to the same length of the triangle edge and pull them tight into the shape of the triangle mark the locations of the three vertices on the ground with spray paint these are the three camera locations that will be used in the following steps getting some help from the kids holding strings in place br img alt string positioning src https raw githubusercontent com aaknitt pixel mapper main images stringposition jpg width 40 camera alignment for correct results when translating the three sets of 2d pixel coordinates into one set of 3d coordinates the camera must be aligned to point directly at the center of the equilateral triangle from each of its three capture locations additionally the height of the camera at each location must be the same relative to a fixed reference point not relative to ground unless the ground is perfectly flat using a camera tripod is highly recommended as tripods have adjustments to move the camera in each of the required axis and usually have bubble levels built in to aid in leveling once the camera has been placed directly over its required position on the tripod the first step is to get the camera leveled once it has been leveled the camera align py script can be run to aid in rotational and vertical alignment you ll need to edit the first two lines of code in camera align py to specify the resolution of your camera and its videocapture source address an ip address for a wifi camera or simply an index for a usb camera rotational horizontal alignment first place some easily visible objects 5 gallon bucket for example at the other two camera locations on the equilateral triangle the camera on the tripod needs to be aimed so that its center point is directly between the objects camera align py will overlay two yellow vertical lines on the camera image the location of these lines can be moved in and out by using the right and left arrow keys on the keyboard when properly aligned these yellow lines should pass directly over the two objects buckets or whatever that you ve placed at the two other camera locations note that you ll likely need to re level the camera as you make rotational adjustments it doesn t take much movement to bump things out of level br video showing horizontal alignment process using soccer balls as markers https github com aaknitt pixel mapper blob main images alignmenthorizontal mp4 br img alt horizontal camera alignment src https github com aaknitt pixel mapper blob main images alignhorizontal png width 40 vertical alignment once the camera is properly aligned horizontally the last step is to set its vertical location relative to a fixed object that s visible from all three camera locations in my case i manually turned on a single pixel on the tree that i was mapping using the display testing function in falcon player fpp this pixel was used as my vertical reference point for all three camera locations i then raised or lowered the camera on the tripod using the hand crank extension until the horizontal blue overlay line from camer align py was directly over the reference pixel as before frequently recheck that the camera remains level in all directions while doing the vertical alignment data capture pixel automap py is used to capture the data this script will be run three times once with the camera at each of the three locations identified above depending on your controller configuration you may need to put it into bridge mode to allow it to receive sacn packets from pixel automap py there are a number of configuration elements at the beginning of the script that will need to be modified to fit your setup first the e1 31 sacn universes need to be configured this will be entirely dependent on the number of pixels you re mapping and how you have the universes set up in your pixel controller you ll also need to specify the camera source to use you can also optionally specify an output rgb value to use default is 100 100 100 when the pixels are turned on and the name of the output csv file that is created once pixel automap py is configured and run it will first turn on all of the pixels that have been configured for e1 31 sacn control and show the camera image the user can then click points around the pixels to create a polygon that encompasses all of the pixels this polygon will be used as a mask when detecting individual pixels bright lights outside of this polygon will be ignored during the detection process left click to create new points on the polygon and right click to end the polygon video showing polygon drawing process https github com aaknitt pixel mapper blob main images drawpolygon mp4 br img alt polygon drawing src https github com aaknitt pixel mapper blob main images drawpolygon png width 40 after the polygon has been drawn pixel automap py will turn on each individual pixel one at a time and try to detect its location if the script is able to detect the location it will draw a red circle around the pixel record the coordinates in the output csv file and automatically move to the next pixel if it us unable to detect the location of the pixel it will play a sound on the computer to get your attention at this point you have two choices about how to proceed 1 if you are able to see the location of the pixel you may click on it the coordinates of your click will be used as the pixel location 2 if you are unable to see the location of the pixel it s on the back side of the tree for example simply click anywhere outside the masking polygon the coordinates of the pixel will be stored as 0 0 in the output csv file video showing pixel detection process https github com aaknitt pixel mapper blob main images mapping mp4 br img alt pixel mapping src https github com aaknitt pixel mapper blob main images mapping png width 40 data combining 3d xlights model generation once a csv file has been created for each of the three camera positions those three csv files are used by calc points py to convert the 2d coordinates captured from each location into a single set of 3d coordinates at the top of the script are several parameters that must be edited files a list of the three csv data files containing 2d coordinates to combine fov field of view of the camera degreeshorizontal degreesvertical res resolution in pixels of the camera horizontal vertical xlights output scale factor a scaling factor to reduce the size of the xlights output file to a reasonable size outfilename file name of the final xlights model file strictly speaking only two camera locations are needed to generate 3d coordinates however because many pixels will be blocked from a single camera location the tree is not transparent three positions are used because there are three camera locations there are three different combinations of two camera positions that can each be used to generate a set of 3d coordinates positions 1 2 2 3 3 1 calc points py will calculate a set of 3d coordinates from each 2 camera combination and then average the three sets of 3d coordinates together to generate the final result three sets of 3d coordinates prior to averaging br img alt three 3d data sets src https github com aaknitt pixel mapper blob main images three3d png width 40 br combined averaged coordinate set along with final xlights model br img alt final result src https github com aaknitt pixel mapper blob main images xlightsmodel jpg width 40 once the final set of 3d coordinates has been generated calc points py will then export the final results to an xlights format model file and plot the results for viewing video showing mapped tree with effects https github com aaknitt pixel mapper blob main images finalresult mp4 br img alt final result with effects src https github com aaknitt pixel mapper blob main images finalresult png width 40 | ai |
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sponge | english assets readme cn md p align center img width 500px src https raw githubusercontent com zhufuyi sponge main assets logo png p div align center go report https goreportcard com badge github com zhufuyi sponge https goreportcard com report github com zhufuyi sponge codecov https codecov io gh zhufuyi sponge branch main graph badge svg https codecov io gh zhufuyi sponge go reference https pkg go dev badge github com zhufuyi sponge svg https pkg go dev github com zhufuyi sponge go https github com zhufuyi sponge workflows go badge svg branch main https github com zhufuyi sponge actions license mit https img shields io github license zhufuyi sponge https img shields io github license zhufuyi sponge div sponge https github com zhufuyi sponge is a powerful golang productivity tool that integrates automatic code generation web and microservices frameworks basic development framework sponge has a wealth of generating code commands generating different functional code can be combined into a complete service similar to the way that artificially broken sponge cells can automatically recombine into a new sponge the code is decoupled and modularly designed it is easy to build a complete project from development to deployment so that you develop web or microservices project easily golang can also be low code development br sponge generates the code framework sponge is mainly based on sql and protobuf two ways to generate code each way has to generate code for different functions generate code framework p align center img width 1500px src https raw githubusercontent com zhufuyi sponge main assets sponge framework png p br generate code framework corresponding ui interface p align center img width 1200px src https raw githubusercontent com zhufuyi sponge main assets en sponge ui png p br services framework sponge generated microservice code framework is shown in the figure below which is a typical microservice hierarchical structure with high performance high scalability contains commonly used service governance features you can easily replace or add their own service governance features p align center img width 1000px src https raw githubusercontent com zhufuyi sponge main assets microservices framework png p br egg model for complete service code the sponge separates the two major parts of code during the process of generating web service code it isolates the business logic from the non business logic for example consider the entire web service code as an egg the eggshell represents the web service framework code while both the albumen and yolk represent the business logic code the yolk is the core of the business logic manually written code it includes defining mysql tables defining api interfaces and writing specific logic code on the other hand the albumen acts as a bridge connecting the core business logic code to the web framework code automatically generated no manual writing needed this includes the registration of route code generated from proto files handler method function code parameter validation code error codes swagger documentation and more egg model profiling diagram for web services created based on protobuf p align center img width 1200px src https raw githubusercontent com zhufuyi sponge examples main assets en web http pb anatomy png p this is the egg model for web service code and there are egg models for microservice grpc code and rpc gateway service code described in sponge documentation https go sponge com learn about sponge id f0 9f 8f b7project code egg model br quick start installation sponge sponge can be installed on windows macos and linux environments click here to view installation instructions https github com zhufuyi sponge blob main assets install en md after installing the sponge start the ui service bash sponge run visit http localhost 24631 in your browser generate code by manipulating it on the page br documentation sponge documentation https go sponge com br examples of use simple examples excluding business logic code 1 web gin crud https github com zhufuyi sponge examples tree main 1 web gin crud 2 web gin protobuf https github com zhufuyi sponge examples tree main 2 web gin protobuf 3 micro grpc crud https github com zhufuyi sponge examples tree main 3 micro grpc crud 4 micro grpc protobuf https github com zhufuyi sponge examples tree main 4 micro grpc protobuf 5 micro gin rpc gateway https github com zhufuyi sponge examples tree main 5 micro gin rpc gateway 6 micro cluster https github com zhufuyi sponge examples tree main 6 micro cluster full project examples including business logic code 7 community single https github com zhufuyi sponge examples tree main 7 community single 8 community cluster https github com zhufuyi sponge examples tree main 8 community cluster br if it s help to you give it a star br license see the license license file for licensing information br how to contribute you are more than welcome to join us raise an issue or pull request pull request instructions 1 fork the code 2 create your own branch git checkout b feat xxxx 3 commit your changes git commit am feat add xxxxx 4 push your branch git push origin feat xxxx 5 commit your pull request | gin go grpc microservices architecture golang crud grpc-gateway restful-api rpc sponge web development-framework generate-code microservice-framework | front_end |
dolly | dolly databricks dolly https huggingface co databricks dolly v2 12b is an instruction following large language model trained on the databricks machine learning platform that is licensed for commercial use based on pythia 12b dolly is trained on 15k instruction response fine tuning records databricks dolly 15k https huggingface co datasets databricks databricks dolly 15k generated by databricks employees in capability domains from the instructgpt paper including brainstorming classification closed qa generation information extraction open qa and summarization dolly v2 12b is not a state of the art model but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based databricks is committed to ensuring that every organization and individual benefits from the transformative power of artificial intelligence the dolly model family represents our first steps along this journey and we re excited to share this technology with the world the model is available on hugging face as databricks dolly v2 12b https huggingface co databricks dolly v2 12b model overview dolly v2 12b is a 12 billion parameter causal language model created by databricks https databricks com that is derived from eleutherai s https www eleuther ai pythia 12b https huggingface co eleutherai pythia 12b and fine tuned on a 15k record instruction corpus https github com databrickslabs dolly tree master data generated by databricks employees and released under a permissive license cc by sa known limitations performance limitations dolly v2 12b is not a state of the art generative language model and though quantitative benchmarking is ongoing is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses the dolly model family is under active development and so any list of shortcomings is unlikely to be exhaustive but we include known limitations and misfires here as a means to document and share our preliminary findings with the community in particular dolly v2 12b struggles with syntactically complex prompts programming problems mathematical operations factual errors dates and times open ended question answering hallucination enumerating lists of specific length stylistic mimicry having a sense of humor etc moreover we find that dolly v2 12b does not have some capabilities such as well formatted letter writing present in the original model dataset limitations like all language models dolly v2 12b reflects the content and limitations of its training corpuses the pile gpt j s pre training corpus contains content mostly collected from the public internet and like most web scale datasets it contains content many users would find objectionable as such the model is likely to reflect these shortcomings potentially overtly in the case it is explicitly asked to produce objectionable content and sometimes subtly as in the case of biased or harmful implicit associations databricks dolly 15k the training data on which dolly v2 12b is instruction tuned represents natural language instructions generated by databricks employees during a period spanning march and april 2023 and includes passages from wikipedia as references passages for instruction categories like closed qa and summarization to our knowledge it does not contain obscenity intellectual property or personally identifying information about non public figures but it may contain typos and factual errors the dataset may also reflect biases found in wikipedia finally the dataset likely reflects the interests and semantic choices of databricks employees a demographic which is not representative of the global population at large databricks is committed to ongoing research and development efforts to develop helpful honest and harmless ai technologies that maximize the potential of all individuals and organizations getting started with response generation if you d like to simply test the model without training the model is available on hugging face as databricks dolly v2 12b https huggingface co databricks dolly v2 12b to use the model with the transformers library on a machine with a100 gpus from transformers import pipeline import torch instruct pipeline pipeline model databricks dolly v2 12b torch dtype torch bfloat16 trust remote code true device map auto you can then use the pipeline to answer instructions instruct pipeline explain to me the difference between nuclear fission and fusion generating on other instances a100 instance types are not available in all cloud regions or can be hard to provision inference is possible on other gpu instance types a10 gpus the 6 9b and 2 8b param models should work as is to generate using the 12b param model on a10s ex g5 4xlarge 1 x a10 24gb it s necessary to load and run generating using 8 bit weights which impacts the results slightly also install bitsandbytes add model kwargs load in 8bit true to the pipeline command shown above v100 gpus when using v100s ex p3 2xlarge 1 x v100 16gb nc6s v3 in all cases set torch dtype torch float16 in pipeline instead otherwise follow the steps above the 12b param model may not function well in 8 bit on v100s getting started with training add the dolly repo to databricks under repos click add repo enter https github com databrickslabs dolly git then click create repo start a 13 x ml includes apache spark 3 4 0 gpu scala 2 12 or later single node cluster with node type having 8 a100 gpus e g standard nd96asr v4 or p4d 24xlarge note that these instance types may not be available in all regions or may be difficult to provision in databricks note that you must select the gpu runtime first and unselect use photon for these instance types to appear where supported open the train dolly notebook in the repo which is the train dolly py file in the github dolly repo attach to your gpu cluster and run all cells when training finishes the notebook will save the model under dbfs dolly training training on other instances a100 instance types are not available in all cloud regions or can be hard to provision training is possible on other gpu instance types for smaller dolly model sizes and with small modifications to reduce memory usage these modifications are not optimal but are simple to make select your gpu family type from the gpu family widget enter the number of gpus available in the num gpus widget and then run the rest of the code a number of different options will be set for you to train the model for one of the following gpu types a100 default a10 v100 details of the different configurations are below a100 gpus a100 gpus are preferred for training all model sizes and are the only gpus that can train the 12b param model in a reasonable amount of time as such this is the default configuration as set in the a100 config json deepspeed config file a10 gpus training the 12b param model is not recommended on a10s to train the 6 9b param model on a10 instances ex g5 24xlarge 4 x a10 24gb standard nv72ads a10 v5 2 x a10 simply select a10 from the gpu family widget and enter the number of gpus available in the num gpus widget then run the rest of the code this will use the a10 config json deepspeed config file which makes the following changes per device train batch size and per device eval batch size are set to 3 in the train dolly py invocation of deepspeed within the zero optimization section of the deepspeed config we have added offload optimizer device cpu pin memory true v100 gpus to run on v100 instances with 32gb of gpu memory ex p3dn 24xlarge or standard nd40rs v2 simply select v100 from the gpu family widget and enter the number of gpus available in the num gpus widget and then run the rest of the code this will use the v100 config json deepspeed config file which makes the following changes it makes the changes described above for a10s it enables fp16 floating point format it sets the per device train batch size and per device eval batch size to 3 you may be able to slightly increase the batch size with 32gb instances compared to what works above for 24gb a10s running unit tests locally pyenv local 3 8 13 python m venv venv venv bin activate pip install r requirements dev txt run pytest sh citation online databricksblog2023dollyv2 author mike conover and matt hayes and ankit mathur and jianwei xie and jun wan and sam shah and ali ghodsi and patrick wendell and matei zaharia and reynold xin title free dolly introducing the world s first truly open instruction tuned llm year 2023 url https www databricks com blog 2023 04 12 dolly first open commercially viable instruction tuned llm urldate 2023 06 30 | databricks gpt chatbot dolly | ai |
webdev-js-dom-arrays-forms | web development javascript introduction this is an introductory lab to javascript instructions can be found in the pdf in the docs folder docs js intro arrays forms pdf | front_end |
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pytorch_active_learning | pytorch active learning library for common active learning methods to accompany human in the loop machine learning robert monarch manning publications https www manning com books human in the loop machine learning the code is stand alone and can be used with the book active learning methods in the library the code currently contains methods for least confidence sampling margin of confidence sampling ratio of confidence sampling entropy classification entropy model based outlier sampling cluster based sampling representative sampling adaptive representative sampling active transfer learning for uncertainty sampling active transfer learning for representative sampling active transfer learning for adaptive sampling atlas the book covers how to apply them indepedently in combination and for different use cases in computer vision and natural language processing it also covers strategies for sampling for real world diversity to avoid bias installation if you clone this repo and already have pytorch installed you should be able to get going immediately git clone https github com rmunro pytorch active learning cd pytorch active learning running chapter 2 getting started with human in the loop machine learning python active learning basics py when you run the software you will be prompted to classify news headlines as being disaster related or not the prompt will also tell you give you the option to see a precise definitions for what constitutes disaster related you can also read those definitions in the code in the detailed instructions variable https github com rmunro pytorch active learning blob master active learning basics py after you have classified annotated enough data for evaluation and to begin training you will see that machine learning models now train after each iteration of annotation reporting the accuracy on your held out evaluation data as f scores and auc after the initial iteration of training which will just be on randomly chosen data you will start to see active learning kick in to find unlabeled items that the model is confused about or are outliers with novel features the active learning will be evident in the annotations too as the disaster related headlines will be very rare initially but should become around 40 of the data that you are annotating after a few iterations running chapter 4 diversity sampling python diversity sampling py this builds on the earlier dataset see the chapter for the details of the feature flags that allow you to sample using different types of diversity sampling like model based outliers clustering and representative sampling requirements the code assumes that you are using python3 6 or later if you really need to get this working on python2 please let me know the pytorch and active learning algorithms should all be 2 compliant and it is only python s methods for getting command line inputs that will need to be changed python2 expects integrer inputs only if enough people request it then i ll try to update the code to be compatible for earlier versions of python installing pytorch aws i recommend using the deep learning ami on aws because pytorch is already installed and can be activated with source activate pytorch p36 that should be all you need to run the program immediately for more details on using pytorch on aws see https docs aws amazon com dlami latest devguide tutorial pytorch html google cloud i recommend using a pytorch image for a deep learning virtual machine on google cloud because pytorch is already installed both the cpu and gpu should work pytorch latest cpu pytorch latest gpu for more details on using pytorch on google cloud see https cloud google com deep learning vm docs images microsoft azure i recommend using a data science pre configured virtual machine on microsoft azure https azure microsoft com en us develop pytorch the azure notebook option might also be a good option but i haven t tested it out please let me know if you do linux mac windows if you re installing locally or on a cloud server without pytorch pre installed you can use these options mac conda install pytorch torchvision c pytorch linux windows conda install pytorch torchvision cudatoolkit 9 0 c pytorch these local instructions are current as of june 2019 pytorch are great about maintaining quickstart instructions so i recommend going there if these commands don t work for you for some reason see quick start locally at https pytorch org mac users should also make sure they are using python3 6 or later as mac s still ship with python2 7 by default see above re support for 2 7 if you really require it for pip users it is possible that you can install pytorch with the following commands pip3 install torch or pip3 install torch however this sometimes works and sometimes doesn t depending on the versions of various libraries and your exact operating system that s why conda is recommended over pip on the pytorch website data sources currently the data used is from the million news headlines dataset posted on kaggle https www kaggle com therohk million headlines the data is taken from headlines from australia s abc news organization they are in austalian english which will be closer to uk english than us english but a complete lexical subset of uk us english differing only in that some words in australian english have meanings that do not occur in uk or us english however i intend to replace it soonish the headlines are all lower case and stripped of all characters other than a z and 0 9 no punctuation accented characters etc many of the headlines seem to be truncated for some reason too so i will update it with a dataset that is closer to true headlines this dataset is perfectly fine for everything that you need to learn in this code it is just that the resulting annotations models will be less useful in real world situations | ai |
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LArCV | larcv liquid argon computer vision data format and image processing tools routines and framework for lar tpc derived images developed as bridge between larsoft and deep learning frameworks e g pytorch caffe tensorflow we originally developed for microboone but are now using this library across experiments microboone specific code has been moved into a new repository ublarcvapp https github com larbys ublarcvapp dependent on this library and larlite one recent big change is that the assumed row order is now in postive time order same as larcv2 we however are attempting to maintain the ability to read old tickbackward files created for microboone when reading tickbackward images the data is flipped along the row axis in order to be treated as tickforward data planned features root utilities for quick cheap visualization from command line or python interpretor proper documentation off all classes convenient multi process data loader into dl frameworks still kind of clunky right now examples and unittests image2d bjson image2d support for message passing to gpu server in progress maintain vic s pyqtgraph viewer add 3d and pmt views from pylard done pyutil doesn t need transpose row col means same in image2d as in numpy array recently completed features cmake support for easier exporting of package into other projects done python2 and python3 support row indices are in positive time order same as larcv2 but we provide mechanism to read old larcv1 data then reverse convention this way data is not obsolete but any code that assumes reverse time order is we do this by specifying in iomanager that image2d loaded from branches are reversetimeorder for those image2d s we reshape the array done remove ub stuff from app now in its own repo ublarcvapp https github com larbys ublarcvapp propagate changes of tick forward order to rest of code imagemeta methods done image2d methods pyrgb viewer more installation direct dependencies cmake 3 10 required root 6 required python 2 or 3 optional opencv 4 optional setup 0 dependencies to build with are determined through the presence of environment variables or executables in your path root usually can setup the environment variables we need by runningn the thisroot sh script opencv need the presence of environment variables opencv incdir and opencv libdir that point to the include and library directories respectively 1 clone the repository git clone https github com larbys larcv git 2 go into the larcv directory 3 run the build configuration script source configure sh 4 make a build folder somewhere e g in the same folder as this readme mkdir build 5 go into the folder and run cmake cd build cmake duse python3 on if you require python2 probably deprecated in the future you can use the cmake flag duse python2 on instead 6 then make and install the code make install the output of make install will be libaries and headers in build installed a cmake config file is provided in build installed lib larcv in case you want to incorporate the library into other projects wiki checkout the wiki https github com larbys larcv wiki for notes on using this code | ai |
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YouTube-Video-Review-Analysis-Using-Natural-Language-Processing | youtube video review analysis using natural language processing sentiment analysis of youtube comments using natural language processing the project youtube video review analysis using natural language processing has made use of the following natural language toolkit nltk youtube data api python programming language naive bayes classifier movie reviews corpus for training the classifier polarity data set v 2 0 for testing the classifier and matplotlib for plotting the end results the project peforms analysis on the comments of the top ten video for a given query and produces the positive to negative review ratio for each of the ten videos further explanation regarding the project can be found in the report in the link https drive google com open id 0b3hgz62mbiwoyw5dtmzkrwk0tu0 | natural-language-processing sentiment-analysis natural-language-toolkit naive-bayes-classifier | ai |
tcr-bert | tcr bert tcr bert is a large language model trained on t cell receptor sequences built using a lightly modified bert architecture with tweaked pre training objectives please see our pre print https www biorxiv org content 10 1101 2021 11 18 469186v1 for more information installation to install tcr bert clone the github repository and create its requisite conda environment as follows should take 10 minutes bash conda env create f environment yml afterwards use conda activate tcrbert before running any commands described below model availability tcr bert pretrained tcr bert is available through the huggingface model hub https huggingface co models we make several versions of our model available see manuscript for full details regarding each model s training and usage pre trained on masked amino acid antigen binding classification https huggingface co wukevin tcr bert this is the model to use if you are interested in analzying trb sequences pre trained on masked amino acid https huggingface co wukevin tcr bert mlm only this is the model to use if you are interested in analying both tra and trb sequences these models can be downloaded automatically by using python code like the following python different model classes needed for different models from transformers import bertmodel this model was pretrained on maa and trb classification tcrbert model bertmodel from pretrained wukevin tcr bert this will download the model or use a cached version if previously downloaded and load the pre trained weights as appropriate see the huggingface documentation https huggingface co transformers index html for more details on this api to actually use this model to perform a task like predicting which of 45 antigen peptides a trb sequence might react to you can use the following code snippet python import model utils found under the tcr folder load a textclassificationpipeline using tcr bert tcrbert trb cls model utils load classification pipeline wukevin tcr bert device 0 for the pipeline input amino acids are expected to be spaced df model utils reformat classification pipeline preds tcrbert trb cls c a s s p v t g g i y g y t f binds to nlvpmvatv cmv antigen c a t s g r a g v e q f f binds to gilgfvftl flu antigen return a dataframe where each column is an antigen each row corresponds to an input please see our example jupyter notebooks for more usage examples with this api fine tuned tcr bert we fine tune our tcr bert model to predict lcmv gp33 antigen binding given tra trb amino acid pairs data from daniel et al preprint https www biorxiv org content 10 1101 2021 12 16 472900v1 full this uses an architecture augmented from those natively supported in the transformers library thus this model cannot be loaded using the above python api rather we make the model available for download at the following link model version description link tar gz md5sum 1 0 initial release link https drive google com file d 1vz1qynmeyu7mtddmsh1i00lkiby26qoo view usp sharing e51d8ae58974c2e02d37fd4b51d448ee we also provide a function implemented under model utils py that provides a single line call to download and cache the latest version of the model and load the model python import model utils returns a skorch neuralnet object net model utils load two part bert classifier alternatively this function can be used to load a manually downloaded and unpacked model directory as below net model utils load two part bert classifier model dir you can then make predictions on new examples using the following code snippet python import data loaders as dl import model utils net model utils load two part bert classifier these two lists should zip together to form your tra trb pairs tra sequences caalygnekitf we show only one sequence for simplicity trb sequences cassdaggrntlyf create a dataset wrapping these sequences my dset dl tcrfinetunedataset tra sequences trb sequences skorch mode true preds net predict proba my dset 1 returns a n seq x 2 array of predictions second col is positive print preds 0 26887017 please see our example jupyter notebooks for additional examples using this model usage tcr bert has been tested on a machine running ubuntu 18 04 3 with the provided conda environment this software should be compatible with any recent linux distrubtion also note that while tcr bert does not have any special hardware requirements tcr bert s runtime benefits greatly from having a gpu available particularly for larger inputs using tcr bert to identify antigen specificity groups tcr bert can be used to embed sequences these embeddings can then be used to cluster sequences into shared specificity groups using the leiden clustering algorithm the script bin embed and cluster py performs this with the following positional arguments use h for a complete list of all arguments input file file containing list of sequences this could be formatted as one sequence per line or as a tab delimited file with the sequence as the first column output file file containing on each line a comma separated list of sequences each line corresponds to one group of sequences predicted to share antigen specificity an example of its usage is below this snippet should take under a minute to run assuming the tcr bert model is downloaded already and its expected output is provided at example files glanville np177 training patient clustered csv for reference bash python bin embed and cluster py example files glanville np177 training patient tsv temp csv r 128 g 0 info root read in 1055 unique valid tcrs from example files glanville np177 training patient tsv some weights of the model checkpoint at wukevin tcr bert were not used when initializing bertmodel bert pooler dense weight bert pooler dense bias this is expected if you are initializing bertmodel from the checkpoint of a model trained on another task or with another architecture e g initializing a bertforsequenceclassification model from a bertforpretraining model this is not expected if you are initializing bertmodel from the checkpoint of a model that you expect to be exactly identical initializing a bertforsequenceclassification model from a bertforsequenceclassification model home wukevin miniconda3 envs tcrbert lib python3 9 site packages anndata core anndata py 119 implicitmodificationwarning transforming to str index warnings warn transforming to str index implicitmodificationwarning info root writing 622 tcr clusters to temp csv md5sum temp csv example files glanville np177 training patient clustered csv dd9689e632dae8ee38fc74ae9f154061 temp csv dd9689e632dae8ee38fc74ae9f154061 example files glanville np177 training patient clustered csv in the above we use the r parameter to adjust the resolution of clustering which affects the granularity of the clusters larger values corresponds to more granular clusters whereas smaller values create coarser more inclusive clusters the g parameter is used to specify the index of the gpu to run on this example should finish in less than 5 minutes on a machine with or without gpu availability training a model to predict trb antigen binding tcr bert as a black box embeddings generator the most straightforward data efficient way to use tcr bert to perform trb antigen binding prediction is to use tcr bert to embed the trb sequences apply pca to reduce the dimensionality of the embedding layer and use a svm to perform classification this process is automated in the script under bin embed and train pcasvm py this script takes three positional arguments input file tab delimited file where the first column lists trb sequences and the second column lists corresponding labels other columns are ignored these inputs will be randomly split into training and test splits unless the test argument is also provided output directory folder to write the resulting pca svm model to an example of its usage is below along with expected output bash python bin embed and train classifier py example files glanville np177 training patient tsv temp t example files glanville np177 testing patients tsv c svm g 0 info root git commit cfb7dd4451672683c5248b13cb5d98fe470b9f51 info root reading in test set examples info root training 1055 info root testing 227 some weights of the model checkpoint at wukevin tcr bert were not used when initializing bertmodel bert pooler dense weight bert pooler dense bias this is expected if you are initializing bertmodel from the checkpoint of a model trained on another task or with another architecture e g initializing a bertforsequenceclassification model from a bertforpretraining model this is not expected if you are initializing bertmodel from the checkpoint of a model that you expect to be exactly identical initializing a bertforsequenceclassification model from a bertforsequenceclassification model some weights of the model checkpoint at wukevin tcr bert were not used when initializing bertmodel bert pooler dense weight bert pooler dense bias this is expected if you are initializing bertmodel from the checkpoint of a model trained on another task or with another architecture e g initializing a bertforsequenceclassification model from a bertforpretraining model this is not expected if you are initializing bertmodel from the checkpoint of a model that you expect to be exactly identical initializing a bertforsequenceclassification model from a bertforsequenceclassification model info root classifier svc probability true random state 6489 info root test auroc 0 6240 info root test auprc 0 4047 info root writing svm model to temp svm sklearn these example files contain trb sequences that are known to bind to the np177 influenza a antigen courtesy of a dataset published by glanville et al as well as a set of randomly selected endogenous human trbs sampled from tcrdb as a background negative set since we split this data by patient rather than using default of random splits we explicitly provide a separate set of test examples using the test argument fine tuning tcr bert in addition to training an svm on top of tcr bert s embeddings you can also directly fine tune tcr bert provided you have enough data to do so effectively to do so use the script under bin fintune transformer single py as an input data file provide a tab separated tsv file with a column named trb or tra and a column named label the label column should contain the tcr that the corresponding tcr binds to if a tcr binds to multiple antigens this can be expressed by joining the multiple antigens using a comma doing so will also automatically result in a multi label classification problem instead of the default multi class classification problem an example of the usage is as below bash python bin finetune transformer single py p wukevin tcr bert data data pp65 tsv s trb o finetune pp65 in the above the p option controls the pretrained model being used as a starting point data denotes the data file being used s indicates that finetuning for trbs controls how input files are read and o indicates the output folder for the resulting model and logs the data file indicated above contains a sample input of sequences from the pird and vdjdb databases that bind and do not bind the pp65 antigen note this resulting model and approach isn t used in our manuscript and is simply provided here for completeness example jupyter notebooks we provide several jupyter notebooks that demonstrate more interactive in depth analysis using tcr bert these notebooks also serve to replicate some of the results we present in our manuscript under the jupyter directory please find antigen cv ipynb containing code and results for running antigen cross validation analyses lcmv test set comparison ipynb containing classifier performance on the held out lcmv test set transformers lcmv clustering ipynb containing clustering performance on the held out lcmv test set for tcrdist3 reference see the corresponding notebook under the external eval folder transformers glanville classifier and clustering ipynb containing classification analysis and clustering analysis using the glanville dataset transformers finetuned tcr engineering mlm gen ipynb containing code used for generating analyzing and visualizing novel tcr sequences binding to murine gp33 to run these you must first install jupyter notebook support under the tcrbert conda environment these packages are not included by default to save space you can do this by running conda activate tcrbert conda install c conda forge notebook selected references j glanville et al identifying specificity groups in the t cell receptor repertoire nature 547 7661 94 98 2017 j devlin m w chang k lee k toutanova bert pre training of deep bidirectional transformers for language understanding proceedings of the 2019 conference of the north american chapter of the association for computational linguistics human language technologies volume 1 long and short papers proceedings of the 2019 conference of the north 4171 4186 2019 | bert machine-learning tcr | ai |
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