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tp-itba-mle | tp final machine learning engineering flujo de trabajo de ejemplo https github com luchonaveiro tp itba mle actions workflows deploy s3 yml badge svg the objective of the following project is to build an etl capable of processing the airlines delay and cancellation data from 2009 2018 https www kaggle com yuanyuwendymu airline delay and cancellation data 2009 2018 select 2009 csv to detect the anomalies on the average of the daily departure delay for each airport the etl is developed using apache airflow which extracts the raw data from s3 process it applies an arima model and stores the data on a database created on an rds instance where apache superset is connected to visualize the results a scalable infrastructire is deployed to enable the solution implementation of a managed workflows for apache airflow environment mwaa https aws amazon com managed workflows for apache airflow where the model will run to detect the airport s departure delya anomalies to detect an anomaly an arima https www statsmodels org devel generated statsmodels tsa arima model arima html model is used and the threshold to split a normal observation of an anomaly is a 80 confidence interval calculated by the same arima model deployment of a postgresql rds instance https aws amazon com rds where the daily summarized values are going to be stored deployment of apache superset https superset apache org which we will connect to the database created on the rds instance and generates a dahsboard to visualize the number of daily flights of each airport indicating the days considered as an anomaly regarding the departure delay the average departure delay of each airport together with the expected value and the 80 ci returned by the arima model airports data as i comented previously the data refers to the airlines delay and cancellation flights from 2009 2018 and once downloaded i upload them on com lucianonaveiro itba tp airport data s3 bucket after uploading them i deploy all the infrastructure aws infrastructure first i create some s3 buckets and copy my local files to aws airflow itba tp lucianonaveiro where the dags and requirements txt file are stored com lucianonaveiro itba tp cloudformation where i store some large cloudformation templates com lucianonaveiro itba tp airport plots where i store the ouptut files from the etl process executed by apache airflow to do that i execute the following cloudformation template aws cloudformation deploy stack name tp itba s3 template file cloudformation 01 s3 yaml once created i copy my local files to s3 aws s3 cp recursive airflow dags s3 airflow itba tp lucianonaveiro dags aws s3 cp airflow requirements requirements txt s3 airflow itba tp lucianonaveiro requirements txt afer this i execute the template that builds the vpc where all the services and resources created for this project are going to run aws cloudformation deploy stack name tp itba vpc template file cloudformation 02 vpc yaml capabilities capability iam once the vpc is created i deploy both the mwaa and apache superset for the latter one i base on the following quick start reference deployment https aws quickstart github io quickstart apache superset aws cloudformation deploy stack name tp itba airflow template file cloudformation 03 managed airflow yaml capabilities capability iam aws cloudformation deploy stack name tp itba superset template file cloudformation 04 superset yaml s3 bucket com lucianonaveiro itba tp cloudformation s3 prefix superset capabilities capability iam once all tthe stacks finish creating i deploy the rds instance this instance is not available from the public internet only mwaa apache superset and an ec2 instance i deploy to run the db init sql script by using the userdata on the cloudformation template to create the airport daily summary table inside the rds instance have access to it aws cloudformation deploy stack name tp itba rds template file cloudformation 05 rds yaml capabilities capability iam when all the stacks are created i look on cloudformation aws console for the apache airflow url the apache superset url and the rds endpoint on the output of each stack image assets aws cloudformation png just to visualize it this is the whole infrastruture created by the cloudformation stacks to develop the etl image assets aws infra png apache airflow the first thing to do when mwaa is running is to create the connection to the rds instance under the name of airports db the host value we get it from cloudformation outputs from tp itba rds stack user and password are both postgres port is 5432 image assets airflow db connection png once created the connection i can turn on the airports daily summary dag which has an annual schedule with a backfill from 2009 to execute the etl for all the necessary years image assets airflow running png the dag is in charge of downloading the raw data from com lucianonaveiro itba tp airport data s3 bucket process the data generating a daily timeseries for each airport run the arima model to each timeseries finding the days where the departure delay is quite different as normal store the daily summarized table on the table created on the rds instance generate 2 plots for each airport and store them on com lucianonaveiro itba tp airport plots s3 bucket the plots explain the following number of dialy flights for each airport indicating the days with anomalies expected values and 80 ci vs the observed average departure delay to understand the decision of the model when splitting between normal and days with anomalies the dag executes all these steps for each year from 2009 to 2018 on the following plots we can see the data from the anc airport from 2013 where we can see clearly the days with strange average departure delay are quite off the 80 ci from the arima model image assets airflow anc daily average delay departure png image assets airflow anc daily flights png in case of executing twice the same year from apache airflow the data does not duplicate on the table inside the rds instance since the combination of date and airport is defined as primary key and the integrityerror exception is managed so neither the data is duplicated nor the code is going to break arima model as i explained previously to split between normal and days with anomalies i fit an arima model to the daily average departure delay for each airport for each year and compare the obersved average with the 80 ci returned by the arima model arima short for auto regressive integrated moving average is actually a class of models that explains a given time series based on its own past values that is its own lags and the lagged forecast errors so that equation can be used to forecast future values an arima model is characterized by three terms p is the order of the auto regressive ar term it refers to the number of lags of y to be used as predictors q is the order of the moving average ma term it refers to the number of lagged forecast errors that should go into the arima model d is the number of differencing required to make the time series stationary an arima model is one where the time series was differenced at least once to make it stationary and you combine the ar and the ma terms so the equation becomes image assets arima formula png on this project to select the optimum values of p q and d for each time series a grid search approach was selected using 66 of the data as training an the rest as test where the selected values are the ones that minimizes the error as the problem is one of a time series the order of tha data points matter so the split between training and test set is ont random instead we do a nested approach where i iterate the 33 test data predicting only one day at a time so at the end of the process i compare all the single pedictions with the test array to get the final error image assets arima nested cross validation png apache superset once that the dag ran for all the airports for all the years storing the summarized data on the postrgesql rds table i can start using apache superset and connect it to the db image assets superset db connection png when the connection is created i can define a dataset pointing to the airport daily summary table created on the rds instance while creating the dataset i am going to define 2 new dimensions date is the same date as the db but in timestamp and dep delay anomaly label is just a map from dep delay anomaly to visualize it clearly on the dashabord image assets superset dataset creation png image assets superset dataset creation 2 png image assets superset dataset creation 3 png having apache superset connected to the postgresql db i can create the dashboards with the same plots as the dag stored on com lucianonaveiro itba tp airport plots s3 bucket along with 2 filters one for date and the other for the airport to accomplish these we have to build 3 charts charts chart button first we will build the amount of flights plot where we select the dataset we want to use public airport daily summary and the visualization type time series chart image assets superset explanation 1 png now on the data section we have just to select the date column the metric we want to plot and how we are going to break it down here i chose to break down the amout of daily flights by the flag dep delay anomaly which is the flag created with arima model to detect the days with anomalies image assets superset explanation 2 png on the customize section i just enable the legend to be displayed and stacked the daily values image assets superset explanation 3 png for the second plot the one displaying the predicted average departure delay vs the observed value together with the ci we choose the same dataset and visualization type as the previuos one but we add more metrics on per each series we want to plot image assets superset explanation 4 png last for the filter we also create a chart with the same dataset as teh previous but with a filter box as the visualization type here we choose the date and origin airport columns to be filtered when the dashboard is set up i can filter the year 2013 and the anc airport and find the same insights as on the plots stored on s3 image assets superset results 1 png image assets superset results 2 png ci cd i also implemented a ci cd pipeline using github actions the workflow named deploy s3 yml which on each push and merge to the main branch it uploads the airflow dags directory and the airflow requirements txt file to the airflow itba tp lucianonaveiro s3 bucket to correctly implement this i create an iam user with an attached policy to be able to upload and delete files to the airflow itba tp lucianonaveiro s3 bucket aws cloudformation deploy stack name tp itba gha iam template file cloudformation 06 gha iam yaml capabilities capability named iam on the outputs pane of this stack both the access key id and the secret access key can be found these two values are necessary to create the secrets on github actions settings image assets github actions png so once the github action is triggered we can see clearly how th dags and requirements txt files are uploaded to s3 image assets github actions cicd png the dags deployment has a special treatment in which i first delete all the files on the bucket and then upload the files | airflow aws cloudformation | cloud |
cs193p_2017_Stanford_Swift_iOS10 | stanford engineering cs193p developing ios 10 apps with swift art itunesu png raw true this repo contains my lecture notes and projects from paul hegarty s classic cs 193p iphone application development course http web stanford edu class cs193p cgi bin drupal offered by the school of engineering at stanford this course is described as being updated for ios 10 and swift 3 tools and apis required to build applications for the iphone and ipad platforms using the ios sdk the course covers user interface design for mobile devices and unique user interactions using multi touch technologies object oriented design using model view controller paradigm memory management the swift programming language other topics include animation mobile device power management multi threading networking and performance considerations lectures lecture slides source video date published my notes 1 course overview intro to ios xcode and swift slides lecture 201 20slides pdf art play png raw true https youtu be ilq tq772vi list plpa aybrweuz32nsgnzdl0 qisw f12ai january 9 2017 2 applying mvc calculator demo continued slides lecture 202 20slides pdf art play png raw true https youtu be aug myu02q list plpa aybrweuz32nsgnzdl0 qisw f12ai january 11 2017 3 more swift foundation framework slides lecture 203 20slides pdf art play png raw true https youtu be 4voseyy6krc list plpa aybrweuz32nsgnzdl0 qisw f12ai january 18 2017 4 custom views slides lecture 204 20slides pdf art play png raw true https youtu be lx4ohhsc3ho list plpa aybrweuz32nsgnzdl0 qisw f12ai january 23 2017 5 gestures and multiple mvcs slides lecture 205 20slides pdf art xcode png raw true democode lecture 5 demo code faceit pdf art play png raw true https youtu be fxinju nkwu list plpa aybrweuz32nsgnzdl0 qisw f12ai january 25 2017 6 multiple mvcs view controller lifecycle and memory management slides lecture 206 20slides pdf art xcode png raw true democode lecture 6 demo code faceit pdf art play png raw true https youtu be hqrxm2zupvy list plpa aybrweuz32nsgnzdl0 qisw f12ai january 30 2017 7 error handling in swift extensions protocols delegation and scroll views slides lecture 207 20slides pdf art xcode png raw true democode lecture 7 demo code cassini pdf art play png raw true https youtu be gilsl 6tqmm list plpa aybrweuz32nsgnzdl0 qisw f12ai february 1 2017 8 multithreading text field table view intro slides lecture 208 20slides pdf art xcode png raw true democode lecture 8 demo code cassini pdf art play png raw true https youtu be h9kbzg3rk8 list plpa aybrweuz32nsgnzdl0 qisw f12ai february 6 2017 9 table view slides lecture 209 20slides pdf art xcode png raw true democode lecture 9 demo code smashtag pdf art play png raw true https youtu be 78lwmmdxr4k list plpa aybrweuz32nsgnzdl0 qisw f12ai february 8 2017 10 core data slides lecture 2010 20slides pdf art play png raw true https youtu be ssipdu73p7a list plpa aybrweuz32nsgnzdl0 qisw f12ai february 13 2017 11 core data demo art xcode png raw true democode lecture 11 demo code smashtag pdf art play png raw true https youtu be whf63gtaw1w list plpa aybrweuz32nsgnzdl0 qisw f12ai february 15 2017 12 autolayout slides lecture 2012 20slides pdf art play png raw true https youtu be uppl3lv5l8w list plpa aybrweuz32nsgnzdl0 qisw f12ai february 22 2017 13 timer animation slides lecture 2013 20slides pdf art xcode png raw true democode lecture 13 demo code faceit pdf art play png raw true https youtu be 6tdnjwdwfys list plpa aybrweuz32nsgnzdl0 qisw f12ai february 27 2017 14 dynamic animation demos art xcode png raw true democode lecture 14 demo code asteroids pdf art play png raw true https youtu be 8ryq1a zdmw list plpa aybrweuz32nsgnzdl0 qisw f12ai march 1 2017 15 segues slides lecture 2015 20slides pdf art xcode png raw true democode lecture 15 demo code faceit segues pdf art play png raw true https youtu be mjklubbkggc list plpa aybrweuz32nsgnzdl0 qisw f12ai march 6 2017 16 alerts notifications lifecycles persistence slides lecture 2016 20slides pdf art play png raw true https youtu be hkuedmw7qx0 list plpa aybrweuz32nsgnzdl0 qisw f12ai march 8 2017 17 accessibility art play png raw true https youtu be nozxrbom7bw list plpa aybrweuz32nsgnzdl0 qisw f12ai march 13 2017 reading assignments reading name 1 reading 1 intro to swift reading reading assignment 1 intro to swift pdf 2 reading 2 more swift reading reading assignment 2 more swift pdf 3 reading 3 the rest of swift reading reading assignment 3 the rest of swift pdf problem sets ps name 1 assignment 1 calculator problemsets programming project 1 calculator pdf 2 assignment 2 calculator brain problemsets programming project 2 calculator brain pdf 3 assignment 3 graphing calculator problemsets programming project 3 graphing calc pdf 4 assignment 4 smashtag mentions problemsets programming project 4 smashtag mentions pdf 5 assignment 5 smashtag mention popularity problemsets programming project 5 smashtag mention popularity pdf my assignment solutions as name 1 assignment 1 calculator 2 assignment 2 calculator brain 3 assignment 3 graphing calculator 4 assignment 4 smashtag mentions 5 assignment 5 smashtag mention popularity licensing my cs193p projects are licensed under the mit license license support or contact | os |
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PlatformIO-FreeRTOS | platformio freertos platformio wrapper for freertos supported frameworks libopencm3 arduino cmsis stm32cube spl supported platforms st stm32 gigadevice gd32v silicon labs efm32 nxp lpc atmel sam usage under your global environment add the library to lib deps using either the string platformio freertos or by using the github url if using the url make sure that submodules are cloned recursively alternatively simply add the library to the libs folder in your platformio project the library handles configuring freertos for your microcontroller s architecture by default this library wrapper defaults to using heap 4 c as the freertos dynamic memory allocation scheme note that the library expects there to be a freertosconfig h file located somewhere in your includepath if the config file is not present compilation will fail notes this library has been tested for the following architectures stm32f1 stm32f4 stm32f7 the rest of the architectures supported are added based on the type of arm core specified in the datasheet note that for some devices this isn t always a direct match for example certain cortex m7 devices work better with the arm cm4f port of freertos the stm32h7 line sometimes has both a cortex m7 and cortex m4 onboard but the library uses arm cm4f by default feel free to tweak submit a pull request if an architecture needs building differently and confirm if architectures not listed here compile and function correctly currently the freertos ports that utilise the mpu are not utilised custom library overrides macros freertos tag used to override the version of the freertos kernel that is used by default the latest tagged commit is used platform this library selects the correct freertos port by looking at global compiler macros these are usually set correctly by the framework but if compilation fails these can be added as a build flag | os |
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ciml | a course in machine learning this repository contains the source code for the ciml book see http ciml info as well as any course materials that seem useful slides documents labs etc | ai |
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xai | github https img shields io badge release alpha yellow svg github https img shields io badge version 0 0 5 alpha lightgrey svg github https img shields io badge python 3 5 3 6 3 7 blue svg github https img shields io badge license mit lightgrey svg xai an explainability toolbox for machine learning xai is a machine learning library that is designed with ai explainability in its core xai contains various tools that enable for analysis and evaluation of data and models the xai library is maintained by the institute for ethical ai ml http ethical institute and it was developed based on the 8 principles for responsible machine learning http ethical institute principles html you can find the documentation at https ethicalml github io xai index html https ethicalml github io xai index html you can also check out our talk at tensorflow london https www youtube com watch v gzpfbhqj0h4 where the idea was first conceived the talk also contains an insight on the definitions and principles in this library youtube video showing how to use xai to mitigate undesired biases table tr td width 30 this a href https www youtube com watch v vq8mdidodhc video of the talk presented at the pydata london 2019 conference a which provides an overview on the motivations for machine learning explainability as well as techniques to introduce explainability and mitigate undesired biases using the xai library td td width 70 a href https www youtube com watch v vq8mdidodhc img src images video jpg a td tr tr td width 30 do you want to learn about more awesome machine learning explainability tools check out our community built a href https github com ethicalml awesome machine learning operations awesome machine learning production operations a list which contains an extensive list of tools for explainability privacy orchestration and beyond td td width 70 a href https github com ethicalml awesome machine learning operations img src images mlops link png a td tr table 0 1 0 if you want to see a fully functional demo in action clone this repo and run the a href https github com ethicalml xai blob master examples xai 20example 20usage ipynb example jupyter notebook in the examples folder a what do we mean by explainable ai we see the challenge of explainability as more than just an algorithmic challenge which requires a combination of data science best practices with domain specific knowledge the xai library is designed to empower machine learning engineers and relevant domain experts to analyse the end to end solution and identify discrepancies that may result in sub optimal performance relative to the objectives required more broadly the xai library is designed using the 3 steps of explainable machine learning which involve 1 data analysis 2 model evaluation and 3 production monitoring we provide a visual overview of these three steps mentioned above in this diagram img width 100 src images bias png xai quickstart installation the xai package is on pypi to install you can run pip install xai alternatively you can install from source by cloning the repo and running python setup py install usage you can find example usage in the examples folder 1 data analysis with xai you can identify imbalances in the data for this we will load the census dataset from the xai library python import xai data df xai data load census df head img width 100 src images readme csv head jpg view class imbalances for all categories of one column python ims xai imbalance plot df gender img width 100 src images readme imbalance gender jpg view imbalances for all categories across multiple columns python im xai imbalance plot df gender loan img width 100 src images readme imbalance multiple jpg balance classes using upsampling and or downsampling python bal df xai balance df gender loan upsample 0 8 img width 100 src images readme balance upsample jpg perform custom operations on groups python groups xai group by columns df gender loan for group group df in groups print group print group df loan head n img width 100 src images readme groups jpg visualise correlations as a matrix python xai correlations df include categorical true plot type matrix img width 100 src images readme correlation matrix jpg visualise correlations as a hierarchical dendogram python xai correlations df include categorical true img width 100 src images readme correlation dendogram jpg create a balanced validation and training split dataset python balanced train test split with minimum 300 examples of the cross of the target y and the column gender x train y train x test y test train idx test idx xai balanced train test split x y gender min per group 300 max per group 300 categorical cols categorical cols x train display bal df train idx x test display bal df test idx print total number of examples x test shape 0 df test x test display copy df test loan y test xai imbalance plot df test gender loan categorical cols categorical cols img width 100 src images readme balance split jpg 2 model evaluation we are able to also analyse the interaction between inference results and input features for this we will train a single layer deep learning model model build model proc df drop loan axis 1 model fit f in x train y train epochs 50 batch size 512 probabilities model predict f in x test predictions list probabilities 0 5 astype int t 0 img width 100 src images readme 15 png visualise permutation feature importance python def get avg x y return model evaluate f in x y verbose 0 1 imp xai feature importance x test y test get avg imp head img width 100 src images readme 6 png identify metric imbalances against all test data python xai metrics plot y test probabilities img width 100 src images readme metrics plot jpg identify metric imbalances across a specific column python xai metrics plot y test probabilities df x test display cross cols gender categorical cols categorical cols img width 100 src images readme metrics column jpg identify metric imbalances across multiple columns python xai metrics plot y test probabilities df x test display cross cols gender ethnicity categorical cols categorical cols img width 100 src images readme metrics multiple jpg draw confusion matrix python xai confusion matrix plot y test pred img width 100 src images readme confusion matrix jpg visualise the roc curve against all test data python xai roc plot y test probabilities img width 100 src images readme 9 png visualise the roc curves grouped by a protected column python protected gender ethnicity age xai roc plot y test probabilities df x test display cross cols p categorical cols categorical cols for p in protected img width 100 src images readme 10 png visualise accuracy grouped by probability buckets python d xai smile imbalance y test probabilities img width 100 src images readme 12 png visualise statistical metrics grouped by probability buckets python d xai smile imbalance y test probabilities display breakdown true img width 100 src images readme 13 png visualise benefits of adding manual review on probability thresholds python d xai smile imbalance y test probabilities bins 9 threshold 0 75 manual review 0 375 display breakdown false img width 100 src images readme 14 png | explainability xai ml ai bias artificial-intelligence bias-evaluation explainable-ai explainable-ml machine-learning machine-learning-explainability interpretability xai-library evaluation imbalance upsampling downsampling feature-importance | ai |
Asclepius | asclepius publicly shareable clinical large language model built on synthetic clinical notes license mit https img shields io badge license mit yellow svg https opensource org licenses mit python 3 9 https img shields io badge python 3 9 blue svg https www python org downloads release python 390 code style black https img shields io badge code 20style black 000000 svg https github com psf black p align center img src resources task jpg center align true width 100 p br update we are excited to introduce the new asclepius based on llama2 you can find these models at the links below asclepius llama2 7b https huggingface co starmpcc asclepius llama2 7b asclepius llama2 13b https huggingface co starmpcc asclepius llama2 13b we introduce the first shareable clinical large language model asclepius the development of large language models tailored for handling patients clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations to address these challenges we trained our model asclepius on synthetic clinical notes generated from publicly available case reports extracted from biomedical literature on gpt 4 and expert evaluation our model shows comparable performance to the model trained on real clinical notes the model checkpoints and data are publicly available via huggingface paper https arxiv org abs 2309 00237 asclepius 7b https huggingface co starmpcc asclepius 7b asclepius 13b https huggingface co starmpcc asclepius 13b synthetic clinical notes instruction dataset https huggingface co datasets starmpcc asclepius synthetic clinical notes p align center img src resources pipeline jpg center align true div align center data synthesis pipeline div p how to use python prompt you are an intelligent clinical languge model below is a snippet of patient s discharge summary and a following instruction from healthcare professional write a response that appropriately completes the instruction the response should provide the accurate answer to the instruction while being concise discharge summary begin note discharge summary end instruction begin question instruction end from transformers import autotokenizer automodel tokenizer autotokenizer from pretrained starmpcc asclepius 7b use fast false model automodel from pretrained starmpcc asclepius 7b note this is a sample note question what is the diagnosis model input prompt format note note question question input ids tokenizer model input return tensors pt input ids output model generate input ids print tokenizer decode output 0 data our synthetic discharge summaries and corresponding instruction answer pairs are available on huggingface https huggingface co datasets starmpcc asclepius synthetic clinical notes due to the license issue of mimic iii https mimic mit edu dataset instruction answer pairs used for asclepius r will be available via physionet reproducing guide our models asclepius 7b and asclepius 13b is available on huggingface 7b https huggingface co starmpcc asclepius 7b 13b https huggingface co starmpcc asclepius 13b asclepius r will be available via physionet details summary environment setup summary conda create n asclepius python 3 9 y conda activate asclepius conda install pytorch torchvision torchaudio pytorch cuda 11 8 c pytorch c nvidia y pip install pandarallel pandas jupyter numpy datasets sentencepiece openai wandb accelerate tiktoken transformers 4 32 0 details details summary note synthesis summary convert pmc patients to gpt input python preprocessing pmc preprocessing py input path pmc patients csv path save path a run openai api python preprocessing openai parallel request py input path a save path b request url openai api endpoint api key openai api key postprocess openai api output python preprocessing pmc postprocessing py input path b save path c summary instruction generation summary to use mimic iii instead of synthetic notes python preprocessing mimiciii preproc py input path noteevents csv path save path c make instructions python preprocessing bootstrapping py input path c save path d request url openai api endpoint api key openai api key postprocess instructions python preprocesisng postproc questions py input path d save path e make answers python preprocessing openai parallel request py input path e save path f request url openai api endpoint api key openai api key postprocess answers python preprocessing make instruction dataset py input path f save path g summary pretraining summary note preprocessing we concatenate all notes with eos tokens python src tokenize data py input path c save path h run pretriaining for 8x a6000 gpus adjust nproc per node gradient accumulate step per device train batch size to fit to your hardware global batch size 128 torchrun nproc per node 8 master port your port src train py model name or path decapoda research llama 7b hf data path h bf16 true output dir i num train epochs 1 per device train batch size 2 per device eval batch size 2 gradient accumulation steps 8 evaluation strategy no save strategy steps save steps 1000 learning rate 2e 5 weight decay 0 warmup ratio 0 03 lr scheduler type cosine logging steps 1 fsdp full shard auto wrap fsdp transformer layer cls to wrap llamadecoderlayer tf32 true model max length 2048 gradient checkpointing true details details summary instruction finetuning summary torchrun nproc per node 8 master port your port src instruction ft py model name or path i data path g bf16 true output dir checkpoints num train epochs 3 per device train batch size 2 per device eval batch size 2 gradient accumulation steps 8 evaluation strategy no save strategy epoch learning rate 2e 5 weight decay 0 warmup ratio 0 03 lr scheduler type cosine logging steps 1 fsdp full shard auto wrap fsdp transformer layer cls to wrap llamadecoderlayer tf32 true model max length 2048 gradient checkpointing true ddp timeout 18000 details details summary evaluation summary evaluate models python src evaluate py model name i input path j save path k run gpt 4 for evaluation python eval gpt4 evaluate py input path k save path l details citation misc kweon2023publicly title publicly shareable clinical large language model built on synthetic clinical notes author sunjun kweon and junu kim and jiyoun kim and sujeong im and eunbyeol cho and seongsu bae and jungwoo oh and gyubok lee and jong hak moon and seng chan you and seungjin baek and chang hoon han and yoon bin jung and yohan jo and edward choi year 2023 eprint 2309 00237 archiveprefix arxiv primaryclass cs cl code references stanford alpaca https github com tatsu lab stanford alpaca minimal llama https github com zphang minimal llama vicuna https github com lm sys fastchat clinical t5 https www physionet org content clinical t5 1 0 0 | ai |
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desafio-iac-aws | desafio iac terraform exerc cio da disciplina iac terraform do curso devops engineering and cloud solutions da mackenzie voc pode subir tudo em uma nica estrutura v rios tf voc pode utilizar m dulos para organizar a estrutura voc pode utilizar tudo hardcoded ou com vari veis documenta o terraform providers e modules https registry terraform io desafios img desafios pdf desafio 1 detalhes do desafio 1 desafio 01 1 vpc 1 internet gateway 1 subnet p blica 1 routetable p blica apontando para o internet gateway 1 inst ncia ec2 na subnet p blica desafio 2 detalhes do desafio 2 desafio 02 1 vpc 1 internet gateway 2 subnet p blica 1 routetable p blica apontando para o internet gateway 2 inst ncias ec2 uma em cada subnet desafio 3 detalhes do desafio 3 desafio 03 1 vpc 1 internet gateway 2 natgateway 4 subnets 2 p blicas e 2 privadas 3 routetable 1 routetable p blica apontando para o internetgateway 2 routetable privadas cada uma apontando para um natgateway 2 inst ncias ec2 nas subnets privadas security group com porta 80 liberada elastic load balancer ou application load balancer nas subnets p blicas security group com porta 80 liberada desafio 4 detalhes do desafio 4 desafio 04 1 vpc 1 internet gateway 2 natgateway 4 subnets 2 p blicas e 2 privadas 3 routetable 1 routetable p blica apontando para o internetgateway 2 routetable privadas cada uma apontando para um natgateway 1 autoscaling nas subnets privadas 2 inst ncias ec2 nas subnets privadas security group com porta 80 liberada elastic load balancer ou application load balancer nas subnets p blicas security group com porta 80 liberada desafio 5 detalhes do desafio 5 desafio 05 1 vpc 1 internet gateway 2 natgateway 4 subnets 2 p blicas e 2 privadas 3 routetable 1 routetable p blica apontando para o internetgateway 2 routetable privadas cada uma apontando para um natgateway 1 autoscaling nas subnets privadas 2 inst ncias ec2 nas subnets privadas security group com porta 80 liberada 1 rds mysql subnet privada security group com porta 3306 liberada elastic load balancer ou application load balancer nas subnets p blicas security group com porta 80 liberada made with by pedro santos wave get in touch https www linkedin com in santospedroh | terraform aws cloud iaac iac-module hands-on snake-game flappy-bird site | cloud |
awesome-blockchain | awesome blockchain blockchain and etc contributing thank to group blockchain ha noi and group vietnam blockchain developer help me collecting resources below cryptocurrencies ever wonder how bitcoin and other cryptocurrencies actually work https youtu be bbc nxj3ng4 how to cryptocurencies work blockchain tutorials python how to build your own blockchain part 1 https bigishdata com 2017 10 17 write your own blockchain part 1 creating storing syncing displaying mining and proving work creating storing syncing displaying mining and proving work part 2 https bigishdata com 2017 10 27 build your own blockchain part 2 syncing chains from different nodes syncing chains from different nodes part 3 https bigishdata com 2017 11 02 build your own blockchain part 3 writing nodes that mine writing nodes that mine part 4 1 https bigishdata com 2017 11 13 how to build a blockchain part 4 1 bitcoin proof of work difficulty explained bitcoin proof of work difficulty explained part 4 2 https bigishdata com 2017 11 21 how to build your own blockchain part 4 2 ethereum proof of work difficulty explained ethereum proof of work difficulty explained nodejs how to build pow blockchain with 1500 lines of code https hackernoon com a cryptocurrency implementation in less than 1500 lines of code d3812bedb25c nodejs how to blockchain in 200 lines of code without consensus https medium com lhartikk a blockchain in 200 lines of code 963cc1cc0e54 blockchain courses free blockchain for business https courses edx org courses course v1 linuxfoundationx lfs171x 3t2017 course an introduction to hyperledger technologies free ibm blockchain foundation for developers https www coursera org learn ibm blockchain essentials for developers ico development smart contract https erc20token sonnguyen ws en latest steps to develope a ico smart contract ebooks pdf building blockchain projects with ethereum https github com packtpublishing building blockchain projects pdf mastering bitcoin 1st edition unlocking digital cryptocurrencies https drive google com open id 1gd0psfjune8t5z5bdhcruzljyyfk3a0p pdf mastering bitcoin 2nd edition programming the open blockchain https github com bitcoinbook bitcoinbook pdf bitcoin for dummies https drive google com file d 1vqum7vun3ewlrksh0he7h79mbsiunpqw view usp sharing pdf bitcoin primer https drive google com file d 1r2mwkogstpfxswywaadna02cmzcr81 6 view usp sharing pdf blocks and chains https drive google com open id 1dvyw54apnfwrvktgwwfi5 mt9ugz ay introdution to bitcoin cryptocurrencies and their consensus mechanisms pdf the atomy of money like infomational commodity https drive google com open id 175an2omutridoh8tzjum2olbyhp2ojm a study of bitcoin pdf the bitcoin big bang https drive google com open id 1x2m lrys6fn3vn2s6hyezxdphqoz9016 how alternative currencies are about to change the world pdf bitcoin for the befuddled https drive google com open id 1dwryqufrmxqnolwvx3qpohik 3ldcwst pdf blockchain https drive google com open id 1onfc2kywhxlcj8yir4iik4h87btt2dxe blueprint for a new economy pdf understanding bitcoin https drive google com open id 12i spxw4u c9ioyk9z4abswbrrhjggq6 cryptography engineering and economic pdf mastering blockchain https drive google com open id 1myw5lm7kbs59tghruccmva4jc0yammpm distributed ledgers decentralization and smart contracts explained epub bitcoin beginner https drive google com open id 1dqqcx7yanilgemvukqwq5oo4bkpwoihh a step by step guide to buying selling and investing bitcoin epub bitcoin internal https drive google com open id 16ectydw pljapuwbkhvizzgl3e5uf0og a thorough explanation of bitcoin and how it works from a technical perspective epub a book of satoshi https drive google com file d 1gfgsodiagj5xzhzhdaa8gzqxtub gzjm the collected writings of bitcoin creator epub digital gold https drive google com open id 1hqfedahwheuj jufbpb bld2pjj20ebg bitcoin and the inside stoty of the misfits and milionaires trying to reinvent money epub blockchain basics https drive google com open id 1kmlpje dmyxk4o48u6lsjfugtekq5aig a non technical introdution in 25 steps epub bitcoin exposed https drive google com open id 1xknrwiljfimg5twqltcn 3bxgutfjsuo today s complete guide to tomorrow s currency epub decoding bitcoin https drive google com open id 1ngge9tioy3rf8l m 4jajezapzktae1t all you need to know all about the new world currency epub the age of cryptocurrency https drive google com open id 1kp3myt62jyirqb72zuxdoi6jpwqaptyn how bitcoin and digital money are challenging the global economic order epub bitcoin and the future of money https drive google com open id 1trhzervtbv7gg mb1jprsoq4v6ftyesa how does it work how do you invest how risk is it websites learn blockchains by building one https hackernoon com learn blockchains by building one 117428612f46 the fastest way to learn how blockchains work is to build one truffer boxes http truffleframework com boxes solidity contracts libraries front end views and more all the way up to complete example dapps 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natds-ios | natura design system for ios build status https app bitrise io app 38848108b04aa71a status svg token kep02isbtn73sde7kezo4w branch master https app bitrise io app 38848108b04aa71a cocoapods compatible https img shields io cocoapods v natds svg https img shields io cocoapods v natds svg what for library with ios components defined by natura group design system team https natds natura design docs tech stack swift 5 supports ios 10 to 14 cocoapods swift package manager fastlane bitrise swiftlint jazzy note for versions 6 2 0 and previous git lfs is also a dependency how to install cocoapods cocoapods http cocoapods org is a dependency manager for swift and objective c cocoa projects you can install it with the following command bash gem install cocoapods you can also check cocoapods installation guide https guides cocoapods org using getting started html installation for other options to integrate natds into your xcode project using cocoapods specify it in your podfile ruby target your target name do pod natds end then run the following command bash pod install swift package manager to use natds in your project with swift package manager you need to add it from the xcode menu follow the path file swift packages add package dependency and then provide it the git url for this repository https github com natura cosmeticos natds ios git xcode will manage the imports how to configure the library provide the components according to the design system brand themes which must be chosen before using the components usually at your app s launch all themes available are options in the availabletheme enum to choose a theme configure the library with the following code swift designsystem configure with availabletheme reminder this step is mandatory if the design system is not configured with a brand theme a fatalerror will be raised how to add icons this library does not have the design system icons since version 3 0 0 they re stored in the icon library natdsicons https cocoapods org pods natdsicons which can also be used with cocoapods to use the icons specify their pod in your podfile ruby target your target name do pod natdsicons end if you re using a natds version older than 3 0 0 there are available icons in the library nonetheless some newer icons won t be available natdsicons versions to check all available versions for natds icons you can check the changelog https github com natura cosmeticos natds commons blob master packages natds icons changelog md or run a pod command from your terminal bash pod search natdsicons simple check the documentation on how to use icons in your code docs how 20to 20use 20icons md sample app the sample app has visual and code implementation examples for the design system s components and tokens to run it download this repository install its dependencies following the project setup then build and run check how to use the sample app docs setup md themes components and tokens check how to use the library docs getting 20started md how to contribute check the contribution guidelines docs how 20to 20contribute md | os |
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Uber-Database-Design | uber database design designed a sql based database for uber application using reverse engineering technique | server |
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cs496-project1 | cs496 project1 first project for cs 496 mobile and cloud development | front_end |
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design-system | warning this version of waffles design system was deprecated at the end of 2022 it s main repository https github com datacamp design system is archived please migrate your app to the new waffles https waffles datacamp com this repository holds all of the code for the waffles design system this includes but is not limited to the documentation site design tokens and a react component library full details on usage can be found on old waffles datacamp com https old waffles datacamp com running locally waffles uses yarn and lerna to manage dependencies follow the following steps to work with the code on a local machine clone the repository execute yarn install in the root directory execute yarn bootstrap to install and link all subdependencies in order to view and work with components locally check out the storybook package https github com datacamp engineering design system tree master packages docs storybook repository structure this repository is a mono repo powered by lerna all the source code can be found in the packages directory this is split into 3 directories to organise different types of packages docs contains all packages relating to documentation other contains published packages that do are neither react components or stylesheets react components contains styled react components stylesheets contains scss stylesheets tools contains private packages that are used within this repo as dev dependencies they are not published to the registry this separation is purely organisational and does not affect the contents of each package every package json should represent its dependencies as if they will be installed from the registry instead of as relative file paths lerna will link these packages locally when running the bootstrap command check the readme files in each directory for more information contributing for guidelines around ci deployment and anything else you may need to contribute to this project check out the contribution guidelines https github com datacamp engineering design system blob master contributing md | os |
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LoctekMotion_IoT | flexispot desk with automation logos images header png image source windows central https www windowscentral com flexispot e5 standing desk review turn your loctekmotion flexispot desk into a smart desk recently i acquired a new standing desk from flexispot during assembly i noticed that the control panel had a rj45 cable and a second rj45 port which sparked my interest can we connect my desk to the internet most of the models flexispot sells are using components from loctekmotion loctekmotion https www loctekmotion com is a manufacturer of lifting columns for height adjustable standing desks on their website they mention a bluetooth receiver https www loctekmotion com shop accessories bt desk app that can be inserted to the control box but i haven t been able to find this this repository will help you to connect your desk to the internet via the serial communication ports rj45 for example for use with home assistant https www home assistant io think of scenarios like controlling your desk via voice or creating notifications when you sit for too long or just because it is cool packages use the information in this repository at your own risk and with caution tinkering with electronics always has risks name description arduino packages arduino custom code to control your desk via an esp32 esp8266 module via mqtt esphome packages esphome control your desk via an esp32 esp8266 module connected to home assistant raspberry pi packages raspberry pi custom code to control your desk via a raspberry pi via python for more packaged solutions see similar projects similar projects research pull requests are welcome research if you are interested in the internals of the loctecmotion desk system have a look at the research below which is composed of my own findings combined with findings of similar projects similar projects research control panels at the time of writing loctekmotion sells 11 different control panels https www loctekmotion com product control panel the features can differ per model but it looks like the serial interface is pretty similar for the more advanced models the tables below will show a mapping of the rj45 pinout to the pinout used by the control panel please note that all rj45 pins are described in the following way rj 45 connector layout images rj 45 connector jpg in order to connect the control box to a raspberry pi and esp32 esp8266 chip i used a rj45 to rs232 adapter https www allekabels nl rs232 kabel 4568 1041186 rj45 naar rs232 html with dupont cables jump wires but you simply can cut and split an ethernet cable as well supported control panels prettier ignore start markdownlint disable table tr td align center a href hs13b 1 br sub b hs13b 1 b sub a td td align center a href hs13a 1 br sub b hs13a 1 b sub a td td align center a href hs01b 1 br sub b hs01b 1 b sub a td tr table markdownlint enable prettier ignore end if your control panel is missing feel free to create an issue https github com imicknl loctekmotion iot issues new to discuss the possibilities or create a pr to add your research to this overview hs13b 1 desk model flexispot e7 tested with control box cb38m2j ib 1 source printed on the pcb of the control box rj45 pin name original cable color ethernet cable color t568b 8 reset brown white orange 7 swim white orange 6 empty purple white green 5 pin 20 red blue 4 rx green white blue 3 tx black green 2 gnd blue white brown 1 5v vdd yellow brown note that rx and tx is defined like this on receiver control panel side so rx can be used to receive data tx to send data hs13a 1 desk model flexispot ek5 tested with control box cb38m2b ib 1 source printed on the pcb of the control box rj45 pin name original cable color ethernet cable color t568b 8 reset swim brown white orange 7 pin 20 white orange 6 rx purple white green 5 tx red blue 4 gnd1 green white blue 3 5v vdd black green 2 29v blue white brown 1 29v yellow brown note that rx and tx is defined like this on receiver control panel side so rx can be used to receive data tx to send data hs01b 1 desk model flexispot e5b tested with control box cb38m2a 1 source nv1t standing desk interceptor https github com nv1t standing desk interceptor rj45 pin name original cable color ethernet cable color t568b 8 5v vdd yellow brown 7 gnd blue white brown 6 tx black green 5 rx green white blue 4 pin 20 red blue 3 unknown purple white green 2 swim white orange 1 res brown white orange note that rx and tx is defined like this on receiver control panel side so rx can be used to receive data tx to send data other control panels control boxes could be supported in the same way but you would need to figure the rj45 pinout mapping most control boxes have an extra rj45 port for serial communication but otherwise you would need to place your device in between the control panel and the control box retrieve current height based upon the great work of minifloat https www mikrocontroller net topic 493524 it became clear that the control panel utilises a 7 segment display https en wikipedia org wiki seven segment display fortunately this is very common in such devices and thus there is a lot of documentation https lastminuteengineers com seven segment arduino tutorial on this topic the control box sends the height as 4 bit hexadecimal which is decoded in the control panel to drive the 7 segment display the second number on the display also supports an optional decimal point 8 segment make sure you set the baud rate to 9600 for most loctekmotion desks the control box only broadcasts the current height for x seconds after you sent the wake up command otherwise you will receive 0x00 0x00 0x00 as payload https alselectro files wordpress com 2015 03 image 27 png source alselectro https alselectro wordpress com 2015 03 03 8051 tutorials 3 interfacing 7 segment display execute a command the control box only accepts commands when the screen is active to activate the screen pin 20 needs to be set to high for about 1 second the screen gets disabled automatically again after some amount of time receiving no commands command list command name start length type payload checksum end wake up 9b 06 02 00 00 6c a1 9d up 9b 06 02 01 00 fc a0 9d down 9b 06 02 02 00 0c a0 9d m 9b 06 02 20 00 ac b8 9d preset 1 9b 06 02 04 00 ac a3 9d preset 2 9b 06 02 08 00 ac a6 9d preset 3 stand 9b 06 02 10 00 ac ac 9d preset 4 sit 9b 06 02 00 01 ac 60 9d all bytes combined will become the command to send to the control box see the packages packages for sample code similar projects research while working on this project i found out that i am not the only one with this idea there are a few repositories on github with great research which helped me kickstart this project grssmnn ha flexispot standing desk https github com grssmnn ha flexispot standing desk home assistant integration via mqtt micropython dude88 loctek iot box https github com dude88 loctek iot box arduino code to control via alexa and mqtt nv1t standing desk interceptor https github com nv1t standing desk interceptor research on intercepting commands from flexispot desks vinzspring loctekreverseengineering https github com vinzspring loctekreverseengineering assumptions research and python samples and a huge thanks to the tweakers net https gathering tweakers net community dutch whom helped me to kickstart this project support join our discord channel https discord gg c7tnzuz9xf | flexispot loctek smart-desk esp8266 esp32 home-assistant standing-desk loctekmotion flexispot-desks | server |
Employee_PostgreSQL_Database | employee postgresql database background it s been two weeks since you were hired as a new data engineer at pewlett hackard a fictional company your first major task is to do a research project about people whom the company employed during the 1980s and 1990s all that remains of the employee database from that period are six csv files for this project you ll design the tables to hold the data from the csv files import the csv files into a sql database and then answer questions about the data that is you ll perform data modeling data engineering and data analysis respectively instructions this challenge is divided into three parts data modeling data engineering and data analysis data modeling inspect the csv files and then sketch an entity relationship diagram of the tables to create the sketch feel free to use a tool like quickdbdlinks https www quickdatabasediagrams com result fig1 db erd employee db erd png data engineering 1 use the provided information to create a table schema for each of the six csv files be sure to do the following remember to specify the data types primary keys foreign keys and other constraints for the primary keys verify that the column is unique otherwise create a composite keylinks to an external site which takes two primary keys to uniquely identify a row be sure to create the tables in the correct order to handle the foreign keys 2 import each csv file into its corresponding sql table data analysis 1 list the employee number last name first name sex and salary of each employee 2 list the first name last name and hire date for the employees who were hired in 1986 3 list the manager of each department along with their department number department name employee number last name and first name 4 list the department number for each employee along with that employee s employee number last name first name and department name 5 list first name last name and sex of each employee whose first name is hercules and whose last name begins with the letter b 6 list each employee in the sales department including their employee number last name and first name 7 list each employee in the sales and development departments including their employee number last name first name and department name 8 list the frequency counts in descending order of all the employee last names that is how many employees share each last name see employee db analysis queries sql employee db 20analysis 20queries sql for queries ran and analysis query out analysis query out folder for the outputs of each query references data generated by mockaroo llc https mockaroo com 2022 realistic data generator 2023 edx boot camps llc | server |
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bsc-thesis-project | bsc thesis project vuorio v 2020 range camera based point cloud visualization update and usage for mobile robot control university of oulu degree programme in computer science and engineering 25 p abstract the development of mobile robots has grown tremendously in recent years because of their wide range of applications they promise upheavals in many different sectors of the economy range cameras are increasingly being used to aid in the detection cognition and navigation of a mobile robot this work investigates how point cloud data collected by a robot equipped with a range camera can visualized in real time in addition the possibility of controlling the robot directly from the visualization tool is investigated the aim is to build a simple implementation based on the requirements and compare it with different approaches the robot is simulated with coppeliasim a simulation software that specializes in modeling and programming robots coppeliasim provides a programming interface b0 based remote api which is used to retrieve information the simulation as well as to modify various parameters the collected data is sent to a server program built in python the purpose of the server is to act as an intermediary between the data source and the visualization tool the employed visualization tool is potree an open source webgl based software that can visualize point clouds of over 500 billion points in real time potree supports the most common data formats las laz binary ply xyz ptx and offers a versatile user interface the collected data was converted to a point cloud and sent to potree the update of the visualization tool was implemented in two different ways based on the results the solutions are not exactly suitable for real time due to drawing speed and memory problems but the solution can be further developed for satisfactory results further development should consider studying the source code of potree in more depth and implement the update scheme for it s mno hierarchy robot navigation was implemented by retrieving a target point using potree s internal functionality the navigation algorithm implemented in the robot was found to be successful in a situation where there are no obstacles between the target point keywords mobile robotics robot control simulation point cloud real time visualization | cloud |
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Nvidia-DLI-s-C-FX-01 | nvidia dli s c fx 01 image https user images githubusercontent com 12884292 42420933 ee53e8aa 82ea 11e8 92f1 f9d3232aeda0 png nvidia s dli course for fundamentals of deep learning for computer vision image https user images githubusercontent com 12884292 42421626 4b013782 82f6 11e8 93bc 53341c1b9be1 png image https user images githubusercontent com 12884292 42421642 777ab8d8 82f6 11e8 9f36 5ae409855b8c png | ai |
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scikit-learn-book | learning scikit learn machine learning in python ipython sources for each chapter of the book this repository holds all the ipython source and data for the learning scikit learn machine learning in python book by ra l garreta and guillermo moncecchi http www packtpub com learning scikit learn machine in python book for the planned 2nd edition we added diego garat as a new author chapter 1 a gentle introduction to machine learning http nbviewer ipython org github gmonce scikit learn book blob master chapter 201 20 20 20a 20gentle 20introduction 20to 20machine 20learning ipynb chapter 1 2nd ed a gentle introduction to machine learning with python and scikit learn http nbviewer ipython org github gmonce scikit learn book blob master chapter 201 20 282nd 20ed 29 20 20a 20gentle 20introduction 20to 20machine 20learning 20with 20python 20and 20scikit learn ipynb extended version including classification clustering and regression warning python 3 chapter 2 supervised learning image recognition with support vector machines http nbviewer ipython org github gmonce scikit learn book blob master chapter 202 20 20supervised 20learning 20 20image 20recognition 20with 20support 20vector 20machines ipynb chapter 2 supervised learning regression http nbviewer ipython org github gmonce scikit learn book blob master chapter 202 20 20supervised 20learning 20 20regression ipynb chapter 2 supervised learning text classification with naive bayes http nbviewer ipython org github gmonce scikit learn book blob master chapter 202 20 20supervised 20learning 20 20text 20classification 20with 20naive 20bayes ipynb chapter 2 supervised learning explaining titanic hypothesis with decision trees http nbviewer ipython org github gmonce scikit learn book blob master chapter 202 20 20supervised 20learning 20 20explaining 20titanic 20hypothesis 20with 20decision 20trees ipynb chapter 3 unsupervised learning clustering handwritten digits http nbviewer ipython org github gmonce scikit learn book blob master chapter 203 20 20unsupervised 20learning 20 20clustering 20handwritten 20digits ipynb chapter 3 unsupervised learning principal component analysis http nbviewer ipython org github gmonce scikit learn book blob master chapter 203 20 20unsupervised 20learning 20 20principal 20component 20analysis ipynb chapter 4 advanced features feature engineering and selection http nbviewer ipython org github gmonce scikit learn book blob master chapter 204 20 20advanced 20features 20 20feature 20engineering 20and 20selection ipynb chapter 4 advanced features model selection http nbviewer ipython org github gmonce scikit learn book blob master chapter 204 20 20advanced 20features 20 20model 20selection ipynb | ai |
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Web-Development | web development contribution in web development under devincept 30 days contributor program module 1 introduction to web development 1 introduction to web development 2 hypertext markup language html fundamentals 3 cascading style sheets css fundamentals 4 javascript js fundamentals module 2 intermediate web development 1 css framework bootstrap bulma 2 the document object model dom 3 css display properties flexbox css grid 4 jquery fundamentals 5 version control git module 3 advanced web development 1 deep dive javascript 2 modern javascript ecmascript 6 es7 3 object oriented programming with javascript 4 asynchronous javascript promises observables async await module 4 reactjs 1 introduction to reactjs 2 application programming interfaces apis with reactjs rest api s graphql api s 3 front end routing in reactjs react router 4 react hooks module 5 back end javascript development 1 back end development with nodejs and expressjs 2 build your own rest api with expressjs 4 integrate front end with back end api s module 6 advanced pre processors 1 introduction to typescript 2 nodejs meet typescript nestjs 3 scss css pre processor module 7 databases 1 overview of databases 2 structured query language sql and nosql mongodb 3 deep dive databases 4 typeorms sequelize module 8 authentication 1 authentication concepts jwt s and passportjs 2 implementing authentication o auth and other 3rd party authentication module 9 other tools techniques third party apis 1 3rd party apis processing payments with stripe razorpay 2 3rd party apis sending emails with nodemailer mailman 3 advanced git back to the basics git review 4 advanced git branching merging and collaborating 5 deployment heroku firebase netlify | front_end |
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design-system-starter | first time setup instructions click this link and make a new repo https github com sparkbox designsystemstarter generate this will start a repo using github s repository templates https github blog 2019 06 06 generate new repositories with repository templates which copies all the files from this one to the new repo add your company s name you ll need to change it from yournamehere in package json package json src data global yaml src data global yaml readme md readme md that s this file consider changing the license the design system starter is licensed with a cc attribution sharealike license you should consider changing the license file license if this doesn t fit the project you re starting with it start styling you can build a color palette in src scss settings variables scss src scss settings variables scss adjust typography and default a styles in src scss tools mixins scss src scss tools mixins scss add font face blocks in src scss settings fonts scss src scss settings fonts scss add link s to stylesheets in src markup patterns drizzle partials stylesheet link tags hbs src markup patterns drizzle partials stylesheet link tags hbs good starting places must be running the app to view these pages common elements page http localhost 3000 demos common elements html the btn component http localhost 3000 patterns components buttons html delete this documentation this documentation is for first time setup go into readme md readme md and delete this list of steps yournamehere design system setup 1 before running the project setup node npm installation instructions https nodejs org en download 2 run npm install 3 run npm start this will clear any previously built project files build project files start the server localhost 3000 run watch tasks drizzle the pattern library is powered by drizzle https github com cloudfour drizzle and will be organized by data https github com cloudfour drizzle tree master docs data pages https github com cloudfour drizzle tree master docs pages patterns https github com cloudfour drizzle tree master docs patterns templates https github com cloudfour drizzle tree master docs templates public the contents of the public directory will be copied directly into the root of the dist directory sass all css is compiled from sass https sass lang com and can be found in the scss directory any files not prefixed with an underscore will compile to dist css javascript all javascript is compiled with webpack https webpack js org and can be found in the js directory all javascript files should be imported into index js which will then be compiled to dist js scripts js testing running npm test will run the following tasks pa11y runs accessibility tests on all html files in the dist directory stylelint checks all css in the dist for errors and enforces sparkbox s code conventions https www npmjs com package sparkbox stylelint config sparkbox eslint checks all javascript in the dist for errors and enforces sparkbox s code conventions https www npmjs com package eslint config sparkbox | os |
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health-blockchain | health blockchain warning work in progress build status https travis ci org ibm bluemix health blockchain svg branch master https travis ci org ibm bluemix health blockchain coverage status https coveralls io repos github ibm bluemix health blockchain badge svg branch react https coveralls io github ibm bluemix health blockchain branch react bluemix deployments https deployment tracker mybluemix net stats 68c5ff9637bb588a929f1557b07ffcc7 badge svg overview a blockchain for fitness data demo fitchain views https raw githubusercontent com ibm bluemix health blockchain master design screens jpeg blockchain is first and foremost about a peer to peer exchange of value the following demonstration reviews the exchange of an individual s fitness workout data for various rewards from organizations they interact with in the demo the workout health data never leaves the person s phone the person accepts a fitness challenge from an organization and each time their workout matches the challenge criteria it is recorded as a transaction in a block during the demo we will view what the individual sees what a network administrator sees and what the organization sees we look behind the scenes at the blockchain fabric where fitness challenge data exists and explore the tenets of shared ledger participants only see transactions they re entitled to see trust endorsements assets privacy anonymity smart contract verifiable queryable searchable encrypted prerequisites ibm bluemix account sign up bluemix signup url for bluemix or use an existing account node js 6 9 1 nvm https github com creationix nvm is recommended deploying the app automatically in bluemix the app comes with a toolchain you can use to deploy the solution with few clicks if you want to deploy it manually you can skip this section 1 ensure your organization has enough quota for one web application using 256mb of memory and 2 services 1 click deploy to bluemix to start the bluemix devops wizard deploy to bluemix https console ng bluemix net devops graphics create toolchain button png https console ng bluemix net devops setup deploy repository https github com ibm bluemix health blockchain branch master 1 select the github box 1 decide whether you want to fork clone the app repository 1 if you decide to clone set a name for your github repository 1 select the delivery pipeline box 1 select the region organization and space where you want to deploy the app 1 click create 1 select the delivery pipeline 1 wait for the deploy job to complete 1 access the app when it s ready and start uploading videos and images run the app locally 1 clone the app to your local environment from your terminal using the following command git clone https github com ibm bluemix health blockchain git 1 cd into this newly created directory 1 create a cloudant service in bluemix cf create service cloudantnosqldb lite health blockchain db 1 in the checkout directory copy the file vcap local template json to vcap local json edit vcap local json and update the credentials for the cloudant service you can retrieve the service credentials from the bluemix console cp vcap local template json vcap local json 1 get the project dependencies npm install 1 run the app npm start data model and api refer to api md api md for details on the app architecture license see license txt license txt for license information bluemix signup url https console ng bluemix net cm mmc githubreadme privacy notice this application is configured to track deployments to ibm bluemix https www bluemix net and other cloud foundry platforms the following information is sent to a deployment tracker https github com ibm bluemix cf deployment tracker service service on each deployment node js package version node js repository url application name application name application guid application id application instance index number instance index space id space id application version application version application uris application uris labels of bound services number of instances for each bound service and associated plan information this data is collected from the package json file in the sample application and the vcap application and vcap services environment variables in ibm bluemix and other cloud foundry platforms this data is used by ibm to track metrics around deployments of sample applications to ibm bluemix to measure the usefulness of our examples so that we can continuously improve the content we offer to you only deployments of sample applications that include code to ping the deployment tracker service will be tracked disabling deployment tracking deployment tracking can be disabled by removing require cf deployment tracker client track from the beginning of the app js file | blockchain blockchain-demos watson bluemix node-js fitness health | blockchain |
Infraengg-Automation-Microservices | this repo for all apps related to platform and devops engineering automation and site reliability engineering ci cd and microservices for ci cd am using jenkins for now and for iac will stick to terraform i will look at pulumi sometime in the future the configuration management tools am using is ansible depending on need basis i will look chef or puppet | cloud |
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gforms | gforms a flexible forms validation and rendering library for golang web development inspired by django forms https docs djangoproject com en dev topics forms and wtforms https github com wtforms wtforms wercker status https app wercker com status 51a7f6720baf8e67a28241790380d19b s wercker status https app wercker com project bykey 51a7f6720baf8e67a28241790380d19b overview validate http request rendering form html support parsing content type form urlencoded json support many widgets for form field getting started install go get github com bluele gforms examples see examples https github com bluele gforms tree master examples usage define form go userform gforms defineform gforms newfields gforms newtextfield name gforms validators gforms required gforms maxlengthvalidator 32 gforms newfloatfield weight gforms validators validate http request server code https github com bluele gforms blob master examples simple form go go type user struct name string gforms name weight float32 gforms weight func main tpltext form method post range i field fields label field getname label field html range ei err field errors label class error err label end br end input type submit form tpl template must template new tpl parse tpltext userform gforms defineform gforms newfields gforms newtextfield name gforms validators gforms required gforms maxlengthvalidator 32 gforms newfloatfield weight gforms validators http handlefunc func w http responsewriter r http request w header set content type text html form userform r if r method post tpl execute w form return if form isvalid tpl execute w form return user user form mapto user fmt fprintf w ok v user http listenandserve 9000 nil client show html form curl x get localhost 9000 users form method post label name label input type text name name value input br label weight label input type text name weight value input br input type submit form valid request curl x post localhost 9000 users d name bluele weight 71 9 ok bluele 71 9 name field is required curl x post localhost 9000 users d weight 71 9 form method post label name label input type text name name value input label class error this field is required label br label weight label input type text name weight value 71 9 input br input type submit form define form by struct model go type user struct name string gforms name weight float32 gforms weight func initform userform gforms definemodelform user gforms newfields override user name field gforms newtextfield name gforms validators gforms required gforms maxlengthvalidator 32 equal an above defined form userform gforms defineform gforms newfields gforms newtextfield name gforms validators gforms required gforms maxlengthvalidator 32 gforms newfloatfield weight gforms validators render html forminstance html go form userform r fmt println form html output input type text name name input input type text name weight input fieldinstance html form userform r fmt println form getfield name html output input type text name name input parse request data default parse http request to create a new forminstance go http handlefunc func w http responsewriter r http request form userform r parse net url values to create a new forminstance go http handlefunc func w http responsewriter r http request parse querystring values form userform fromurlvalues r url query customize formfield attributes go customform gforms defineform gforms newfields gforms newtextfield name gforms validators gforms required gforms textinputwidget map string string class custom form customform r fmt println form html output input type text name name value class custom input custom validation error message go userform gforms defineform gforms newfields gforms newtextfield name gforms validators gforms required custom error required message gforms maxlengthvalidator 32 custom error maxlength message support fields textfield it maps value to forminstance cleaneddata as type string go gforms newtextfield text gforms validators booleanfield it maps value to forminstance cleaneddata as type bool go gforms newbooleanfield checked gforms validators integerfield it maps value to forminstance cleaneddata as type int go gforms newintegerfield number gforms validators floatfield it maps value to forminstance cleaneddata as type float32 or float64 go gforms newfloatfield floatnumber gforms validators multipletextfield it maps value to forminstance cleaneddata as type string go gforms newmultipletextfield texts gforms validators datetimefield it maps value to forminstance cleaneddata as type time time go gforms newdatetimefield date defaultdatetimeformat gforms validators support validators required validator added an error msg to forminstance errors if the field is not provided go gforms validators gforms required regexp validator added an error msg to forminstance errors if the regexp doesn t matched a value go gforms validators gforms regexpvalidator number d email validator added an error msg to forminstance errors if a value doesn t looks like an email address go gforms validators gforms emailvalidator url validator added an error msg to forminstance errors if a value doesn t looks like an url go gforms validators gforms urlvalidator minlength validator added an error msg to forminstance errors if the length of value is less than specified first argument go gforms validators gforms minlengthvalidator 16 maxlength validator added an error msg to forminstance errors if the length of value is greater than specified first argument go gforms validators gforms maxlengthvalidator 256 minvaluevalidator added an error msg to forminstance errors if value is less than specified first argument go gforms validators gforms minvaluevalidator 16 maxvaluevalidator added an error msg to forminstance errors if value is greater than specified first argument go gforms validators gforms maxvaluevalidator 256 support widgets selectwidget go form gforms defineform gforms newfields gforms newtextfield gender gforms validators gforms required gforms selectwidget map string string class custom func gforms selectoptions return gforms stringselectoptions string men 0 women 1 form form fmt println form html output select class custom option value 0 men option option value 1 women option select radioselectwidget go form gforms defineform gforms newfields gforms newtextfield lang gforms validators gforms required gforms radioselectwidget map string string class custom func gforms radiooptions return gforms stringradiooptions string golang 0 false false python 1 false true form form fmt println form html output input type radio name lang value 0 golang input type radio name lang value 1 disabled python checkboxmultiplewidget go form gforms defineform gforms newfields gforms newmultipletextfield lang gforms validators gforms required gforms checkboxmultiplewidget map string string class custom func gforms checkboxoptions return gforms stringcheckboxoptions string golang 0 false false python 1 false true form form fmt println form html output input type checkbox name lang value 0 golang input type checkbox name lang value 1 disabled python todo support filefield datefield datetimefield writing more godoc and unit tests improve performance author jun kimura http github com bluele junkxdev gmail com | front_end |
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System-Design-Interview-Questions | system design interview questions a curated list of system design interview questions for sde 1 experienced sde 2 and above all the questions have been manually curated by me from sites like geeksforgeeks careercup and other interview prep sites targeted companies amazon google facebook and other biggies hope it helps you to prepare for your upcoming interviews questions design commenting system design subscription based sports website which can display scores game status history for any games design netflix search video serving authentication encryption dns lookup caching strategy serving multi quality video etc https www youtube com watch v lyosd2wcjto design a latency management system design a library management system design a notification service https www youtube com watch v cuwt9 l0dog design espn cricinfo cricbuzz design uber https www youtube com watch v tp8kpme zkw design whatsapp https www youtube com watch v rjqjbj2ujdg design quora design lookahead system design google docs collaborative editing service design url shortner service https www youtube com watch v avztry77xxa list plhgw50vuymyckxl3d1ilxovl94wknjfuc index 7 design redbus design bookmyshow design domain backdooring system design amazon locker design movies review aggregator system data should be fetched from movie rating providers like imdb rotten tomatoes etc design offline caching system for ecommerce platform design amazon e commerce https youtu be epasu 1dude list plhgw50vuymyckxl3d1ilxovl94wknjfuc design online chess game multiplayer game design gaming platform a number of games can be hosted on this platform user can login and select a particular game design a last mile delivery platform in case of peak seasons design zomato swiggy foodpanda design meeting calendar system design spotify design promo code api by taking into account amazon s customer traffic into picture design vending machine with following functionalities three types of users user operator admin user can select and buy multiple items at a time money can be inputted multiple times you will get the item if there is a time gap 30 secs he can also do window shopping see only the prices of items and buy nothing operator can load the items and mark the items as expired if needed gets notified if a product goes out of stock admin can own multiple vending machines he should have a analytics report of the items purchased in a month he can also change the prices directly and it should reflect in all the vending machines which he owns exception handling in all the edge cases design splitwise design google pay at scale design a job schedular scalability fault tolerance high availability how scheduler picks up job how will you take care where one job can run for 30 min and one for 30 hour how will you distribute jobs on servers based on frequency time how will you execute them how will you notify back the user about start stop or completion of a job how will your system know if a job is killed terminated due to unknown reasons design meeting scheduler design debugger design automatic parking system design a ranking system we have an infinite supply of words ending with we need to implement a reader program that ranks words on the basis of certain criteria example this is my cat this house belongs to my uncle an amazing country with so many tourist places and so on ranking system criteria rank the words on the basis of occurrence for example output this 2 is 2 my 2 highest rank sorted asc or desc based on provided flag design it completely and scalable ranking system design amazon cart system design google search design twitter https www youtube com watch v ekudbdvbdhs design facebook https www youtube com watch v 9 hjbgxuies design snapchat design instagram https www youtube com watch v 9 hjbgxuies design app store design a music player application design a distributed lru cache design gmail design a recommendation system design a food sharing application design payment module for uber app design truecaller type of system design performance management system appraisal workflow system that can be used across companies design comment system like disqus design flight system design tinder design survey site like surveymonkey design a geographically partitioned multi player card game that supports multiple players multiple games at a time each game will have one contractor like ones we have in a bar he can play a game or just watch it integrate payment systems design a kind of kindle fire application where we can subscribe news channel and read the news from all publishers as a digital format design a realtime video chat like google duo design news paper magazine subscription system design a system like hackerrank codechef topcoder design a concurrent hashmap design an atm machine system which can support massive amount of transactions design airport baggage system design flight information display system design a conference room booking system for a company which can have offices in multiple cities each city can have multiple buildings each building can have multiple floors each floor can have multiple rooms each room can have features like capacitiy video conferencing available etc design newsfeed feature of facebook design an efficient mail delivery system design like dislike feature at youtube scale design paypal design air traffic control system design a realtime service which tells your friends who is online design google maps https www youtube com watch v jk3yvvfnvds design grammarly design airbnb https www youtube com watch v yyoxt2mekv4 design zoom https www youtube com watch v g32thjakehk design twitter https www codekarle com system design twitter system design html design online examination portal like byju s design dropbox or google drive https medium com narengowda system design dropbox or google drive 8fd5da0ce55b | interview interview-preparation interview-questions interview-practice system-design system-design-questions | os |
Fashniii_frontend | getting started with create react app this project was bootstrapped with create react app https github com facebook create react app available scripts in the project directory you can run npm start runs the app in the development mode open http localhost 3000 http localhost 3000 to view it in your browser the page will reload when you make changes you may also see any lint errors in the console npm test launches the test runner in the interactive watch mode see the section about running tests https facebook github io create react app docs running tests for more information npm run build builds the app for production to the build folder it correctly bundles react in production mode and optimizes the build for the best performance the build is minified and the filenames include the hashes your app is ready to be deployed see the section about deployment https facebook github io create react app docs deployment for more information npm run eject note this is a one way operation once you eject you can t go back if you aren t satisfied with the build tool and configuration choices you can eject at any time this command will remove the single build dependency from your project instead it will copy all the configuration files and the transitive dependencies webpack babel eslint etc right into your project so you have full control over them all of the commands except eject will still work but they will point to the copied scripts so you can tweak them at this point you re on your own you don t have to ever use eject the curated feature set is suitable for small and middle deployments and you shouldn t feel obligated to use this feature however we understand that this tool wouldn t be useful if you couldn t customize it when you are ready for it learn more you can learn more in the create react app documentation https facebook github io create react app docs getting started to learn react check out the react documentation https reactjs org code splitting this section has moved here https facebook github io create react app docs code splitting https facebook github io create react app docs code splitting analyzing the bundle size this section has moved here https facebook github io create react app docs analyzing the bundle size https facebook github io create react app docs analyzing the bundle size making a progressive web app this section has moved here https facebook github io create react app docs making a progressive web app https facebook github io create react app docs making a progressive web app advanced configuration this section has moved here https facebook github io create react app docs advanced configuration https facebook github io create react app docs advanced configuration deployment this section has moved here https facebook github io create react app docs deployment https facebook github io create react app docs deployment npm run build fails to minify this section has moved here https facebook github io create react app docs troubleshooting npm run build fails to minify https facebook github io create react app docs troubleshooting npm run build fails to minify | server |
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wiki | bruce wiki my personal wiki for recording web development knowledge tech stack react typescript docusaurus usage install bash pnpm i start bash pnpm dev build bash pnpm build others host by vercel ui design inspired by notion license mit license license copy 2023 bruce song https github com recallwei | blog docs docusaurus wiki react typescript | front_end |
Sparkify-ETL-Pipeline | sparkify etl pipeline this project was created as part of the udacity nanodegree data engineering program sparkify is a fictional music platform the goal is to take json files of song data from the million songs dataset http millionsongdataset com and user log data generated by this event simulator https github com interana eventsim and load them into a postresql database whose schema has been designed to optimize for queries into user song choice patterns this repository contains the files to create a relational database and export the accompanying song and user log files into it once the files are in this format querying the song log data for analysis becomes much easier a few sample queries demonstrating this are shown at the end of this document the database the database created is named sparkifydb and is a relational database with 5 tables organized into a star schema table songplays is the fact table while the other 4 are dimension tables you can see the specific contents of each table in the sql queries py file the data the raw data is stored within the data folder into two subfolders log data and song data both contain json files which are transformed through the etl pipeline into the database log data contains data from user s actions on the sparkify platform while song data contains data on specific songs the scripts the entire etl process is seperated into three scripts sql queries py contains all the necessary sql queries as strings for creating the tables droping the tables and inserting data into the tables there is also a query for matching songs from log data and the song data to ensure consistent song and artist ids create tables py first drops tables if they exist and then creates them accourding to the layout found in sql queries py etl py is the etl pipeline that loads the information from the json files into the relational database running the files create tables py must be run first to create the relevent tables then run etl py there will be printed messages displayed in the command line to confirm all files from log data and song data have been processed sample queries number of songplays of each song sql select song id count songplay id from songplays group by song id number of songplays of each artist sql select atist id count songplay id from songplays group by artist id songs listened to by usera sql select distinct songs song id songs title from songplays join songs on songplays song id songs song id where songplays user id usera | relational-databases etl-pipeline sql postgresql python | server |
IIT | iit introduction to information technology | server |
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grizzled-slf4j | grizzled slf4j a simple scala friendly front end to slf4j build status https travis ci org bmc grizzled slf4j svg branch master https travis ci org bmc grizzled slf4j maven central https maven badges herokuapp com maven central org clapper grizzled slf4j 2 11 badge svg https maven badges herokuapp com maven central org clapper grizzled slf4j 2 11 the grizzled slf4j package provides a very thin scala friendly layer on top of the slf4j http slf4j org simple logging fa ade for java api it is released under a bsd license please see the web site http software clapper org grizzled slf4j for details copyright copy 2010 2018 brian m clapper i bmc clapper org i | front_end |
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nodezoo-web | the nodezoo microservice demonstration architecture this is a repository in the microservice demonstration system for the tao of microservices bit ly rmtaomicro book chapter 9 this code is live at nodezoo com nodezoo com this system shows you how to construct a full microservice architecture it is mit licensed so that you can cut and paste to build your own system with minimal effort the system consists of multiple repositories and runs ten or so microservices in production kubernetes kubernetes io staging docker docker com and development fuge github com apparatus fuge modes the best place to get started is the nodezoo tao github com nodezoo tao repository which links to everything else and has the getting started guide nodezoo web todo | front_end |
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computer-vision-basics-in-microsoft-excel | computer vision basics in microsoft excel creative commons license cc by nc sa 4 0 https licensebuttons net l by nc sa 4 0 88x31 png by alok govil https alokgovil com principal engineer amazon linkedin profile https www linkedin com in alokgovil collaborator venkataramanan subramanian https www linkedin com in venkataramanansubramanian principal engineer amazon computer vision is often seen by software developers and others as a hard field to get into in this article we ll learn computer vision from basics using sample algorithms implemented within microsoft excel using a series of one liner excel formulas we ll use a surprise trick that helps us demonstrate and visualize algorithms like face detection hough transform etc within excel with no dependence on any script or a third party plugin microsoft excel trick for spreadsheet images img excel trick gif figure 1 outline of the steps to visualize a spreadsheet as an image the spreadsheet and thereby the image is then manipulated step by step using formulas selected feedback quotes its amazing to see an image show up on the excel sheet as you zoom out and numbers appear as you zoom back in very cool to see that with simple excel formulas you can do some real computer vision never thought you can explain cv through simple excel formulas and transformations hats off used excel to explain the core concepts and algorithms so clear that i feel i could start working with it right now i ve been wanting to learn how cv works for a while and this was probably the best transition from data to visuals i ve ever seen just incredible build up from small to great one step at a time preview of what we will achieve we will see how to detect a face using a toy example below are screenshots of excel spreadsheets face detection in microsoft excel img face detection jpg even the rectangles lines are drawn using just formulas we will also see how to find edges and lines edge detection in microsoft excel img edges jpg lines in microsoft excel img lines jpg expectations from the audience no prior background in computer vision should be needed to follow the material it is assumed that the audience knows microsoft excel basics and can read its documentation or search online for interpreting the formulas used exceljet https exceljet net is a great resource for the latter some mathematical understanding would be needed those who won t know what weighted average is won t be able to follow much understanding of partial derivatives would be helpful but not required most complex mathematical concept used is eigenvalues https en wikipedia org wiki eigenvalues and eigenvectors but again the readers should be able to follow even if they do not know or remember the same instructions the crux of the material is in the excel files xlsx available below for downloading these are self explanatory with notes inserted within please follow the sheets step by step you may need to change zoom levels as per your monitor resolution screenshot of an excel file img hough transform 50p jpg software requirements the work was created using excel 2016 on windows it should however open in other versions of excel tested with excel 2007 on windows and excel for mac while the files open in libreoffice https www documentfoundation org tested in version 6 4 0 3 x64 it is slow to the level of being unusable even when using native libreoffice calc file format see hacker news discussion on this here https news ycombinator com item id 22357374 we have not tested in apache openoffice https www openoffice org on quick testing it seems to work fine in wps office https en wikipedia org wiki wps office tried on windows 10 relevant excel formula options before opening the excel file s change excel formula calculation to manual since some calculations hough transform specifically are time consuming say an hour then trigger recalculation manually as per need excel formulas change to manual img excel formulas change to manual jpg also uncheck recalculate workbook before saving else excel will recalculate all the formulas every time you save the files don t recalculate workbook before saving img dont recalculate workbook before saving jpg note be sure to revert these settings once you are done those familiar with r1c1 formula reference style in excel or those adventurous should try switching to it by looking in excel options and turning it on see the screenshot below and check the box to enable it this changes the formulas from d5 type format to a relative style like r 1 c 2 absolute references also allowed as r4c5 for example bringing it closer to programming languages and aiding understanding excel r1c1 formula reference style img excel r1c1 formula reference style jpg downloads the full excel file is more than 50 mb in size the same content is also available in smaller parts the following may not be downloadable by right clicking and saving on left clicking github will take you to preview page from where the raw xlsx files can be downloaded contents file description the full excel file computer vision basics in excel computer vision basics in excel xlsx the file is unsurprisingly heavy for excel be patient with it even if it goes busy for an hour excel usually does finish up and come back part 0 computer vision basics in excel 0 introduction and outline computer vision basics in excel 0 introduction and outline xlsx p introduction and outline br start here if following the individual parts p part 1 computer vision basics in excel 1 edges and lines computer vision basics in excel 1 edges and lines xlsx p edges and lines br one of the sheets in this file named hough is very compute intensive p part 2 computer vision basics in excel 2 keypoints and descriptors computer vision basics in excel 2 keypoints and descriptors xlsx p corners keypoints br we do not go into the details of these p part 3 computer vision basics in excel 3 face detection computer vision basics in excel 3 face detection xlsx p face detection br functional face detection demo on the specific input image using simplified viola jones object detection framework p part 4 computer vision basics in excel 4 text computer vision basics in excel 4 text xlsx p character recognition br a toy example that recognizes uppercase e s in the image p questions and answers many of the following would make sense only after going through the excel files above q1 how was the image data imported into excel you can follow this blog article https alvinalexander com blog post java getting rgb values for each pixel in image using java bufferedi and output data into a csv file https en wikipedia org wiki comma separated values which excel readily opens here are two more images imported into excel ready for use einstein image einstein xlsx pillars image pillars xlsx note that the face detection parameters used in the excel files would likely fail to detect einstein s face as the haar like features were fine tuned by hand for detecting mona lisa s face in just that image however the method can again be easily fine tuned for einstein s face and when the parameters are calculated using machine learning https en wikipedia org wiki viola e2 80 93jones object detection framework components of the framework it works on most frontal looking faces assuming not occluded not too small etc see question 4 below for further details on this q2 are the techniques presented still relevant or are they replaced by deep neural networks the techniques are still relevant neural networks are taking over for all complex computer vision problems especially those unsolved by the classical techniques for simpler operations the classical solutions are faster to put together and are usually computationally more efficient also classical techniques are still the default choice for edge devices smartphones web clients though modern techniques are making an entry notably via hardware acceleration e g 1 https petewarden com 2018 06 11 why the future of machine learning is tiny 2 https petewarden com 2019 04 14 what machine learning needs from hardware q3 why was the green channel of the image used and not red or blue how can i represent color images in excel in this fashion of the three primary color channels red green and blue green contributes the most to luminosity ideally the image should be converted to grayscale first or luminosity values should be computed see here https en wikipedia org wiki cie 1931 color space this was skipped just for simplicity of explanation why green img why green jpg one way of representing color images in excel is referenced in the answer to the question 7 below q4 why was the watermark face on the id not detected and yet mona lisa s was we demonstrated the core concept of a popular face detection algorithm https en wikipedia org wiki viola e2 80 93jones object detection framework using just three haar like features https en wikipedia org wiki haar like feature and two stages which were hand crafted to detect the face of mona lisa in that specific image the actual features as well as the stages are in practice calculated using machine learning which commonly results in a few thousands of such features as well as over ten stages then the system is able to detect over 99 of the nearly frontal looking faces while a separate pre trained model is available for faces looking nearly sideways in opencv https en wikipedia org wiki opencv the face shadow on the right would still be missed by the algorithm since such face images are not included in the training data my educated guess further will be that to detect such shadowed faces the algorithm described would not do a good job and using neural networks would be recommended likewise the algorithm we demonstrated is outperformed by a neural networks for labeled faces in the wild http vis www cs umass edu lfw dataset where faces are often partially occluded too q5 in the ocr example how did you choose the mask and its orientation for document ocr https en wikipedia org wiki optical character recognition as opposed to scene text https en wikipedia org wiki scene text recognition the document itself is typically straightened first before character recognition is performed for the characters in the document therefore the characters are expected to be nearly upright in the talk a toy example was shown using a single convolutional https en wikipedia org wiki convolutional neural network neuron to recognize an e neural networks use a number of layers of neurons for the task to recognize all characters of interest the same neural network then outputs which character is present at the input you can imagine this as having a separate simple neural network like for e for recognizing each character of interest the combined neural network would however have several neurons shared in the path for recognizing each character see also the q a below for more on character recognition q6 how well does the ocr approach here work on different fonts in the talk we used a single convolutional neuron https en wikipedia org wiki convolutional neural network to identify an uppercase e as an example the actual systems still commonly use neural networks not just a single neuron for the purpose and that performs well across fonts and languages some additional details are present below in the talk a single neuron was used to both scan the image and recognize the letter typically scanning text of different sizes is done separately using various methods once every character of text is isolated it is re scaled to a fixed size and then a neural network is used to identify the letter handwriting recognition is harder unsurprisingly the best performance is reached when the pen strokes data is available as a function of time e g when recognizing handwriting input on a touch screen references are readily available online for further reading in the example shown in the talk even the weights of that single neuron were hand crafted not actually learned using a training algorithm even a single neuron would do better than the demo when actually trained q7 how did you come up with the idea for using microsoft excel for this about 1 5 years back we had to give an introductory talk on computer vision to a wide audience within amazon many of whom would have been completely unfamiliar with the subject we thought about starting from the very basics by showing that an image is essentially a 2d array of numbers for each color channel for color images and thought about showing these using excel hmm if the numbers are in excel i could do more with it that was the a ha moment it took about seven hours to create the first fully functional version for that talk which did not include face detection and text recognition the latter took about eight more hours for the first version we have since then discovered several related works that represent images in excel using this technique images in excel spreadsheet including color stand up comedy routine about spreadsheets https www youtube com watch v ubx2qqhlq i ray tracing in excel the latest thing to support ray tracing is excel apparently https www pcgamer com the latest thing to support ray tracing is excel apparently 3d engine in excel real 3d engine in excel https www youtube com watch v bfol9kantxa 3d graphics in excel microsoft excel revolutionary 3d game engine https www gamasutra com view feature 131968 microsoft excel revolutionary 3d php excelence by braadworsten brigade https www pouet net prod php which 53021 here are some more related works using excel deep excel http www deepexcel net see also hacker news discussion https news ycombinator com item id 11308718 tatsuo horiuchi the 73 year old excel spreadsheet artist http www spoon tamago com 2013 05 28 tatsuo horiuchi excel spreadsheet artist a few people have mentioned an implementation of super mario bros in excel however as reported here https kottke org 16 08 super mario bros recreated in excel it is not a playable game implementation it s effectively just a video running inside excel see the hacker news discussion https news ycombinator com item id 22357374 for a few more q8 can i use the materials for teaching please see the license summary and details below q9 is excel the right tool for this spreadsheets are not designed for something like this and the technique is not being recommended for any work or research it is however helping many people understand the concepts better while excel has not been designed for this it has been designed well to have worked surprisingly well for this q10 does excel have built in formulas for computer vision no we believe on the very least this work is not using any of them as noted above even the rectangles and lines used for annotations are drawn using generic formulas i e not using any potential special formulas available in excel add ins q11 are there specialized interactive developer environments for computer vision matlab https www mathworks com products matlab html has traditionally been used for this as it has many computer vision functions built in natively or in toolboxes function imshow can be used to instantly display array data as an image python and notebooks based tooling is also very popular q12 is that your passport information on the slides yes however all critical information in the image has been changed like passport number signatures etc including in the machine readable lines at the bottom of the image q13 why was the photo of mona lisa used we just picked an image with no copyright limitations q14 why does hough transform show artifacts for 45 please refer to the answer here https stackoverflow com questions 33983389 hough line transform artifacts at 45 degree angle additional resources and references books below are references to two freely downloadable good books on classical computer vision i e before deep learning came into the field computer vision algorithms and applications http szeliski org book richard szeliski 2010 this books provides a summary of many computer vision techniques along with research results from academic papers the diagrams in the book by themselves are worth browsing through to understand the state of the art in the field till 2010 when the book was published the book usually does not give enough detail to allow someone to implement the methods described though appropriate references are cited computer vision metrics survey taxonomy and analysis https link springer com book 10 1007 2f978 1 4302 5930 5 scott krig 2014 this book provides a good top level view of computer vision though is often mixed on details for practical implementation there are many books on opencv a common computer vision library like learning opencv 3 computer vision in c with the opencv library http shop oreilly com product 0636920044765 do gary bradski adrian kaehler selected articles and blogs corner detection see https en wikipedia org wiki harris corner detector also see other corner detection algorithms like fast https en wikipedia org wiki features from accelerated segment test the following blog talks about feature descriptors very commonly used in computer vision a short introduction to descriptors https gilscvblog com 2013 08 18 a short introduction to descriptors and tutorial on binary descriptors https gilscvblog com 2013 08 26 tutorial on binary descriptors part 1 this blog talks about how feature descriptors are used in a sample application image alignment feature based using opencv c python https www learnopencv com image alignment feature based using opencv c python hacker news discussion on the work https news ycombinator com item id 22357374 https news ycombinator com item id 22357374 license summary copyright 2018 20 amazon com inc or its affiliates all rights reserved spdx license identifier cc by nc sa 4 0 this work is made available under the creative commons attribution noncommercial sharealike 4 0 international public license see the license license file it cannot be used for commercial training or lectures | ai |
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conference_call_for_paper | 2021 2022 international conferences in artificial intelligence machine learning computer vision data mining natural language processing and robotics conference page https jackietseng github io conference call for paper conferences html conference page with ccf https jackietseng github io conference call for paper conferences with ccf html china computer federation recommended ranking for chinese student only todo list auto crawler add item abstract ddl notification due x add function sort by submission deadline p s h index data comes from google scholar https scholar google com citations view op top venues hl en ddls are denoted by tbd if they are not finalized and will be corrected once the conference updates the information these websites is maintained by jackie tseng tsinghua computer vision and intelligent learning lab | ai |
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onchain-pwa-starter | onchain pwa starter a starter kit to create a fully onchain application with a progressive web app pwa mobile client this shows up on a users mobile homescreen with one command get a mobile client synced to onchain state the client syncs and reacts to onchain state updates out of the box using mud https mud dev s client and smart contract libraries social onboarding simple easy onboarding for users without wallets using privy https privy io s embedded wallets pwa detection and installation detect a user s environment guide them to install on mobile and configure pwa settings however you want using vite pwa https vite pwa org netlify app for pwa configuration this is a modification of mud https mud dev s starter template for progressive web apps examples of onchain apps that use pwas include friend tech https friend tech and words art https words art no affiliation with this project get started this project uses pnpm workspaces to install packages and run simply run pnpm install pnpm dev this will deploy smart contracts to a local chain and deploy the client you can also deploy the contracts to the lattice testnet https mud dev tutorials emojimon deploy to testnet by running cd packages contracts pnpm deploy testnet then make sure to set vite chain id 4242 in packages client env and you have a pwa with synced onchain state deployed on testnet it will even automagically drip funds using lattice s faucet service https mud dev cli faucet to whichever wallet you connect with privy so you can submit transactions right away setup privy this starter kit uses privy https privy io for social onboarding creation of embedded wallets on users devices you will need a privy app id to get going grab one at privy io set this app id to the vite privy app id field in the packages client env environment an example environment is provided if you d like embedded wallet functionality make sure the create embedded wallet on user login setting is enabled on your privy dashboard configure contracts this project uses mud https mud dev a solidity library and associated set of client libraries for smart contracts client side onchain state syncing for more info on mud look at mud dev mud dev getting help if you have questions concerns bug reports etc please file an issue in this repository s issue tracker | front_end |
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Text-Rewrite-NLP | text rewrite nlp this lib uses two natural language processing a href https spacy io spacy a amp a href https www nltk org nltk a and a online word finding query engine for developers called a href https www datamuse com api datamuse a as base to rewrite texts first step install python dependencies pre pip install r requirements txt pre second step install spacy en support pre python m spacy download en pre third step install nltk corpora run this code in any python file or python terminal pre import nltk br nltk download pre after that select all corpora and download it last step enjoy img alt example src https i imgur com kwxytan png | ai |
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sample_portfolio_site | the css changes i ve made 1 color i changed the p color to a darker gray 6666 and the btn color to black and a hover state of light gray 2 background color to green 3 font i changed all the h1 h2 h3 h4 h5 p to georgia times new roman times serif font family 4 box or text shadows i add a box shaddow to my profile pic 5 transitions or animation i made the thumbnail to expand on its hover state 6 add a logo although its not the best logo i had added jj to the top of the page | server |
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Holocron | p align center img src https github com frgfm holocron releases download v0 1 3 holocron logo text png width 40 p p align center a href https github com frgfm holocron actions workflows builds yml img alt ci status src https img shields io github actions workflow status frgfm holocron builds yml branch main label ci logo github style flat square a a href https frgfm github io holocron img src https img shields io github actions workflow status frgfm holocron docs yaml branch main label docs logo read the docs style flat square alt documentation status a a href https codecov io gh frgfm holocron img src https img shields io codecov c github frgfm holocron svg logo codecov style flat square alt test coverage percentage a a href https github com ambv black img src https img shields io badge code 20style black 000000 svg style flat square alt black a a href https github com pycqa bandit img src https img shields io badge security bandit yellow svg style flat square alt bandit a a href https www codacy com gh frgfm holocron dashboard utm source github com amp utm medium referral amp utm content frgfm holocron amp utm campaign badge grade img src https app codacy com project badge grade 49fc8908c44b45d3b64131e49558f1e9 a p p align center a href https pypi org project pylocron img src https img shields io pypi v pylocron svg logo python logocolor fff style flat square alt pypi status a a href https anaconda org frgfm pylocron img src https anaconda org frgfm pylocron badges version svg a img src https img shields io pypi pyversions pylocron svg style flat square alt pyversions img src https img shields io pypi l pylocron svg style flat square alt license p p align center a href https huggingface co spaces frgfm holocron img src https img shields io badge f0 9f a4 97 20hugging 20face spaces blue alt huggingface spaces a a href https colab research google com github frgfm notebooks blob main holocron quicktour ipynb img src https colab research google com assets colab badge svg alt open in colab a p implementations of recent deep learning tricks in computer vision easily paired up with your favorite framework and model zoo holocrons were information storage datacron https starwars fandom com wiki datacron devices used by both the jedi order https starwars fandom com wiki jedi order and the sith https starwars fandom com wiki sith that contained ancient lessons or valuable information in holographic https starwars fandom com wiki hologram form source wookieepedia quick tour open in colab https colab research google com assets colab badge svg https colab research google com github frgfm notebooks blob main holocron quicktour ipynb this project was created for quality implementations increased developer flexibility and maximum compatibility with the pytorch ecosystem for instance here is a short snippet to showcase how holocron models are meant to be used python from pil import image from torchvision transforms import compose convertimagedtype normalize piltotensor resize from torchvision transforms functional import interpolationmode from holocron models classification import repvgg a0 load your model model repvgg a0 pretrained true eval read your image img image open path to an image convert rgb preprocessing config model default cfg transform compose resize config input shape 1 interpolation interpolationmode bilinear piltotensor convertimagedtype torch float32 normalize config mean config std input tensor transform img unsqueeze 0 inference with torch inference mode output model input tensor print config classes output squeeze 0 argmax item output squeeze 0 softmax dim 0 max installation prerequisites python 3 8 or higher and pip https pip pypa io en stable conda https docs conda io en latest miniconda html are required to install holocron latest stable release you can install the last stable release of the package using pypi https pypi org project pylocron as follows shell pip install pylocron or using conda https anaconda org frgfm pylocron shell conda install c frgfm pylocron developer mode alternatively if you wish to use the latest features of the project that haven t made their way to a release yet you can install the package from source install git https git scm com book en v2 getting started installing git first shell git clone https github com frgfm holocron git pip install e holocron paper references pytorch layers for every need activation hardmish https github com digantamisra98 h mish nlrelu https arxiv org abs 1908 03682 frelu https arxiv org abs 2007 11824 loss focal loss https arxiv org abs 1708 02002 multilabelcrossentropy mixuploss https arxiv org pdf 1710 09412 pdf classbalancedwrapper https arxiv org abs 1901 05555 complementcrossentropy https arxiv org abs 2009 02189 mutualchannelloss https arxiv org abs 2002 04264 diceloss https arxiv org abs 1606 04797 polyloss https arxiv org abs 2204 12511 convolutions normconv2d https arxiv org pdf 2005 05274v2 pdf add2d https arxiv org pdf 1912 13200 pdf slimconv2d https arxiv org pdf 2003 07469 pdf pyconv2d https arxiv org abs 2006 11538 involution https arxiv org abs 2103 06255 regularization dropblock https arxiv org abs 1810 12890 pooling blurpool2d https arxiv org abs 1904 11486 spp https arxiv org abs 1406 4729 zpool https arxiv org abs 2010 03045 attention sam https arxiv org abs 1807 06521 lambdalayer https openreview net forum id xtjen ggl1b tripletattention https arxiv org abs 2010 03045 models for vision tasks image classification res2net https arxiv org abs 1904 01169 based on the great implementation https github com rwightman pytorch image models blob master timm models res2net py from ross wightman darknet 24 https pjreddie com media files papers yolo 1 pdf darknet 19 https pjreddie com media files papers yolo9000 pdf darknet 53 https pjreddie com media files papers yolov3 pdf cspdarknet 53 https arxiv org abs 1911 11929 resnet https arxiv org abs 1512 03385 resnext https arxiv org abs 1611 05431 tridentnet https arxiv org abs 1901 01892 pyconvresnet https arxiv org abs 2006 11538 rexnet https arxiv org abs 2007 00992 sknet https arxiv org abs 1903 06586 repvgg https arxiv org abs 2101 03697 convnext https arxiv org abs 2201 03545 mobileone https arxiv org abs 2206 04040 object detection yolov1 https pjreddie com media files papers yolo 1 pdf yolov2 https pjreddie com media files papers yolo9000 pdf yolov4 https arxiv org abs 2004 10934 semantic segmentation u net https arxiv org abs 1505 04597 unet https arxiv org abs 1807 10165 unet3 https arxiv org abs 2004 08790 vision related operations boxes distance iou complete iou losses https arxiv org abs 1911 08287 trying something else than adam optimizer lars https arxiv org abs 1708 03888 lamb https arxiv org abs 1904 00962 tadam https arxiv org abs 2003 00179 adamp https arxiv org abs 2006 08217 adabelief https arxiv org abs 2010 07468 adan https arxiv org abs 2208 06677 and customized versions ralars optimizer wrapper lookahead https arxiv org abs 1907 08610 scout experimental more goodies documentation the full package documentation is available here https frgfm github io holocron for detailed specifications demo app the project includes a minimal demo app using gradio https gradio app demo app https github com frgfm holocron releases download v0 1 3 holocron gradio png you can check the live demo hosted on hugs huggingface spaces https huggingface co spaces hugs over here point down hugging face spaces https img shields io badge f0 9f a4 97 20hugging 20face spaces blue https huggingface co spaces frgfm holocron reference scripts reference scripts are provided to train your models using holocron on famous public datasets those scripts currently support the following vision tasks image classification references classification object detection references detection semantic segmentation references segmentation latency benchmark you crave for sota performances but you don t know whether it fits your needs in terms of latency in the table below you will find a latency benchmark for all supported models arch gpu mean std cpu mean std repvgg a0 https frgfm github io holocron latest models html holocron models repvgg a0 3 14ms 0 87ms 23 28ms 1 21ms repvgg a1 https frgfm github io holocron latest models html holocron models repvgg a1 4 13ms 1 00ms 29 61ms 0 46ms repvgg a2 https frgfm github io holocron latest models html holocron models repvgg a2 7 35ms 1 11ms 46 87ms 1 27ms repvgg b0 https frgfm github io holocron latest models html holocron models repvgg b0 4 23ms 1 04ms 33 16ms 0 58ms repvgg b1 https frgfm github io holocron latest models html holocron models repvgg b1 12 48ms 0 96ms 100 66ms 1 46ms repvgg b2 https frgfm github io holocron latest models html holocron models repvgg b2 20 12ms 0 31ms 155 90ms 1 59ms repvgg b3 https frgfm github io holocron latest models html holocron models repvgg b3 24 94ms 1 70ms 224 68ms 14 27ms rexnet1 0x https frgfm github io holocron latest models html holocron models rexnet1 0x 6 01ms 0 26ms 13 66ms 0 21ms rexnet1 3x https frgfm github io holocron latest models html holocron models rexnet1 3x 6 43ms 0 10ms 19 13ms 2 05ms rexnet1 5x https frgfm github io holocron latest models html holocron models rexnet1 5x 6 46ms 0 28ms 21 06ms 0 24ms rexnet2 0x https frgfm github io holocron latest models html holocron models rexnet2 0x 6 75ms 0 21ms 31 77ms 3 28ms rexnet2 2x https frgfm github io holocron latest models html holocron models rexnet2 2x 6 92ms 0 51ms 33 61ms 0 60ms sknet50 https frgfm github io holocron latest models html holocron models sknet50 11 40ms 0 38ms 54 03ms 3 35ms sknet101 https frgfm github io holocron latest models html holocron models sknet101 23 55 ms 1 11ms 94 89ms 5 61ms sknet152 https frgfm github io holocron latest models html holocron models sknet152 69 81ms 0 60ms 253 07ms 3 33ms tridentnet50 https frgfm github io holocron latest models html holocron models tridentnet50 16 62ms 1 21ms 142 85ms 5 33ms res2net50 26w 4s https frgfm github io holocron latest models html holocron models res2net50 26w 4s 9 25ms 0 22ms 41 84ms 0 80ms resnet50d https frgfm github io holocron latest models html holocron models resnet50d 36 97ms 3 58ms 36 97ms 3 58ms pyconv resnet50 https frgfm github io holocron latest models html holocron models pyconv resnet50 20 03ms 0 28ms 178 85ms 2 35ms pyconvhg resnet50 https frgfm github io holocron latest models html holocron models pyconvhg resnet50 38 41ms 0 33ms 301 03ms 12 39ms darknet24 https frgfm github io holocron latest models html holocron models darknet24 3 94ms 1 08ms 29 39ms 0 78ms darknet19 https frgfm github io holocron latest models html holocron models darknet19 3 17ms 0 59ms 26 36ms 2 80ms darknet53 https frgfm github io holocron latest models html holocron models darknet53 7 12ms 1 35ms 53 20ms 1 17ms cspdarknet53 https frgfm github io holocron latest models html holocron models cspdarknet53 6 41ms 0 21ms 48 05ms 3 68ms cspdarknet53 mish https frgfm github io holocron latest models html holocron models cspdarknet53 mish 6 88ms 0 51ms 67 78ms 2 90ms the reported latency for repvgg models is the one of the reparametrized version this benchmark was performed over 100 iterations on 224 224 inputs on a laptop to better reflect performances that can be expected by common users the hardware setup includes an intel r core tm i7 10750h https ark intel com content www us en ark products 201837 intel core i710750h processor 12m cache up to 5 00 ghz html for the cpu and a nvidia geforce rtx 2070 with max q design https www nvidia com fr fr geforce graphics cards rtx 2070 for the gpu you can run this latency benchmark for any model on your hardware as follows bash python scripts eval latency py rexnet1 0x all script arguments can be checked using python scripts eval latency py help docker container if you wish to deploy containerized environments you can use the provided dockerfile to build a docker image shell docker build t your image tag minimal api template looking for a boilerplate to deploy a model from holocron with a rest api thanks to the wonderful fastapi https github com tiangolo fastapi framework you can do this easily deploy your api locally run your api in a docker container as follows shell cd api make lock api make run api in order to stop the container use make stop api what you have deployed your api is now running on port 8080 with its documentation http localhost 8080 redoc and requestable routes python import requests with open path to your img jpeg rb as f data f read response requests post http localhost 8080 classification files file data json citation if you wish to cite this project feel free to use this bibtex http www bibtex org reference bibtex software fernandez holocron 2020 author fernandez fran ois guillaume month 5 title holocron url https github com frgfm holocron year 2020 contributing any sort of contribution is greatly appreciated you can find a short guide in contributing contributing md to help grow this project license distributed under the apache 2 0 license see license license for more information | pytorch deep-learning computer-vision tridentnet darknet yolo unet-image-segmentation resnet rexnet cspdarknet53 yolov4 object-detection | ai |
lisk-explorer | logo docs assets banner explorer png lisk explorer lisk explorer is a blockchain explorer designed for interaction with lisk network it is a part of lisk ecosystem https lisk io a blockchain platform based on dpos consensus protocol license gpl v3 https img shields io badge license gpl 20v3 blue svg http www gnu org licenses gpl 3 0 lisk explorer is a feature rich single page browser application with following functionalities transaction browser https explorer lisk io txs shows transactions with their details stored in the blockchain supports all transaction types with its metadata allows advanced filtering by date type amount etc block browser https explorer lisk io blocks shows blocks with their details stored in the blockchain account browser https explorer lisk io address 6307579970857064486l supports various account types allows advanced transaction filtering on per account basis delegate monitor https explorer lisk io delegatemonitor shows information about all register delegate accounts live status of the 101 active delegates network monitor https explorer lisk io networkmonitor shows live information about all nodes shows active and disconnected nodes public ips are shown with domain names and geographical location to make it running at least one lisk core sdk node with public api is needed installation clone the lisk explorer repository git clone https github com liskhq lisk explorer git cd lisk explorer installation with docker recommended in order to install docker refer to the official docker installation instruction https docs docker com install you will also need docker compose install docker compose https docs docker com compose install the docker compose configuration assumes that the network called localhost is available on your machine if there is no such a network created you can always add one by using the following command docker network create localhost starting application to start explorer type the following command docker compose up d it will use latest available version from local hub by default stopping application the following command will remove all containers defined by the docker compose yml docker compose down volumes for further information about managing and configuring lisk explorer see run with docker docs run with docker md installation from source for further information about managing and configuring lisk explorer see run from source docs run from source md running tests there are functional backend and end to end tests available the test suite is described in run tests docs run tests md section get involved found a bug create new issue https github com liskhq lisk explorer issues new want to develop with us read contribution guidelines https github com liskhq lisk explorer blob development docs contributing md have ideas to share come to lisk chat http lisk chat want to involve community join community gitter https gitter im liskhq lisk utm source badge utm medium badge utm campaign pr badge utm content badge found a security issue see our bounty program https blog lisk io announcing lisk bug bounty program 5895bdd46ed4 contributors https github com liskhq lisk explorer graphs contributors license copyright 2016 2019 lisk foundation this program is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation either version 3 of the license or at your option any later version this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details you should have received a copy of the gnu general public license license along with this program if not see http www gnu org licenses this program also incorporates work previously released with lisk explorer 1 1 0 and earlier versions under the mit license https opensource org licenses mit to comply with the requirements of that license the following permission notice applicable to those parts of the code only is included below copyright 2016 2017 lisk foundation copyright 2015 crypti 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 | blockchain |
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SWEN304P2 | swen304p2 database systems engineering project 2 | server |
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design-system | p align center a href https strapi io img src assets logo svg width 318px alt strapi logo a p p align center a style margin right 10px href https design system strapi io documentation a a style margin left 10px href https design system git main strapijs vercel app try components a p br version https img shields io npm v strapi design system style flat colora 4945ff colorb 4945ff https www npmjs com package strapi design system downloads https img shields io npm dt strapi design system svg style flat colora 4945ff colorb 4945ff https www npmjs com package strapi design system discord shield https img shields io discord 811989166782021633 style flat colora 4945ff colorb 4945ff label discord logo discord logocolor f0f0ff https discord gg strapi welcome strapi design system provides guidelines and tools to help anyone make strapi s contributions more cohesive and to build plugins more efficiently installation install strapi design system and its peer dependencies sh yarn add react react dom strapi design system strapi icons styled components react router dom or npm i react react dom strapi design system strapi icons styled components react router dom getting started wrap your application with the designsystemprovider you can additionally pass a theme and or locale although you don t have to as we have default values for both jsx import designsystemprovider lighttheme from strapi design system function myapp children return designsystemprovider locale en gb theme lighttheme children designsystemprovider export default app then checkout the complete storybook documentation https design system git main strapijs vercel app to find the components you want to use and how to use them contributing please follow our contributing https github com strapi design system blob main contributing md guidelines license licensed under the mit license copyright 2015 present strapi solutions sas https strapi io see license https github com strapi design system blob main license for more information | design-system styled-components | os |
cloud-espm-v2 | https img shields io badge status not 20currently 20maintained red svg longcache true style flat important notice this public repository is read only and no longer maintained enterprise sales procurement model espm application the espm enterprise sales procurement model application is a reference application which demonstrates how to build applications on sap business technology platform btp with the java runtime the application also consumes and showcases services like the persistence service document service sap jam collaboration and api management which are offered by the platform the application user interface ui is built with the sapui5 framework after the sap fiori design principles demo url of the application https espmrefapps hana ondemand com espm cloud web webshop https espmrefapps hana ondemand com espm cloud web webshop business scenario the business scenario is that of an ecommerce site that sells electronic products the customers can order products and provide reviews on the products the retailer can then accept the sales orders created against orders created by customers the retailer can also update the product stock information usecase diagram docs images espmusecase png raw true get the source code clone the git repository https github com sap cloud espm v2 git or download the latest release 1 quick start guide setting up the developer environment 1 install oracle java se development kit jdk 8 and set up the java home and path environment variables on your local machine 2 install eclipse https tools hana ondemand com oxygen please install eclipse oxygen 3 optional only if you use sap jvm set up sap jvm in eclipse https help sap com viewer ea72206b834e4ace9cd834feed6c0e09 cloud en us da030d10d97610149defa1084cb0b2f1 html 4 install sap development tools for eclipse https help sap com viewer ea72206b834e4ace9cd834feed6c0e09 cloud en us 76137a37711e1014839a8273b0e91070 html please use the link for eclipse oxygen 5 install sap business technology platform sdk https tools hana ondemand com cloud please install java web tomcat 8 6 register for a free developer account on sap business technology platform follow the tutorial https developers sap com tutorials hcp create trial account html build the application and deploy below are the steps to build and run the espm application sh 1 git configuration in eclipse 2 maven configuration 3 clone git repository and import maven project 4 update dependencies and build maven project 5 deploy the application on local cloud runtime 6 deploy the application on sap btp via the cockpit 7 bind the database to espm application and start espm application 1 git configuration in eclipse from the eclipse ide main menu choose window preferences enter git in the filter field in the top left corner navigate to team git configuration and select the configuration node and add the following configuration egit configuration docs images eclipse git settings png raw true note for most people the proxy value doesn t need to be set but if you are working behind a proxy then it should be set as per you environment 2 maven configuration from the eclipse ide main menu choose window preferences enter maven in the filter field in the top left corner navigate to maven user settings and select the user settings node if you have already installed maven before you can click the open file link if you are using maven for the first time you need to create a settings xml file at the location users your user name m2 settings xml the contents of the settings xml file should look like the snippet below maven settings configuration docs images eclipse maven settings png raw true settings xmlns http maven apache org settings 1 0 0 xmlns xsi http www w3 org 2001 xmlschema instance xsi schemalocation http maven apache org settings 1 0 0 http maven apache org xsd settings 1 0 0 xsd localrepository user home m2 repository localrepository profiles profile id development id activation activebydefault true activebydefault activation properties properties profile profiles proxies proxy active true active protocol http protocol host proxy host port 8080 port proxy proxies settings note for most people the proxy value doesn t need to be set you can remove the entire proxy section from the snippet but if you are working behind a proxy then it should be set as per you environment 3 clone git repository and import maven project open https github com sap cloud espm v2 with your web browser click on the copy to clipboard so that the git repository url of the opened github repository is copied to your clipboard repo url docs images clonegithub repo png raw true in eclipse open the git perspective from the eclipse ide main menu choose window open perspective other select git and choose ok to open the git perspective in this perspective you have the git repositories view on the left as long as there is no git repository defined you will see 3 links as shown here to add a repository to the view git clone docs images clonegit png raw true in the corresponding menu top right of the view click on the clone a git repository link because you copied before the espm repository url to your clipboard the fields uri host repository path and protocol of the opened dialog are filled automatically with the correct values do not change anything just click next on this wizard page check that the base branch is selected and click again on next on the last wizard page you can adjust the location of the local git repository but for the scope of this tutorial we ll just leave the default as is click on finish so that the remote espm repository source code is cloned to the local location specified on the last wizard page in eclipse open file import existing maven projects and import the maven project 4 update dependencies and build maven project instruction to run update dependencies for the maven project right click on the web project in espm and choose maven click on update project maven update docs images mavenupdate png raw true note if you face errors you need to modify the parent pom xml for certain property values depending on your environment local server proxy settings delete this if you are not behind a proxy server else update your proxy settings here browser proxy settings delete this if your browser is not using a proxy else update your browser proxy settings here sap cloud sdk version the sap business technology platform sdk for java web tomcat 8 version that you intend to run the application with is version 3 20 3 1 olingo version the apache olingo version that you intend the application to run with the minimum version supported is 2 0 6 the application can be built with the maven command to the parent pom xml sh clean install after building the application update the maven project right click on the web project in espm and choose maven click on update project the unit tests and the integration tests are run by default when building the project with goal clean install 5 deploy the application on local cloud runtime i run the application in btp java web tomcat 8 server right click on the web project in espm and choose the run on server option run espm locally docs images runespm1 png raw true make sure that manually define a new server is selected and choose sap java web tomcat 8 server as server type leave all other settings unchanged and choose finish run espm finish docs images runespm2 png raw true now a local server is started that has your espm application deployed ii create users and assign role to enable local users to access the retailer ui you need to define user ids in the local user store and assigned the role retailer to this user create a user with the below information id name password role ret ret 123 retailer create user locally docs images localuser png raw true the ecommerce site can be accessed via the url https localhost port espm cloud web webshop the retailer ui can be accessed via the url https localhost port espm cloud web retailer 6 deploy the application on sap btp via the cockpit note the application name must be espm else the above url will change based on the application name given during deployment deploy the application in your sap business technology platform trial account 1 go to btp cockpit click on java application under applications click on deploy application btp cockpit docs images hcpcockpit png raw true 2 add war file location give application name espm select runtime name java web tomcat 8 and jvm version jre 8 btp deploy docs images hcpdeploy png raw true 3 after successful deployment click on start deployed docs images deployed png raw true configure the application role assignments from the cockpit https help hana ondemand com help frameset htm db8175b9d976101484e6fa303b108acd html you basically need to add the retailer role to your sap business technology platform user to access the retailer ui you can access the application from the url the ecommerce site can be accessed via the url https espm account hanatrial ondemand com espm cloud web webshop the retailer ui can be accessed via the url https espm account hanatrial ondemand com espm cloud web retailer note the application name must be espm else the above url will change bsaed on the application name given during deployment 7 bind the database to espm application and start espm application below is the process to bind the database to the java application in btp trial account using a shared hana database in the cockpit select an account and choose persistence databases schemas in the navigation area click on the new button in the popup window fill the mandatory details new database schema docs images newdatabaseschema jpg raw true in the cockpit select an account and choose applications java application click on the name of the espm application that you deployed in the navigation area in the cockpit select configuration data source bindings click new binding button in detail plane in the popup window fill the mandatory details make sure to use system as the database user and also uncheck the verify credentials checkbox before saving datasourcebinding docs images datasourcebinding jpg raw true now you need to restart your espm application stop if already started and start the application from the cockpit demo script for espm webshop docs demoscript webshopreadme md demo script for espm retailer salesorderapproval docs demoscript retailer salesorderapprovalreadme md demo script for espm retailer stockupdate docs demoscript retailer stockupdatereadme md documentation for document service docs documentation documentservicereadme md documentation for sap jam integration docs documentation sapjamintegrationreadme md 2 deep dive guide architecture overview the following diagram provides an overview of the espm sample application architecture architecture diagram docs images architecture jpg raw true reading the above architecture diagram from top to bottom we have the following components sapui5 layer all the front end code of the application is written in sapui5 the end user accesses the application using web browser preferably latest google chrome odata layer the espm application services are implemented in odata version 2 0 we use apache olingo for transforming jpa models into odata services for more information on apache olingo jpa models transformation to odata services see details http olingo apache org doc odata2 tutorials createwebapp html jpa layer the persistence of the application is implemented using java jpa we use eclipselink as the persistence provider persistence the persistence used for the application is sap hana db in cloud btp all the tables modelled in java jpa are created in the hana db the espm sample application is a maven based project which has a parent pom xml file and 2 sub projects as below espm cloud jpa this project contains the source code implementation for the backend jpa along with unit tests below is the jpa class diagram espm cloud web this project contains the source code implementation for generating the odata services for jpa entities implemented in espm cloud jpa and front end of the application created using sapui5 the odata integration tests are also implemented in this package the jpa class diagram jpa class diagram docs images espm cloud jpa png raw true espm source code packages the espm cloud jpa has the following packages src main java com sap espm model jpa model classes for persistence com sap espm model data data loader classes to populate the master data for the ecommerce site like product information etc com sap espm model util the utility classses src main resources com sap espm model data xml files containing master data for the ecommerce site like products product categories etc src test java com sap espm model the unit tests for the database entities com sap espm model util the test factory class espm cloud web has the following packages src main java com sap espm model function impl the classes for function import implementation for odata services using apache olingo com sap espm model web the startup servlet and apache olingo odata service implementation classes src test java com sap espm model web the odata integration tests classes com sap espm model web util the utility classes for the tests src test resources com sap espm model web the xml files for the odata integration tests the ui is located in webapps folder the frontend is implemented in sapui5 securing odata services the espm application consists of 2 parts espm webshop and espm retailer the application is split into 2 parts by a secure url and non secure url secure url http appname accountname hana ondemand com espm cloud web espm svc secure non secure url http appname accountname hana ondemand com espm cloud web espm svc the webshop application uses non secure url as any user without any logon credentials can create a sales order the retailer application uses secure url as only a user with java ee web role retailer can access it to implement security we use a mix of java ee web roles and servlet filter web xml for security docs images webxml png raw true any access to the secure entity and respective http method via secure url espm svc secure is secured by java ee web role retailer the form authentication is specified for this retailer and url pattern espm svc secure is restricted to this role this is translated to saml authentication on deploying in cloud secure url docs images secureurl png raw true any access to secure entity and respective http via non secure url is restricted by a servlet filter the servlet filter is defined under the package com sap espm model web as espmservicefactoryfilter class and specified in the web xml the logic for restricting access to secure entities via non secure url is implemented in ispathrestricted secure url docs images servletfilter png raw true after deploying the application in btp assign the retailer role to the user who will act as the retailer of the ecommerce site please refer to documentation of sap business technology platform on how to assign roles to users important disclaimers on security and legal aspects this document is for informational purposes only its content is subject to change without notice and sap does not warrant that it is error free sap makes no warranties express or implied or of merchantability or fitness for a particular purpose coding samples any software coding and or code lines strings code included in this documentation are only examples and are not intended to be used in a productive system environment the code is only intended to better explain and visualize the syntax and phrasing rules of certain coding sap does not warrant the correctness and completeness of the code given herein and sap shall not be liable for errors or damages caused by the usage of the code unless damages were caused by sap intentionally or by sap s gross negligence copyright and license copyright 2016 sap se licensed under the apache license version 2 0 the license you may not use this work except in compliance with the license you may obtain a copy of the license in the license file or at http www apache org licenses license 2 0 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 | sample sample-code sapui5 sap-hana-db sap-btp sap-hana espm | server |
gprbuild | preliminary note for windows users the build instructions for gprbuild may have a slight unix flavor but they can be used on windows platforms with a full cygwin installation the latter makes it simpler to build gprbuild but is not required to use it bootstrapping gprbuild needs gprbuild to build so we also provide a way to easily bootstrap if you don t already have gprbuild provided you already have installed gnat download xml ada sources from https github com adacore xmlada together with the gprconfig knowledge base from https github com adacore gprconfig kb run the bootstrap sh script written for posix systems specifying the install location and the sources of xml ada and gprconfig kb the script will build and install gprbuild for example to build gprbuild and install it to bootstrap in the current working directory run bootstrap sh with xmlada xmlada with kb gprconfig kb prefix bootstrap for maintainers the environment destdir is honoured for staged installs see bootstrap sh h for more with this bootstrapped gprbuild you can build xml ada and gprbuild as documented below configuring you should first configure the build like this if you plan to build in the source tree directly you can omit setting the source dir variable make prefix xxx source dir path to gprbuild sources setup building and installing xml ada must be installed before building building the main executables is done simply with make all when compiling you can choose whether you want to link statically with xml ada the default or dynamically to compile dynamically you should run make library type relocatable all instead of the above installation is done with make install doc examples the documentation is provided in various formats in the doc subdirectory you can also find it online http docs adacore com gprbuild docs html gprbuild ug html it refers to concrete examples that are to be found in the examples subdirectory each example can be built easily using the simple attached makefile make all build the example make run run the executable s make clean cleanup all the examples can be built run cleaned using the same targets and the top level examples makefile | os |
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rate_limited | rate limited pypi version https badge fury io py rate limited svg https badge fury io py rate limited python versions https img shields io badge python 3 8 blue svg lint and test https github com tpietruszka rate limited actions workflows lint and test yml badge svg branch main https github com tpietruszka rate limited actions workflows lint and test yml query branch main publish to pypi https github com tpietruszka rate limited actions workflows pypi publish yml badge svg https github com tpietruszka rate limited actions workflows pypi publish yml a lightweight package meant to enable efficient parallel use of slow rate limited apis like those of large language models llms features parallel execution be as quick as possible within the rate limit you have retry failed requests validate the response against your criteria and retry if not valid for non deterministic apis if interrupted keyboardinterrupt e g in a notebook returns partial results if ran again continues from where it left off and returns full results rate limits of some popular apis already described custom ones easy to add no dependencies will use tqdm progress bars if installed assumptions the client handles timeouts requests will not hang forever raises an exception if the request fails or the server returns an error an invalid response can be any type of a callable function method coroutine function partial etc requests are independent from each other can be retried if failed we want a standard simple interface for the user working the same way in a script and in a notebook most data scientists do not want to deal with asyncio therefore runner run is a blocking call with the same behavior regardless of the context async use is also supported via run coro but it will not support keyboardinterrupt handling installation shell pip install rate limited usage in short wrap your function with a runner describing the rate limits you have call runner schedule to schedule a request with the same arguments as you would call the original function call runner run to run the scheduled requests get the results and any exceptions raised results are returned in the order of scheduling creating a runner the following arguments are required function the callable of the api client resources a list of resource objects describing the rate limits you have see examples below important optional arguments max retries the maximum number of retries for a single request default 5 max concurrent the maximum number of requests to be executed in parallel default 64 validation function a function that validates the response and returns true if it is valid e g conforms to the schema you expect if not valid the request will be retried openai example python import openai from rate limited runner import runner from rate limited apis openai import chat openai api key your api key model gpt 3 5 turbo describe your rate limits values based on https platform openai com account rate limits resources chat openai chat resources requests per minute 3 500 tokens per minute 90 000 model max len 4096 property of the model version in use max sequence length runner runner openai chatcompletion create resources topics honey badgers llamas pandas for topic in topics messages role user content f please write a poem about topic call runner schedule exactly like you would call openai chatcompletion create runner schedule model model messages messages max tokens 256 request timeout 60 results exceptions runner run validating the response we can provide custom validation logic to the runner to retry the request if the response does not meet our criteria for example if it does not conform to the schema we expect this assumes that the api is non deterministic example above continued python def character number is even response poem response choices 0 message content return len ch for ch in poem if ch isalpha 2 0 validating runner runner openai chatcompletion create resources validation function character number is even for topic in topics messages role user content f please write a short poem about topic containing an even number of letters validating runner schedule model model messages messages max tokens 256 request timeout 60 results exceptions validating runner run custom server with a requests per minute limit proceed as above replace openai chatcompletion create with your own function and describe resources as follows python from rate limited resources import resource resources resource requests per minute quota 100 time window seconds 60 arguments usage extractor lambda 1 more complex resource descriptions overall the core of an api description is a list of resource objects each describing a single resource and its limit e g requests per minute tokens per minute images per hour etc each resource has a name just for logging debugging a quota e g 100 a time window e g 60 seconds functions that extract the billing information arguments usage extractor results usage extractor max results usage estimator two distinct billing models are supported before the call we register usage before making the call based on the arguments of the call in these cases just arguments usage extractor is needed after the call we register usage after making the call based on the results of the call and possibly the arguments if needed for some complex cases to avoid sending a flood or requests before the first ones complete and we register any usage we need to estimate the maximum usage of each call based on its arguments we pre allocate this usage then register the actual usage after the call completes in these cases results usage extractor and max results usage estimator are needed both approaches can be used in the same resource description if needed note it is assumed that resource usage expires fully after the time window elapses without a gradual decline some apis openai might use the gradual approach and differ in other details but this approach seems sufficient to get close enough to the actual rate limits without running into them limiting the number of concurrent requests the max concurrent argument of runner controls the number of concurrent requests more advanced usage see apis openai chat rate limited apis openai chat py for an example of a more complex api description with multiple resources implementation details concurrency model the package uses a single thread with an asyncio event loop to kick off requests and keep track of the resources used however threads are also in use each call is actually executed in a thread pool to avoid blocking the runner if the client passed to the runner is synchronous blocking runner run spawns a new thread and a new event loop to run the main runner logic in run coro this serves two objectives having the same sync entrypoint for both sync and async contexts e g run the same code in a notebook and in a script gracefully handling keyboardinterrupt development and testing install the package in editable mode with pip install e test lint to quickly run the whole test suite run the following in the root directory of the project shell pytest n 8 or another number of workers this uses pytest xdist to run tests in parallel but will not support pdb or verbose output to debug tests in detail examine the usage of resources etc run shell pytest log cli level debug pdb k name of test linting and formatting format shell isort black check shell flake8 black check mypy todos make it easier to import things perhaps dedicated runner classes openaichatrunner etc more ready made api descriptions incl batched ones examples of using each pre made api description fix the interrupt and resume test in python 3 11 nice to have optional slow start feature pace the initial requests instead of sending them all at once better utilization of apis with a gradual quota growth like openai text based logging if tqdm is not installed if where possible detect ratelimitexceeded notify the user slow down support streaming and or continuous operation enable scheduling calls while running and or getting inputs from generators support streaming results perhaps similar to as completed in asyncio support async calls for people who prefer openai s acreate etc add timeouts option for now the user is responsible for handling timeouts openai shares information about rate limits in http response headers could it be used without coupling too tightly with their api tests and explicit support for different ways of registering usage time of request vs time of completion vs gradual more robust wrapper like behavior of schedule more complete support of vs code | batch-processing data-science large-language-models rate-limiting | ai |
rmdi | react material design icons built with pixo pixo styled components sc and styled system sys https jxnblk com rmdi pixo https github com c8r pixo sys https github com jxnblk styled system sc https github com styled components styled components sh npm i rmdi jsx import icons individually for better tree shaking import accessibility from rmdi lib accessibility const app props accessibility size 32 color tomato jsx import all icons as a single component import icon from rmdi const app props icon name accessibility size 32 color tomato list of icons see the icon list icons md for a complete list of all icons available props prop type description size number width and height in pixels color string fill color uses styled system s color color function spacing props margin can be applied with the following props which use styled system s space space function margin props accept numbers for pixel values strings with css units or arrays for responsive responsive margin prop description m margin mt margin top mr margin right mb margin bottom ml margin left mx margin left and margin right my margin top and margin bottom color https github com jxnblk styled system color responsive space https github com jxnblk styled system space responsive responsive https github com jxnblk styled system responsive styles contributing sh npm install the build process will 1 parse the material design icons package for svg source code 2 copy the icons to the svg folder 3 create an examples folder for tests and development 4 run pixo pixo on the svg folder and output to src 5 run babel on the src folder and output to lib tests sh npm test to run the development server sh npm start related google material design icons https github com google material design icons pixo pixo styled components sc styled system sys react icons https github com gorangajic react icons mit license license md | react icons material-design pixo svg components | os |
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image-recognition image-analysis vision-framework embedded detection iot-device iot webassembly | ai |
tlc-data-pipeline | tlc data pipeline this is an automated solution to collect tlc trip record data https www1 nyc gov site tlc about tlc trip record data page and upload it to a database on azure which was created as part of the macaw data engineering academy assessment task 1 introduction prerequisites 2 installation installation 3 methods methods 4 technologies technologies 5 license license 6 contact contact preprequisites in order for the script to be used without any problems you need to have at least python 3 9 https www python org downloads installed additionally azure cli https docs microsoft com en us cli azure install azure cli needs to be installed as the script executes cli commands to whitelist your ip address which is for the storage account and database to be connected and the data to eventually be stored in in the database installation the installation guide assumes that you re using windows and have gitbash https git scm com downloads installed 1 clone the repository using the terminal inside the terminal run git clone https github com virbickt tlc data pipeline git a folder called tlc data pipeline will be created on your current folder 2 it s a good idea to create a virtual environment prior to installing all the dependencies this can be done again using the terminal by running python m venv venv 3 activate the virtual environment using the terminal source venv scripts activate the packages that the script makes use of can be install via pip python pip install r requirements txt 4 start the script using the terminal as you would any other python file python app py methods collector generate urls generate urls generates a list of urls to collect the data from by iterating over the range of years that the collector object has been inicialized with if the number for the month consists in a single digit it is appended with 0 returns urls list str the list that contains urls for each month s data in csv format extract data extract data url filename retrieves the content of the page which are then written into a csv file which is saved by the name specified as filename parameters url str the url to retrieve the content from and write into a csv file file name str the name which is to be used to save the file with returns none filemanager create container create container container name creates a new container inside azure storage parameters container name str the name for the container to be created default value tlc data demo returns none upload file upload file container name file name uploads the file to azure storage as a blob blobs as they are referred to in azure documentation are azure specific objects that can hold text or binary data including images documents etc parameters container name str the name of a container created prior to uploading the file default value tlc data demo file name str the name for the file blob this is the name that the file is going to be stored with in your storage account returns none database create container get clients authenticates the service principal to get the clients required to create server and database parameters none returns resource client resourcemanagementclient sql client sqlmanagementclient create resource group create resource group group name region creates a new resource group parameters group name str the name for the new resource group that is to be created region str region to which the new resource group is going to be assigned to to list all the available regions in the accessible format use az account list locations o table when using a terminal with azure cli installed administrator login str the username for logging into the server as an administrator administrator login password str the password for logging into the server as an administrator returns none create server create server creates a new server if you intend to use the methods in isolation be aware that a server must be created prior to creating a database parameters server name str the name for the server group name str the name for the new resource group which has been created prior to creating a server region str region to which the new server is going to be assigned to to list all the available regions in the accessible format use az account list locations o table when using a terminal with azure cli installed administrator login str the username for logging into the server as an administrator administrator login password str the password for logging into the server as an administrator returns none create database create database creates a new sql database parameters group name str the name for the new resource group that the database to be created will belong to server name str the name of the server that will host the database to be created the server has to be created prior to creating a database database name str the name for the database to be created region str region to which the new database is going to be assigned to to list all the available regions in the accessible format use az account list locations o table when using a terminal with azure cli installed collation str type of collation to be used collations determine sorting rules as well as case accent sensitivity for the data which means that the results of the exactly same query might differ when it is on databases with different collations for the types of collations available please refer to the official docs https docs microsoft com en us sql relational databases collations collation and unicode support view sql server ver15 pricing tier str the pricing tier for the database to be created pricing tier determines fixed amount of compute resource that is to be allocated to the database for a fixed price billed hourly returns none whitelist ip whitelist ip add the given ip adress to the list of ip adressess that have access to the database while the indended use case is adding a single ip address it is originally intended to whitelist a range of ip adresses this is useful for cases when ip adress change as it would still fall inside the range of the whitelisted ip addresses parameters rule name str the name for the firewall rule to be created group name str the name for the new resource group that the server and database both preferrably belongs to server name str the name of the server that the access is to be granted to ip address str the ip address to grant the access to the database returns none encrypt database encrypt database encrypt the database parameters server name str the name of the server that hosts the database to be encrypted database name str the name of the database that is to be encrypted login username str the login username for the database which was set when creating the server to host the database login password str the password for the database which was set when creating the server to host the database encryption password str the password used for the encryption of the database driver int odbc driver version the default version is odbc driver 17 for sql server returns none create credentials create credentials create the credentials which will be used to load the csv files to the database from the storage account parameters credential name str the name for the credential to be used to load the resources to the database from the storage account server name str the name of the server hosting the database database name str the name of the database that will be used to store the resources loaded from the storage account login username str the login username for the database which was set when creating the server to host the database login password str the password for the database which was set when creating the server to host the database sas token str shared access signature sas which is required to be generated using azure platform prior to executing the function driver int odbc driver version the default version is odbc driver 17 for sql server returns none create external data source create external data source creates an external data source parameters datasource name str custom name for the external datasource which is to be used to upload data to the database credential name str the name for the credential which will be used to create the external data source must be created prior to creating the external data source location str the name of the storage which is required to be created prior to executing the function container name str the name of the container which is to be established as an external data source server name str the name of the server hosting the database database name str the name of the database that will be used to store the resources loaded from the storage account login username str the login username for the database which was set when creating the server to host the database login password str the password for the database which was set when creating the server to host the database driver int odbc driver version the default version is odbc driver 17 for sql server returns none create table create table creates a new table note that the schema provided is tailored specifically for nyc taxi and limousine commission data parameters server name str the name of the server that hosts the database database name str the name of the database where the table is going to be created login username str the login username for the database which was set when creating the server to host the database login password str the password for the database which was set when creating the server to host the database driver int odbc driver version the default version is odbc driver 17 for sql server table name str the name for the table to be created returns none load csv to db load csv to db loads the csv files taken from the storage and inserts the data to the table which is to be created prior to loading the data parameters server name str the name of the server that hosts the database database name str the name of the database with the table login username str the login username for the database which was set when creating the server to host the database login password str the password for the database which was set when creating the server to host the database driver int odbc driver version the default version is odbc driver 17 for sql server table name str the name for the table where the data is going to inserted file name str the name of the blob inside the storage which is where the data is going to be taken from returns none technologies adal 1 2 7 azure common 1 1 4 azure core 1 21 1 azure identity 1 7 1 azure mgmt core 1 3 0 azure mgmt nspkg 3 0 2 azure mgmt resource 0 31 0 azure mgmt sql 0 2 0 azure mgmt storage 19 0 0 azure nspkg 3 0 2 azure storage blob 12 9 0 certifi 2021 10 8 cffi 1 15 0 charset normalizer 2 0 10 colorama 0 4 4 cryptography 36 0 1 humanize 3 13 1 idna 3 3 isodate 0 6 1 msal 1 16 0 msal extensions 0 3 1 msrest 0 6 21 msrestazure 0 6 4 oauthlib 3 1 1 portalocker 2 3 2 pycparser 2 21 pyjwt 2 3 0 pyodbc 4 0 32 python dateutil 2 8 2 python dotenv 0 19 2 pywin32 303 requests 2 27 1 requests oauthlib 1 3 0 six 1 16 0 tqdm 4 62 3 urllib3 1 26 8 the list of dependencies is to be found at requirements txt https github com virbickt aruodas rent price predictions blob main requirements txt license the project is licenced under mit license https github com virbickt tlc data pipeline blob main license md contact tvirbickas gmail com mailto tvirbickas gmail com subject tlc data pipeline 20on 20github | azure azure-storage azure-cli tsql python pyodbc | server |
vue-examples | vue examples collection of vue js reference examples for teaching and learning vue these examples assume no experience of front end by the reader i myself had more experience as a backend engineer and found most front end examples required too much familiarity of html javascript and the front end stack i journaled these examples to help myself and others the early examples 1 15 are intentionally simple while the remainder start using vue cli and nuxt which are more sophisticated patterns they generate complex starting projects that it is helpful to build up to vue cli is what an experienced front end developer may use if they are unfamiliar with vue and nuxt would be used by an intermediate vue front end engineer as it provides more benefits as well as more complexity introduction these examples can be accessed by opening the index html file within your browser the related javascript and css files are referenced by this file when appropriate this is a good way to test javascript and vue snippets found on the web 1 hello world https github com peterlamar vue workshop tree master 1 helloworld 1 instance https github com peterlamar vue workshop tree master 2 instance 1 vbind https github com peterlamar vue workshop tree master 3 vbind 1 computed https github com peterlamar vue workshop tree master 4 computed 1 watcher https github com peterlamar vue workshop tree master 5 watcher 1 class https github com peterlamar vue workshop tree master 6 class 1 events https github com peterlamar vue workshop tree master 7 events 1 vmodel https github com peterlamar vue workshop tree master 8 vmodel 1 component https github com peterlamar vue workshop tree master 9 component 1 componentmessage https github com peterlamar vue workshop tree master 10 componentmessage 1 componentdynamic https github com peterlamar vue workshop tree master 11 componentdynamic 1 async https github com peterlamar vue workshop tree master 12 async charts and graphs these are interesting if you wish to jumpstart into data visualization 13 chart https github com peterlamar vue workshop tree master 13 chart 14 vuechart https github com peterlamar vue workshop tree master 15 vuechart 15 vuebars https github com peterlamar vue workshop tree master 15 vuebars vue cli these examples start with the vue cli https cli vuejs org and represent the beginnings of the single page app pattern this pattern differs from the index html file in that javascript files are separated out into vue files which is easier to maintain in larger projects 16 localproxy https github com peterlamar vue workshop tree master 16 localproxy 17 aggrid https github com peterlamar vue workshop tree master 17 aggrid 18 tailwind https github com peterlamar vue workshop tree master 18 tailwind 19 svgdots https github com peterlamar vue workshop tree master 19 svgdots 20 d3connectdots https github com peterlamar vue workshop tree master 20 d3connectdots 21 firebasechat https github com peterlamar vue workshop tree master 21 firebase chat nuxt the nuxt https nuxtjs org project is an attempt to improve on the vue cli by providing some common settings as defaults in the generated project 22 hellonuxt https github com peterlamar vue workshop tree master 22 hellonuxt 23 todo app https github com peterlamar vue workshop tree master 23 todo app contributing if any of this is helpful to you or you wish to improve upon these examples pull requests are absolutely welcome references 1 mozilla dom https developer mozilla org en us docs web api document object model 2 vue tutorial https vuejs org v2 guide installation html 3 babel javascript 2015 https babeljs io docs en learn | vue vuejs2 vue-cli beginner | front_end |
Blockchain-Translations | h1 blockchain translations h1 p b do not edit auto these are generated by google translate and changes will be overwritten p p any help with transactions would be greatly appreciated p p if you find a phrase which does not make sense in one of the auto files add the correct entry to the specific language json file p | blockchain |
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wikilon | wikilon wikilon is a wiki insipired software platform and development environment for awelon project and the awelon programming language awelon programming language docs awelonlang md has a simplistic forth like syntax a simple semantics and a purely functional evaluation model based on confluent rewriting of concatenative combinators this simple syntax can be leveraged for projectional editing and views may be preserved over evaluation awelon computations cannot reference external data instead awelon codebases may serve as simple databases documents filesystems spreadsheets depending on how they re used an updated awelon explores non conventional application models docs applicationmodel md wikilon presents awelon language through a web service with wiki inspirations installation wikilon is implemented using f on clr the code generation and jit capabilities of clr or jvm are convenient for implementing a lightweight compiler for user created code codedom and support for tail calls have me favoring clr dependencies lmdb http www lmdb tech doc sudo apt get install liblmdb dev net core https www microsoft com net core linuxubuntu tools instructions on linked website assuming the dependencies and a good internet connection you may use dotnet restore to download required net packages use dotnet run help to view command line options aside i m favoring net core over mono largely for the streamlined dotnet cli tooling this does incur a few unfortunate opportunity costs such as websharper isn t yet available for net core components primary project components src wikilon the web application and ui definitions src awelon awelon language and runtime model src stowage data non relational database model src stowage lmdb based implementation of database src data bytestring bytestring values builders parsers this toplevel directory also has app fsproj referencing src main fs which simply processes command line configuration options and runs a web server configuration configuration of wikilon is performed online generally through a browser initial configuration requires the admin flag to provide an ephemeral password for a root administrative account but the admin may assign administrative authorities to other accounts | front_end |
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MERN-Quick-Start-Guide | react and node web development cookbook react and node web development cookbook published by packt 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 9781787281080 https packt link free ebook 9781787281080 a p | front_end |
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sql_salaries | sql fun with salaries using provided csv files from an old database the database is rebuilt and analyzed by 1 data modeling designing an erd 2 data engineering create table schema and import csv files 3 data analysis create various queries in sql to probe whether the database is real or fabricated and visualize using pandas files data https github com l0per sql challenge tree master data database tables were provided as csv files erd schema sql database schema created from https app quickdatabasediagrams com query sql various queries probing the database db vis ipynb visualizing database using pandas data modeling and engineering the csv files were successfully imported into tables from the following erd erd https github com l0per sql challenge blob master images erd png raw true data analysis salary distributions plotting the distribution of salaries by each position title reveals an excess of salaries around 40000 regardless of position title which suggests a fabricated dataset salary distributions https github com l0per sql challenge blob master images dist title salaries png raw true average salaries average salary does not increase with seniority of title suggesting a fabricated dataset salary barplot https github com l0per sql challenge blob master images bar title salaries png raw true april fools it is indeed fabricated aprilfools https github com l0per sql challenge blob master images aprilfools png raw true | server |
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Neat-EO.pink | a href https twitter com neat eo img src https img shields io badge follow neat eo ff69b4 svg a a href https gitter im neat eo pink community img src https img shields io gitter room neat eo pink community svg color ff69b4 style popout a h1 align center neat eo pink h1 h2 align center computer vision framework for geospatial imagery at scale h2 p align center img src https pbs twimg com media dpjonykwwaanppr jpg alt neat eo pink buildings segmentation from imagery p purposes dataset quality analysis change detection highlighter features extraction and completion main features provides several command line tools you can combine together to build your own workflow follows geospatial standards to ease interoperability and data preparation build in cutting edge computer vision model data augmentation and loss implementations and allows to replace by your owns support either rgb and multibands imagery and allows data fusion web ui tools to easily display hilight or select results and allow to use your own templates high performances eeasily extensible by design img alt draw me neat eo pink src https raw githubusercontent com datapink neat eo pink master docs img readme draw me neat eo png documentation tutorial a href https github com datapink neat eo pink blob master docs 101 md learn to use neat eo pink in a couple of hours a config file a href https github com datapink neat eo pink blob master docs config md neat eo pink configuration file a tools a href https github com datapink neat eo pink blob master docs tools md neo cover neo cover a generate a tiles covering in csv format x y z a href https github com datapink neat eo pink blob master docs tools md neo download neo download a downloads tiles from a remote server xyz wms or tms a href https github com datapink neat eo pink blob master docs tools md neo extract neo extract a extracts geojson features from openstreetmap pbf a href https github com datapink neat eo pink blob master docs tools md neo rasterize neo rasterize a rasterize vector features geojson or postgis to raster tiles a href https github com datapink neat eo pink blob master docs tools md neo subset neo subset a filter images in a slippy map dir using a csv tiles cover a href https github com datapink neat eo pink blob master docs tools md neo tile neo tile a tile raster coverage a href https github com datapink neat eo pink blob master docs tools md neo dataset neo dataset a perform checks and analyses on training dataset a href https github com datapink neat eo pink blob master docs tools md neo train neo train a trains a model on a dataset a href https github com datapink neat eo pink blob master docs tools md neo export neo export a export a model to onnx or torch jit a href https github com datapink neat eo pink blob master docs tools md neo predict neo predict a predict masks from given inputs and an already trained model a href https github com datapink neat eo pink blob master docs tools md neo compare neo compare a compute composite images and or metrics to compare several xyz dirs a href https github com datapink neat eo pink blob master docs tools md neo vectorize neo vectorize a extract simplified geojson features from segmentation masks a href https github com datapink neat eo pink blob master docs tools md neo info neo info a print neat eo pink version informations presentations slides a href http www datapink com presentations 2020 fosdem pdf fosdem 2020 a installs with pip pip3 install neat eo pink with ubuntu 19 10 from scratch neat eo pink mandatory sudo sh c apt update apt install y build essential python3 pip pip3 install neat eo pink export path path local bin nvidia gpu drivers mandatory wget http us download nvidia com xfree86 linux x86 64 435 21 nvidia linux x86 64 435 21 run sudo sh nvidia linux x86 64 435 21 run a q ui none extra cli tools used in tutorials sudo apt install y gdal bin osmium tool http server for webui rendering sudo apt install y apache2 sudo ln s var www html neo notas requires python 3 6 or 3 7 gpu with vram 8go is mandatory to test neat eo pink install launch in a new terminal neo info if needed to remove pre existing nouveau driver sudo sh c echo blacklist nouveau etc modprobe d blacklist nvidia nouveau conf update initramfs u reboot architecture neat eo pink use cherry picked open source libs among deep learning computer vision and gis stacks img alt stacks src https raw githubusercontent com datapink neat eo pink master docs img readme stacks png geospatial opendatasets a href https github com chrieke awesome satellite imagery datasets christoph rieke s awesome satellite imagery datasets a a href https zhangbin0917 github io 2018 06 12 e9 81 a5 e6 84 9f e6 95 b0 e6 8d ae e9 9b 86 zhang bin earth observation opendataset blog a bibliography a href https arxiv org abs 1912 01703 pytorch an imperative style high performance deep learning library a a href https arxiv org abs 1505 04597 u net convolutional networks for biomedical image segmentation a a href https arxiv org abs 1512 03385 deep residual learning for image recognition a a href https arxiv org abs 1806 00844 ternausnetv2 fully convolutional network for instance segmentation a a href https arxiv org abs 1705 08790 the lov sz softmax loss a tractable surrogate for the optimization of the iou measure in neural networks a a href https arxiv org abs 1809 06839 albumentations fast and flexible image augmentations a contributions and services pull requests are welcome feel free to send code don t hesitate either to initiate a prior discussion via a href https gitter im datapink neat eo gitter a or ticket on any implementation question and give also a look at a href https github com datapink robosat pink blob master docs makefile md makefile rules a if you want to collaborate through code production and maintenance on a long term basis please get in touch co edition with an ad hoc governance can be considered if you want a new feature but don t want to implement it a href http datapink com datapink a provide core dev services expertise assistance and training on neat eo pink are also provided by a href http datapink com datapink a and if you want to support the whole project because it means for your own business funding is also welcome requests for funding we ve already identified several new features and research papers able to improve again neat eo pink your funding would make a difference to implement them on a coming release increase again accuracy on low resolution imagery even with few labels aka weakly supervised topology handling instance segmentation improve again performances add support for time series imagery streetview imagery multihosts scaling vectors post treatments authors olivier courtin https github com ocourtin daniel j hofmann https github com daniel j h citing manual title neat eo pink computer vision framework for geospatial imagery author olivier courtin daniel j hofmann organization datapink year 2020 url http neat eo pink | ai |
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deceptiveidn | deceptive idn phishers https www bluecoat com security blog 2014 05 22 bad guys using internationalized domain names idns are still using internationalized domain names https en wikipedia org wiki internationalized domain name to trick users this project uses computer vision to automatically check if idns have a deceptive reading usage to use the tool invoke it with python3 bash python3 deceptiveidn py xn e1awd7f com idn com xn e1awd7f com deceptive idn possible readings cpic com 207 235 47 22 epic com 199 204 56 88 dependencies this script requires python3 pillow the python3 imaging library and tesseract ocr https github com tesseract ocr tesseract brew install python3 tesseract pip3 install pillow license this project copyright ryan stortz withzombies and is available under the apache 2 0 license | security computer-vision idn python3 | ai |
iot-security-module-preview | iot security module azure rtos preview azure security center for iot security module provides a comprehensive security solution for azure rtos devices azure rtos is shipped with a built in security module that covers common threats on real time operating system devices azure security center for iot security module with azure rtos support offers the following features detection of malicious network activities every device inbound and outbound network activity is monitored supported protocols tcp udp icmp on ipv4 and ipv6 azure security center for iot inspects each of these network activities against the microsoft threat intelligence feed the feed gets updated in real time with millions of unique threat indicators collected worldwide device behavior baselining based on custom alerts allows for clustering of devices into security groups and defining the expected behavior of each group as iot devices are typically designed to operate in well defined and limited scenarios it is easy to create a baseline that defines their expected behavior using a set of parameters any deviation from the baseline triggers an alert improve the security hygiene of the device by leveraging the recommended infrastructure azure security center for iot provides gain knowledge and insights about issues in your environment that impact and damage the security posture of your devices poor iot device security posture can allow potential attacks to succeed if left unchanged as security is always measured by the weakest link within any organization monitoring capabilities img asc for iot monitoring capabilities png azure security center for iot security module for azure rtos is provided as a free download for your devices the azure security center for iot cloud service is available with a 30 day trial per azure subscription download the azure security center for iot security module for azure rtos today to get started for more information about azure security center for iot please refer to this link https docs microsoft com en us azure asc for iot getting started with iot security module guides the following board specific guides will get you started with azure rtos and iot security module getting started with the nxp mimxrt1060 evk nxp mimxrt1060 evk reporting security vulnerabilities if you believe you have found a security vulnerability in any microsoft owned repository that meets microsoft s microsoft s definition of a security vulnerability please report it to the microsoft security response center security md contribution feedback and issues if you encounter any bugs have suggestions for new features or if you would like to become an active contributor to this project please follow the instructions provided in the contribution guideline for the corresponding repo for general support please post a question to stack overflow http stackoverflow com questions tagged azure rtos using the azure rtos tags | os |
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webdev-resources | amazing web development resources and more awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome the ultimate repository full of resources to learn about web development design git and more this repository started as an extension guide for web development resources that kode with klossy web dev students can refer to as a way to further expand their webdev abilities after the camp however i ve expanded this doc for anyone willing to learn webdev here not just those in kode with klossy the list will be updated throughout time and feel free to look around or make a pull request here happy learning contents learning web development webdevelopment designs designs site templates templates prototyping prototyping github github ides ides blogs blogs challenges challenges additional resources additionalresources memo contributing memo contributing webdevelopment best sites for learning anything web development related w3schools https www w3schools com freecodecamp https www freecodecamp org codecademy https www codecademy com catalog subject web development educative https www educative io learn django tutorial https tutorial djangogirls org en reactjs https reactjs org tutorial tutorial html setting up react https www codecademy com articles how to create a react app more react https reactjs org docs create a new react app html practicing react js alongside s more resources https github com hack4impact uiuc react exercise mini web dev bootcamp https www notion so curriculum ca431096426b4fd1968ac49121ff2fdb mozilla https developer mozilla org en us docs learn learnjs https github com codenerve learnjavascript techntonica curriculum https github com techtonica curriculum harvard cs50 web programming https cs50 harvard edu web 2020 designs google fonts https fonts google com html colorcode https htmlcolorcodes com font awesome https fontawesome com v4 7 0 icons undraw https undraw co emojipedia https emojipedia org dribble https dribbble com fbclid iwar3c yubr wljhwviqcarbem7ug2zyd02 i8u4zy2ousuwkttfiivoxqhxa css animated gradient background https uigradients com sandtoblue bootstrap https getbootstrap com templates html5up https html5up net json resume https jsonresume org getting started urspace https urspace io customized templates terminal like portfolio https github com codenerve codenerve github io some more templates https html com resources free html templates w3 schools bootstrap theme https www w3schools com bootstrap4 bootstrap templates asp bootstrap is a front end framework that allows your site to be more responsive across all devices prototyping proto io https proto io keep in mind you only have a 14 day free trial figma https www figma com blog bubble io https bubble io easy and free tool to create mockups check this medium blog out https medium com denisz design the 9 best go to prototyping tools for designers in 2019 296b341a51a2 github github guides https guides github com activities hello world introduction to github https lab github com githubtraining introduction to github more github courses https lab github com practicing how to use git https github com benthayer git gud gitbook https www gitbook com importance of having a good read me https github com noffle art of readme fbclid iwar3sfpa8kv71i2ycesls4fcqixvtzbb0eijgeaidmugs1rfz0rv1yn0ksoy very useful to include for your final project why you need a clear readme if you want a coding job https www reddit com r cscareerquestions comments h17blk always write a clear readme if you want to find a github student developer pack https education github com pack showcase portfolio through github pages https github dev fork this repo https github com dipakkr a to z resources for students ides repl it https repl it code pen https codepen io glitch https glitch com atom https atom io vscode https code visualstudio com blogs blogs to check out freecodecamp https www freecodecamp org news codepath https blog codepath org tech together https medium com techtogether better programming https medium com better programming challenges doing challenges are a great way to expand your skill set and to create projects here are some challenges to look at below 7 days 7 websites https www freecodecamp org news the 7days7websites coding challenge 100daysofcode https www 100daysofcode com contributing to open source projects https github com freecodecamp how to contribute to open source list of hackathons https mlh io seasons na 2020 events js challenges https jsbeginners com javascript projects for beginners list additional resources ways to get involved with women in tech https github com nishapant women in tech resources impactful https weareimpactful org fbclid iwar1w5zp9xhk1jccj0dvcttmctomozt7aaqaf6x3nzjvbu76muwn2orom7a4 home adding google analytics to website https www pair com support kb how to google analytics to an html website how to make a chrome extension https developer chrome com extensions getstarted contributing to open source https opensource guide how to contribute missing semester of your cs education https missing csail mit edu developer roadmaps https roadmap sh guides github student pack https education github com pack memo contributing see contributing md contributing md | webdevelopment github ide challenges reactjs contributions-welcome | front_end |
Self-Driving-Car-on-Indian-Roads | self driving car on indian roads computer vision guided deep learning network amp machine learning techniques to build fully functional autonomous vehicles prediction output gif more details of the project here https medium com anandai self driving car on indian roads 4e305cb04198 how to use data source generate training data first of all record a driving video or obtain driving video from any source i have contributed the indian driving dataset along with parameters in kaggle you can download it from here and unzip to project folder root https www kaggle com ananduthaman self driving car on indian roads if you are recording your own driving video then generate the training parameters i e steering angle acceleration brake and gear using the console interface this is a hack to generate training data without tampering with the obd interface of the car which is a lot harder to invoke console interface python create train data py the interface program writes the steering angle acceleration brake and gear values to an output file for every frame in the video if the video is recorded at 30 fps then the parameter values have to be written every 1000 30 33 ms in a loop to get this accuracy is a bit challenging as it depends on the execution time of the while loop that writes the output you may have to write every 30 33 ms to properly sync with the video recorded at 30 fps consider the write frequency as a hyper parameter since it depends on the machine speed you can tune write frequency by doing 1 minute driving simulation and check how far off it is from 60x30 1800 frames data pre processing the self recorded driving videos required some pre processing steps before it could be fed to the network following softwares were used in sequence lossless cut to cut out relevant video without loss anytime video converter to resize video to reduce data video to jpg converter to extract frames from video generate optical flow the optical flow of the video need to be generated before training python gen optical flow traindata py opticalflow gif gif training the data three driving control parameters viz steering angle acceleration and brake are trained with different models and are concurrently executed at run time to drive the car the model files would be generated inside save directory python train accel py python train brake py python train steer py the visualization of cnn layers for model interpretability is done in train accel py cnn visualization png the prototyping for models can be done using ipython notebook cnn accel brake ipynb to run the system the models for steering acceleration brake and gear are simultaneously executed in different sessions with corresponding computation graphs to predict all driving control values for each frame python run dataset py to run the system on new track python run dataset test py if you have any query or suggestion you can reach me here https www linkedin com in ananduthaman | autonomous-driving computer-vision deep-learning diy machine-learning self-driving-car autonomous-vehicles cv deeplearning indian indian-road-detection indian-roads opencv | ai |
GRec | grec gnn based third party library recommendation framework for mobile app development usage each pair of randomly generated training set and testing set is stored in sub folder of data simply start python in command line mode then use the following statement one line in the command prompt window to execute grec on one dataset python main py dataset qiaoji 5 30 alg type ngcf regs 1e 5 lr 0 0001 save flag 1 pretrain 0 batch size 4096 epoch 800 verbose 1 then you may receive the results as follows best iter 79 7756 1 recall 0 59502 0 73338 precision 0 58724 0 36238 map 0 84104 0 78629 cov 0 65841 0 75393 f1 0 59070 0 48482 benchmarking time consuming average 8 1111s per epoch for the subfolder name 5 30 means for each mobile app 5 randomly selected tpls were removed to form the testing set and the remaining tpls formed the training set besides this pair of dataset is used for the 30th experiment run for the parameters dataset specifies the corresponding testing set and training set epoch maximum epochs during the training process grec may stop early if it cannot further improve the performance in 5 consecutive epochs environment settup our code has been tested under python 3 6 9 the experiment was conducted via pytorch and thus the following packages are required pytorch 1 3 1 numpy 1 18 1 scipy 1 3 2 sklearn 0 21 3 updated version of each package is acceptable a gpu accelerator nvidia tesla p100 12gb is needed when running the experiments description of folders and files name type description main py file main python file of grec models py file ngcf modules used by grec utility folder tools and essential libraries used by grec training dataset folder training set and testing set one pair in each sub folder only part of the used dataset is provided due to the size limitation of the uploaded file output file a sample of the tpl recommendation results by comparing the tpls in the testing set with the recommended ones the effectiveness of grec can be evaluated please note only part of the results are provided due to the size limitation original dataset folder the original public malib dataset proposed in 9 tse 2020 essential parameter setup for grec all essential parameters of grec can be set via file utility parser py specifically line description 8 setup dataset path only the parent folder data is needed grec can find out each sub folder through parameter dataset in python commands 20 setup the default maximum epoch of each experiment run it will be overwritten if the corresponding parameter is set through command line 25 setup the total number of gnn layers and the size of each layer for example a value 128 128 128 128 means grec has 4 gnn layers in total and each layer has the same layer size of 128 nodes i e the size of each latent factor vector is 128 46 setup how much information will be randomly discarded during the training the number of elements is equal to the size of parameter array in line 25 and 0 1 means 10 of information will be discarded 49 specify the size of each recommendation list an array 5 10 means the first tpl recommendation list has 5 tpls and the second recommendation list has 10 tpls this way we can evaluate grec s performance with different textit nr in one batch others other paramenter can also be found in this file please refer to the comments of each parameter in this file citation it would be very much appreciated if you cite the following papers li b he q chen f xia x li l grundy j and yang y 2021 embedding app library graph for neural third party library recommendation in proceddings of fse 2021 doi 10 1145 3468264 3468552 | front_end |
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computer_vision | computer vision tum master course computer vision https www ldv ei tum de lehre computer vision assignment solutions this repository contains several computer vision basics coded in matlab topics are rgb to grey harris feature detector sobel filter 8point algorithm point correspondences in stereo images ransac algorithm reconstruction reprojection error | computer-vision tum university-course matlab coding | ai |
TextBrewer | english readme md readme zh md p align center br img src pics banner png width 500 br p p align center a href https github com airaria textbrewer blob master license img alt github src https img shields io github license airaria textbrewer svg color blue style flat square a a href https textbrewer readthedocs io img alt documentation src https img shields io website down message offline label documentation up message online url https 3a 2f 2ftextbrewer readthedocs io a a href https pypi org project textbrewer img alt pypi src https img shields io pypi v textbrewer a a href https github com airaria textbrewer releases img alt github release src https img shields io github v release airaria textbrewer include prereleases a p textbrewer is a pytorch based model distillation toolkit for natural language processing it includes various distillation techniques from both nlp and cv field and provides an easy to use distillation framework which allows users to quickly experiment with the state of the art distillation methods to compress the model with a relatively small sacrifice in the performance increasing the inference speed and reducing the memory usage check our paper through acl anthology https www aclweb org anthology 2020 acl demos 2 or arxiv pre print https arxiv org abs 2002 12620 full documentation https textbrewer readthedocs io news dec 17 2021 we have released a model pruning toolkit textpruner check https github com airaria textpruner oct 24 2021 we propose the first pre trained language model that specifically focusing on chinese minority languages check https github com ymcui chinese minority plm jul 8 2021 new examples with transformers 4 the current examples exmaples have been written with old versions of transformers and they may cause some confusions and bugs we rewrite the examples with transformers 4 in jupyter notebooks which are easy to follow and learn the new examples can be found at examples notebook examples examples notebook examples see examples examples for details mar 1 2021 bert emd and custom distiller we added an experiment with bert emd https www aclweb org anthology 2020 emnlp main 242 in the mnli exmaple examples mnli example bert emd allows each intermediate student layer to learn from any intermediate teacher layers adaptively based on optimizing earth mover s distance so there is no need to specify the matching scheme we have written a new emddistiller examples mnli example distiller emd py to perform bert emd it demonstrates how to write a custom distiller updated mnli example we removed the pretrained pytorch bert and used transformers library instead in all the mnli examples details summary click here to see old news summary nov 11 2020 updated to 0 2 1 more flexible distillation supports feeding different batches to the student and teacher it means the batches for the student and teacher no longer need to be the same it can be used for distilling models with different vocabularies e g from roberta to bert faster distillation users now can pre compute and cache the teacher outputs then feed the cache to the distiller to save teacher s forward pass time see feed different batches to student and teacher feed cached values https textbrewer readthedocs io en latest concepts html feed different batches to student and teacher feed cached values for details of the above features multitaskdistiller now supports intermediate feature matching loss tensorboard now records more detailed losses kd loss hard label loss matching losses see details in releases https github com airaria textbrewer releases tag v0 2 1 august 27 2020 we are happy to announce that our model is on top of glue benchmark check leaderboard https gluebenchmark com leaderboard aug 24 2020 updated to 0 2 0 1 fixed bugs in multitaskdistiller and training loops jul 29 2020 updated to 0 2 0 added the support for distributed data parallel training with distributeddataparallel trainingconfig now accpects the local rank argument see the documentation of trainingconfig for detail added an example of distillation on the chinese ner task to demonstrate distributed data parallel training see examples msra ner example examples msra ner example jul 14 2020 updated to 0 1 10 now supports mixed precision training with apex just set fp16 to true in trainingconfig see the documentation of trainingconfig for detail added data parallel option in trainingconfig to enable data parallel training and mixed precision training work together apr 26 2020 added chinese ner task msra ner results added results for distilling to t12 nano model which has a similar strcuture to electra small updated some results of conll 2003 cmrc 2018 and drcd apr 22 2020 updated to 0 1 9 added cache option which speeds up distillation fixed some bugs see details in releases https github com airaria textbrewer releases tag v0 1 9 added experimential results for distilling electra base to electra small on chinese tasks textbrewer has been accepted by acl 2020 http acl2020 org as a demo paper please use our new bib entry citation mar 17 2020 added conll 2003 english ner distillation example see examples conll2003 example examples conll2003 example mar 11 2020 updated to 0 1 8 improvements on trainingconfig and train method see details in releases https github com airaria textbrewer releases tag v0 1 8 mar 2 2020 initial public version 0 1 7 has been released see details in releases https github com airaria textbrewer releases tag v0 1 7 details table of contents toc section contents introduction introduction introduction to textbrewer installation installation how to install workflow workflow two stages of textbrewer workflow quickstart quickstart example distilling bert base to a 3 layer bert experiments experiments distillation experiments on typical english and chinese datasets core concepts core concepts brief explanations of the core concepts in textbrewer faq faq frequently asked questions known issues known issues known issues citation citation citation to textbrewer follow us follow us toc introduction pics arch png textbrewer is designed for the knowledge distillation of nlp models it provides various distillation methods and offers a distillation framework for quickly setting up experiments the main features of textbrewer are wide support it supports various model architectures especially transformer based models flexibility design your own distillation scheme by combining different techniques it also supports user defined loss functions modules etc easy to use users don t need to modify the model architectures built for nlp it is suitable for a wide variety of nlp tasks text classification machine reading comprehension sequence labeling textbrewer currently is shipped with the following distillation techniques mixed soft label and hard label training dynamic loss weight adjustment and temperature adjustment various distillation loss functions hidden states mse attention matrix based loss neuron selectivity transfer freely adding intermediate features matching losses multi teacher distillation textbrewer includes 1 distillers the cores of distillation different distillers perform different distillation modes there are generaldistiller multiteacherdistiller basictrainer etc 2 configurations and presets configuration classes for training and distillation and predefined distillation loss functions and strategies 3 utilities auxiliary tools such as model parameters analysis to start distillation users need to provide 1 the models the trained teacher model and the un trained student model 2 datasets and experiment configurations textbrewer has achieved impressive results on several typical nlp tasks see experiments experiments see full documentation https textbrewer readthedocs io for detailed usages architecture pics arch png installation requirements python 3 6 pytorch 1 1 0 tensorboardx or tensorboard numpy tqdm transformers 2 0 optional used by some examples apex 0 1 0 optional mixed precision training install from pypi shell pip install textbrewer install from the github source shell git clone https github com airaria textbrewer git pip install textbrewer workflow pics distillation workflow en png pics distillation workflow2 png stage 1 preparation 1 train the teacher model 2 define and initialize the student model 3 construct a dataloader an optimizer and a learning rate scheduler stage 2 distillation with textbrewer 1 construct a traningconfig and a distillationconfig initialize a distiller 2 define an adaptor and a callback the adaptor is used for adaptation of model inputs and outputs the callback is called by the distiller during training 3 call the train method of the distiller quickstart here we show the usage of textbrewer by distilling bert base to a 3 layer bert before distillation we assume users have provided a trained teacher model teacher model bert base and a to be trained student model student model 3 layer bert a dataloader of the dataset an optimizer and a learning rate builder or class scheduler class and its args dict scheduler dict distill with textbrewer python import textbrewer from textbrewer import generaldistiller from textbrewer import trainingconfig distillationconfig show the statistics of model parameters print nteacher model s parametrers result textbrewer utils display parameters teacher model max level 3 print result print student model s parametrers result textbrewer utils display parameters student model max level 3 print result define an adaptor for interpreting the model inputs and outputs def simple adaptor batch model outputs the second and third elements of model outputs are the logits and hidden states return logits model outputs 1 hidden model outputs 2 training configuration train config trainingconfig distillation configuration matching different layers of the student and the teacher distill config distillationconfig intermediate matches layer t 0 layer s 0 feature hidden loss hidden mse weight 1 layer t 8 layer s 2 feature hidden loss hidden mse weight 1 build distiller distiller generaldistiller train config train config distill config distill config model t teacher model model s student model adaptor t simple adaptor adaptor s simple adaptor start with distiller distiller train optimizer dataloader num epochs 1 scheduler class scheduler class scheduler args scheduler args callback none examples notebook examples with transformers 4 examples notebook examples sst2 ipynb examples notebook examples sst2 ipynb english training and distilling bert on sst 2 an english sentence classification task examples notebook examples msra ner ipynb examples notebook examples msra ner ipynb chinese training and distilling bert on msra ner a chinese sequence labeling task examples notebook examples sqaudv1 1 ipynb examples notebook examples sqaudv1 1 ipynb english training and distilling bert on squad 1 1 an english mrc task examples random token example examples random token example a simple runnable toy example which demonstrates the usage of textbrewer this example performs distillation on the text classification task with random tokens as inputs examples cmrc2018 example examples cmrc2018 example chinese distillation on cmrc 2018 a chinese mrc task using drcd as data augmentation examples mnli example examples mnli example english distillation on mnli an english sentence pair classification task this example also shows how to perform multi teacher distillation examples conll2003 example examples conll2003 example english distillation on conll 2003 english ner task which is in form of sequence labeling examples msra ner example examples msra ner example chinese this example distills a chinese electra base model on the msra ner task with distributed data parallel training single node multi gpu experiments we have performed distillation experiments on several typical english and chinese nlp datasets the setups and configurations are listed below models for english tasks the teacher model is bert base cased https github com google research bert for chinese tasks the teacher models are roberta wwm ext https github com ymcui chinese bert wwm and electra base https github com ymcui chinese electra released by the joint laboratory of hit and iflytek research we have tested different student models to compare with public results the student models are built with standard transformer blocks except for bigru which is a single layer bidirectional gru the architectures are listed below note that the number of parameters includes the embedding layer but does not include the output layer of each specific task english models model layers hidden size feed forward size params relative size bert base cased teacher 12 768 3072 108m 100 t6 student 6 768 3072 65m 60 t3 student 3 768 3072 44m 41 t3 small student 3 384 1536 17m 16 t4 tiny student 4 312 1200 14m 13 t12 nano student 12 256 1024 17m 16 bigru student 768 31m 29 chinese models model layers hidden size feed forward size params relative size roberta wwm ext teacher 12 768 3072 102m 100 electra base teacher 12 768 3072 102m 100 t3 student 3 768 3072 38m 37 t3 small student 3 384 1536 14m 14 t4 tiny student 4 312 1200 11m 11 electra small student 12 256 1024 12m 12 t6 architecture is the same as distilbert sup 1 sup https arxiv org abs 1910 01108 bert sub 6 sub pkd sup 2 sup https arxiv org abs 1908 09355 and bert of theseus sup 3 sup https arxiv org abs 2002 02925 t4 tiny architecture is the same as tinybert sup 4 sup https arxiv org abs 1909 10351 t3 architecure is the same as bert sub 3 sub pkd sup 2 sup https arxiv org abs 1908 09355 distillation configurations python distill config distillationconfig temperature 8 intermediate matches matches others arguments take the default values matches are differnt for different models model matches bigru none t6 l6 hidden mse l6 hidden smmd t3 l3 hidden mse l3 hidden smmd t3 small l3n hidden mse l3 hidden smmd t4 tiny l4t hidden mse l4 hidden smmd t12 nano small hidden mse small hidden smmd electra small small hidden mse small hidden smmd the definitions of matches are at examples matches matches py examples matches matches py we use generaldistiller in all the distillation experiments training configurations learning rate is 1e 4 unless otherwise specified we train all the models for 30 60 epochs results on english datasets we experiment on the following typical english datasets dataset task type metrics train dev note mnli https www nyu edu projects bowman multinli text classification m mm acc 393k 20k sentence pair 3 class classification squad 1 1 https rajpurkar github io squad explorer reading comprehension em f1 88k 11k span extraction machine reading comprehension conll 2003 https www clips uantwerpen be conll2003 ner sequence labeling f1 23k 6k named entity recognition we list the public results from distilbert https arxiv org abs 1910 01108 bert pkd https arxiv org abs 1908 09355 bert of theseus https arxiv org abs 2002 02925 tinybert https arxiv org abs 1909 10351 and our results below for comparison public results model public mnli squad conll 2003 distilbert t6 81 6 81 1 78 1 86 2 bert sub 6 sub pkd t6 81 5 81 0 77 1 85 3 bert of theseus t6 82 4 82 1 bert sub 3 sub pkd t3 76 7 76 3 tinybert t4 tiny 82 8 82 9 72 7 82 1 our results model ours mnli squad conll 2003 bert base cased teacher 83 7 84 0 81 5 88 6 91 1 bigru 85 3 t6 83 5 84 0 80 8 88 1 90 7 t3 81 8 82 7 76 4 84 9 87 5 t3 small 81 3 81 7 72 3 81 4 78 6 t4 tiny 82 0 82 6 75 2 84 0 89 1 t12 nano 83 2 83 9 79 0 86 6 89 6 note 1 the equivalent model structures of public models are shown in the brackets after their names 2 when distilling to t4 tiny newsqa is used for data augmentation on squad and hotpotqa is used for data augmentation on conll 2003 3 when distilling to t12 nano hotpotqa is used for data augmentation on conll 2003 results on chinese datasets we experiment on the following typical chinese datasets dataset task type metrics train dev note xnli https github com google research bert blob master multilingual md text classification acc 393k 2 5k chinese translation version of mnli lcqmc http icrc hitsz edu cn info 1037 1146 htm text classification acc 239k 8 8k sentence pair matching binary classification cmrc 2018 https github com ymcui cmrc2018 reading comprehension em f1 10k 3 4k span extraction machine reading comprehension drcd https github com drcknowledgeteam drcd reading comprehension em f1 27k 3 5k span extraction machine reading comprehension traditional chinese msra ner https faculty washington edu levow papers sighan06 pdf sequence labeling f1 45k 3 4k test chinese named entity recognition the results are listed below model xnli lcqmc cmrc 2018 drcd roberta wwm ext teacher 79 9 89 4 68 8 86 4 86 5 92 5 t3 78 4 89 0 66 4 84 2 78 2 86 4 t3 small 76 0 88 1 58 0 79 3 75 8 84 8 t4 tiny 76 2 88 4 61 8 81 8 77 3 86 1 model xnli lcqmc cmrc 2018 drcd msra ner electra base teacher 77 8 89 8 65 6 84 7 86 9 92 3 95 14 electra small 77 7 89 3 66 5 84 9 85 5 91 3 93 48 note 1 learning rate decay is not used in distillation on cmrc 2018 and drcd 2 cmrc 2018 and drcd take each other as the augmentation dataset in the distillation 3 the settings of training electra base teacher model can be found at chinese electra https github com ymcui chinese electra 4 electra small student model is initialized with the pretrained weights https github com ymcui chinese electra core concepts configurations trainingconfig configuration related to general deep learning model training distillationconfig configuration related to distillation methods distillers distillers are in charge of conducting the actual experiments the following distillers are available basicdistiller single teacher single task distillation provides basic distillation strategies generaldistiller recommended single teacher single task distillation supports intermediate features matching recommended most of the time multiteacherdistiller multi teacher distillation which distills multiple teacher models of the same task into a single student model this class doesn t support intermediate features matching multitaskdistiller multi task distillation which distills multiple teacher models of different tasks into a single student basictrainer supervised training a single model on a labeled dataset not for distillation it can be used to train a teacher model user defined functions in textbrewer there are two functions that should be implemented by users callback and adaptor callback at each checkpoint after saving the student model the callback function will be called by the distiller a callback can be used to evaluate the performance of the student model at each checkpoint adaptor it converts the model inputs and outputs to the specified format so that they could be recognized by the distiller and distillation losses can be computed at each training step batch and model outputs will be passed to the adaptor the adaptor re organizes the data and returns a dictionary for more details see the explanations in full documentation https textbrewer readthedocs io faq q how to initialize the student model a the student model could be randomly initialized i e with no prior knowledge or be initialized by pre trained weights for example when distilling a bert base model to a 3 layer bert you could initialize the student model with rbt3 https github com ymcui chinese bert wwm for chinese tasks or the first three layers of bert for english tasks to avoid cold start problem we recommend that users use pre trained student models whenever possible to fully take advantage of large scale pre training q how to set training hyperparameters for the distillation experiments a knowledge distillation usually requires more training epochs and larger learning rate than training on the labeled dataset for example training squad on bert base usually takes 3 epochs with lr 3e 5 however distillation takes 30 50 epochs with lr 1e 4 the conclusions are based on our experiments and you are advised to try on your own data q my teacher model and student model take different inputs they do not share vocabularies so how can i distill a you need to feed different batches to the teacher and the student see the section feed different batches to student and teacher feed cached values https textbrewer readthedocs io en latest concepts html feed different batches to student and teacher feed cached values in the full documentation q i have stored the logits from my teacher model can i use them in the distillation to save the forward pass time a yes see the section feed different batches to student and teacher feed cached values https textbrewer readthedocs io en latest concepts html feed different batches to student and teacher feed cached values in the full documentation known issues multi gpu training support is only available through dataparallel currently multi label classification is not supported citation if you find textbrewer is helpful please cite our paper https www aclweb org anthology 2020 acl demos 2 bibtex inproceedings textbrewer acl2020 demo title t ext b rewer a n o pen s ource k nowledge d istillation t oolkit for n atural l anguage p rocessing author yang ziqing and cui yiming and chen zhipeng and che wanxiang and liu ting and wang shijin and hu guoping booktitle proceedings of the 58th annual meeting of the association for computational linguistics system demonstrations year 2020 publisher association for computational linguistics url https www aclweb org anthology 2020 acl demos 2 pages 9 16 follow us follow our official wechat account to keep updated with our latest technologies pics hfl qrcode jpg | bert pytorch nlp knowledge distillation | ai |
openup2018_01-06 | openup web development practice 1 write a square constructor function allows create a new instance which can set edge and calculate area 2 write a javascript function that reverse a number example x 356712 expected output 217653 3 write a javascript function that returns a passed string with letters in alphabetical order example string bcda expected output abcd | front_end |
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tpf-foundations-ITBA | trabajo pr ctico final foundations itba cloud data engineering bienvenido al tp final de la secci n foundations del m dulo 1 de la diplomatura en cloud data engineering del itba en este trabajo pr ctico vas a poner en pr ctica los conocimientos adquiridos en 1 bases de datos relacionales postgresql espec ficamente 2 bash y linux commandline 3 python 3 7 4 docker para realizar este tp vamos a utlizar la plataforma github classrooms donde cada alumno tendr acceso a un repositorio de git privado hosteado en la plataforma github en cada repositorio en la secci n de issues https guides github com features issues tab a la derecha de code en las tabs de navegaci n en la parte superior de la pantalla podr s ver que hay creado un issue por cada ejercicio el objetivo es resolver ejercicio issue creando un branch y un pull request asociado debido a que cada ejercico utiliza el avance realizado en el issue anterior cada nuevo branch debe partir del branch del ejercicio anterior para poder realizar llevar a cabo esto puede realizarlo desde la web de github pero recomendamos hacerlo con la aplicaci n de l nea de comando de git o con la aplicaci n de github desktop https desktop github com interfaz visual o github cli https cli github com interfaz de l nea de comando la idea de utilizar github es replicar el ambiente de un proyecto real donde las tareas se deber an definir como issues y cada nuevo feature se deber a crear con un pull request correspondiente que lo resuelve https guides github com introduction flow https docs github com en github getting started with github quickstart github flow muy importante parte importante del trabajo pr ctico es aprender a buscar en google para poder resolver de manera exitosa el trabajo pr ctico ejercicios ejercicio 1 elecci n de dataset y preguntas elegir un dataset de la wiki de postgresql https wiki postgresql org wiki sample databases u otra fuente que sea de inter s para el alumno crear un pull request con un archivo en formato markdown https guides github com features mastering markdown expliando el dataset elegido y una breve descripci n de al menos 4 preguntas de negocio que se podr an responder teniendo esos datos en una base de datos relacional de manera que sean consultables con lenguaje sql otras fuentes de datos abiertos sugeridas https catalog data gov dataset https datasetsearch research google com https www kaggle com datasets ejercicio 2 crear container de la db crear un archivo de docker compose https docs docker com compose gettingstarted que cree un container de docker https docs docker com get started con una base de datos postgresql con la versi n 12 7 recomendamos usar la imagen oficial de postgresql https hub docker com postgres disponible en docker hub se debe exponer el puerto est ndar de esa base de datos para que pueda recibir conexiones desde la m quina donde se levante el container ejercicio 3 script para creaci n de tablas crear un script de bash que ejecute uno o varios scripts sql que creen las tablas de la base de datos en la base postgresql creada en el container del ejercicio anterior se deben solamente crear las tablas primary keys foreign keys y otras operaciones de ddl https en wikipedia org wiki data definition language sin crear o insertar los datos ejercicio 4 popular la base de datos crear un script de python que una vez que el container se encuentre funcionando y se hayan ejecutado todas las operaciones de ddl necesarias popule la base de datos con el dataset elegido la base de datos debe quedar lista para recibir consultas durante la carga de informaci n puede momentareamente remover cualquier constraint que no le permita insertar la informaci n pero luego debe volverla a crear este script debe ejecutarse dentro de un nuevo container de docker mediante el comando docker run el container de docker generado para no debe contener los datos crudos que se utilizar an para cargar la base para pasar los archivos con los datos se puede montar un volumen argumento v de docker run o bien bajarlos directamente desde internet usando alguna librer a de python como requests ejercicio 5 consultas a la base de datos escribir un script de python que realice al menos 5 consultas sql que puedan agregar valor al negocio y muestre por pantalla un reporte con los resultados este script de reporting debe correrse mediante una imagen de docker con docker run del mismo modo que el script del ejercicio 4 ejercicio 6 documentaci n y ejecuci n end2end agregue una secci n al readme md comentando como resolvi los ejercicios linkeando al archivo con la descripci n del dataset y explicando como ejecutar un script de bash para ejecutar todo el proceso end2end desde la creaci n del container operaciones de ddl carga de datos y consultas para esto crear el archivo de bash correspondiente explicaci n de los ejercicios ejercicio 1 para el ejercicio 1 simplemente se seleccion una base de datos de inter s y se cre un archivo markdown dataset md explicando brevemente la info contenida en la fuente seleccionada y algunas posibles preguntas de inter s sobre dicha informaci n ejercicio 2 para el ejercicio 2 se cre un archivo de docker compose que instancia un container con la base de datos postgres version 12 7 como fue solicitado exponiendo el puerto por defecto 5432 y creando las variables de entorno de user password y db mapeando tambi n un volumen externo para poder persistir la informaci n de la base de datos fuera del container ejercicio 3 para el ejercicio 3 se gener un script de creaci n de las tablas iniciales en la base de datos dado que el script debe correr una vez se instancie el container para poder conectarse a la base de datos fue necesario implementar un m todo que permita el normal inicio de la imagen de postgres en el container pero que tambi n permita correr el script de creaci n de las tablas para esto se investig cu l es el comando entrypoint utilizado en la imagen oficial de postegres disponible en https github com docker library postgres blob master dockerfile alpine template y se cre un script de bash wrapper sh encargado de correr el comando original para inicializar normalmente la imagen de postgres pero dejandolo en backgroud y as poder correr en primer plano el script de creacion de tablas teniendo en cuenta que al finalizar ste ltimo se devuelva el proceso original al primer plano para evitar que el container se detenga otro cambio para este ejercicio fue la necesidad de crear un archivo dockerfile que permite el copiado de los sctipts creados a la imagen que se instancia a partir del archivo de docker compose ejercicio 4 para el ejercicio 4 se cre un servicio que se levante junto con la base de datos en el archivo de docker compose cuya funcionalidad es ingestar la data necesaria para los an lisis este servicio descarga la informaci n de los archivos indicados mediante el yml de docker compose conectandose a trav s de internet para transformarla e insertarla en la base de datos dado que la informaci n de los archivos se actualiza diariamente el servicio est dise ado para comparar la informaci n de los archivos descargados contra los datos existentes en la base de datos e insertar nicamente la informaci n nueva a n no cargada dicho servicio se levanta a partir de una imagen est ndar de python 3 a la cual se le instalan las librerias necesarias mediante un archivo de requierimientos al momento de la creaci n de la imagen final luego al instanciarse se mapea la carpeta en la que se encuentra el c digo fuente que realiza el trabajo para resolver este ejercicio fue necesario resolver algunos inconveniente t picos de la arquitectura de microservicios como reintetar las conecciones en caso de fallar o contemplar que el servicio al que se quiere alcanzar puede que a n no se encuentre operativo adem s de tener en cuenta cuestiones como la creaci n de una nica conexion abierta hacia la base de datos y cerrar los cursores una vez utilizados ejercicio 5 para el ejercicio 5 se cre un nuevo servicio que expone las api s creadas a partir de consultas sql que responden a las preguntas planteadas este servicio se levanta en el mismo docker compose que los creados anteriormente y expone los endpoints mediante flask en el puerto 5000 de localhost para exponer los diferentes endpoints en flask fue necesario crear las plantillas o blueprints para los diferentes endpoints definir cada una de las rutas de cada endpoint y los m todos a utilizar en este caso solo se utiliza en m todo get ya que no se requiere cargar ni modificar data desde dichos endpoints solo siendo necesaria la consulta de informaci n a trav s de las rutas definidas luego se definieron las queries que responden a cada una de las consultas creando tambi n algunos errores espec ficos con mensajes acordes para capturar los casos de reportes vac os adicionalmente en este paso se corrigieron algunos bugs del servicio de carga de datos generados en el ejercicio anterior para capturar correctamente la informaci n y agregar algunos mensajes personalizados en la consola para poder determinar el avance de dicho proceso ejercicio 6 para el ltimo ejercicio se cre el archivo run reports sh run reports sh que se encarga de levantar todos los servicios imprimir los reportes y bajar los servicios nuevamente en este paso se encontraron algunas complicaciones en relaci n a las deependencias de los servicios teniendo en cuenta las siguientes cuestiones 1 si bien en docker compose se defini la dependencia de servicios cada uno de los containers solo espera a que el anterior haya levantado pero no necesariamente a que el servicio est disponible ya se hab a visto esta cuesti n al resolver el ejercicio 4 por lo que se agregaron algunos chequeos similares a los utilizados para detectar el estado de la base de datos en el servicio de apis tambi n 2 si bien los servicios pueden estar disponibles no se podr an o deber an realizar las consultas a la api hasta que el servicio que carga la data no haya finalizado dicha tarea este ultimo punto fue m s complejo de resolver porque habr a que poder identificar cu ndo el servicio de carga de la info finaliza con status 0 ok para poder realizar las consultas a los endpoints este problema se resolvi levantando mediante docker compose run la base de datos y el servicio de carga de datos primero y al finalizar este ltimo utilizar docker compose up para levantar el resto de stack faltante en este caso levanta la app pero no levanta la base de datos porque ya se encuentra levantada por el comando anterior la nica desventaja es que el segundo comando levanta nuevamente el servicio load data porque como el anterior ya finaliz no lo detecta corriendo y este servicio vuelve a descargar la data de internet aunque no la vuelve a cargar porque la bd se encuentra actualizada para lidiar con este tipo de problemas de forma correcta se podr a utilizar alg n tipo de mensajer a entre servicios pero ya que este tema escapaba un poco del scope del tp se deja pendiente para plantear en una arquitectura cloud en el pr ximo tp | bash docker docker-compose python rdbms | cloud |
DB_Project_ELIDEK_2022 | db project elidek 2022 this is a project for the databases class in ntua electrical and computer engineering department still work in progress contributors listed alphabetically 1 gina kalogeropoulou 03114174 ginakalog 2 nikos papanikolas 03120624 nikosp6 3 sergios batsas 03114762 azzy90 dependencies 1 nodejs https nodejs org en 2 mysql for windows https dev mysql com downloads installer required nodejs dependencies 1 express https www npmjs com package express fast unopinionated minimalist web framework for node 2 mysql2 https www npmjs com package mysql2 fast mysql driver implements core protocol prepared statements ssl and compression in native js 3 ejs https www npmjs com package ejs a templating engine optional nodejs dependencies 1 nodemon https www npmjs com package nodemon nodemon is a tool that helps develop node js based applications by automatically restarting the node application when file changes in the directory are detected 2 custom env https www npmjs com package custom env custom env is a library built to make development more feasible by allowing multiple env configurations for different environments useful applications database managment applications dbms such as heidisql https www heidisql com navicat https www navicat com en mysql workbench https dev mysql com downloads workbench installation 1 run git clone https github com azzy90 db project elidek 2022 inside the directory you want your application 2 import create database full sql sql codes create database full sql to your database 3 edit the env example txt and rename it to env localhost located under project 4 create the needed views to your database using the 3 2 sql sql codes 3 2 sql 5 run npm install inside your db elidek project 2022 project directory to install dependencies 6 run npm start to start your server with nodemon 7 visit http localhost port er diagram https github com azzy90 db project elidek 2022 blob main diagrams er png relational model https github com azzy90 db project elidek 2022 blob main diagrams relational schema v2 png | server |
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EE-712_Spring_2019_IIT-B | ee 712 spring 2019 iit b this repo consists of all the resources and lab experiments taken under ee 712 embedded system design course in spring 2019 at iit bombay the labs are performed on texas instrument ti s arm based board which has tm4c123gh6pm micro controller chip on board course taken by prof dinesh sharma and prof p c pandey | embedded-systems arm tiva-c-series assembly-language | os |
phoenix-rtos-project | phoenix rtos project ci https github com phoenix rtos phoenix rtos project actions workflows ci yml badge svg https github com phoenix rtos phoenix rtos project actions workflows ci yml nightly https github com phoenix rtos phoenix rtos project actions workflows nightly yml badge svg https github com phoenix rtos phoenix rtos project actions workflows nightly yml phoenix rtos is a scalable real time operating system for iot it is based on its own microkernel and can be used either on small devices based on microcontrollers and on advanced computer systems based on multiple processors and equipped with gigabytes of ram the posix application environment can be emulated to enable the execution of regular un x applications the arinc653 execution environment apex advanced partitioning and do 178c certification package for aerospace applications are under development phoenix rtos supports multiple architectures including arm cortex m arm cortex a intel x86 risc v and some popular microcontrollers and reference boards the system is still under development but it was implemented in numerous smart utility appliances e g in smart gas meters smart energy meters and data concentrators dcu this repository contains sample phoenix rtos project it aggregates all system components kernel standard library device drivers filesystems loader building tools after building for target platform the final image is created and can be run on hardware or on emulators the project was built and tested on minimal clean installation of ubuntu 22 04 2 lts for desktops building the building process was described in phoenix rtos doc building https github com phoenix rtos phoenix rtos doc blob master building readme md running to launch the created image on target architecture please see phoenix rtos doc quickstart https github com phoenix rtos phoenix rtos doc blob master quickstart readme md documentation phoenix rtos philosophy architecture and internals are described in phoenix rtos doc https github com phoenix rtos phoenix rtos doc blob master readme md product website phoenix rtos website phoenix rtos com https phoenix rtos com | phoenix-rtos rtos | os |
cs224d | course description natural language processing nlp is one of the most important technologies of the information age understanding complex language utterances is also a crucial part of artificial intelligence applications of nlp are everywhere because people communicate most everything in language web search advertisement emails customer service language translation radiology reports etc there are a large variety of underlying tasks and machine learning models powering nlp applications recently deep learning approaches have obtained very high performance across many different nlp tasks these models can often be trained with a single end to end model and do not require traditional task specific feature engineering in this spring quarter course students will learn to implement train debug visualize and invent their own neural network models the course provides a deep excursion into cutting edge research in deep learning applied to nlp the final project will involve training a complex recurrent neural network and applying it to a large scale nlp problem on the model side we will cover word vector representations window based neural networks recurrent neural networks long short term memory models recursive neural networks convolutional neural networks as well as some very novel models involving a memory component through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems | ai |
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accord-net-extensions | p align center a href https www nuget org profiles dajuric img src deployment logo logo big png alt accord net extensions logo width 150 align center a p p align center a href https www nuget org profiles dajuric img src https img shields io badge nuget v3 0 2 blue svg style flat square alt nuget packages version a a href https github com dajuric accord net extensions raw master deployment documentation help accord net 20extensions 20documentation chm img src https img shields io badge help download yellow svg style flat square alt help a p p align justify b accord net extensions b is an extension framework for a href http accord framework net accord net a and a href http www aforgenet com framework license html aforge net a the framework sets focus on net native array as primary imaging object and offers computer vision algorithms mostly build as extensions the libraries can be grouped as following p h3 image processing h3 ul li p b a href https www nuget org packages accord extensions imaging algorithms accord extensions imaging algorithms package a b br implements image processing algorithms as net array extensions including the accord net algorithms p table border 0 cellpadding 1 cellspacing 1 tbody tr td align center width 100 img alt algorithms sample src deployment readme resources algorithms sample png width 500 td tr tbody table li li p b a href https www nuget org packages accord extensions imaging algorithms line2d accord extensions imaging algorithms line2d package a b br implements template matching algorithm 20x faster than conventional sliding window approach p p b tutorials b a href http www codeproject com articles 826377 rapid object detection in csharp target blank fast template matching a p table border 0 cellpadding 1 cellspacing 1 tbody tr td align center width 100 a href https www youtube com watch v b4 ir8ysrss target blank img alt fast template matching src deployment readme resources fasttemplatematching jpg width 250 a td tr tbody table li li p b a href https www nuget org packages accord extensions vision accord extensions vision package a b br pyramidal klt tracker camshift meanshift p p b tutorials b a href http www codeproject com articles 840823 object feature tracking in csharp target blank object feature tracking a p table border 0 cellpadding 1 cellspacing 1 tbody tr td align center width 50 a href https www youtube com watch v 6b pndcwtz4 target blank img alt klt optical flow src deployment readme resources kltopticalflow jpg width 250 a td td align center width 50 a href https www youtube com watch v c ivmauhap4 target blank img alt camshift tracker src deployment readme resources camshifttracker jpg width 250 a td tr tbody table li ul h3 math libraries h3 ul li p b a href https www nuget org packages accord extensions math accord extensions math package a b br fluent matrix extensions geometry and graph structures and extensions p li li p b a href https www nuget org packages accord extensions statistics accord extensions statistics package a b br object tracking algorithms kalman filter particle filter joint probability data association filter jpdaf br p p b tutorials b a href http www codeproject com articles 865935 object tracking kalman filter with ease target blank kalman filter a a href http www codeproject com articles 865934 object tracking particle filter with ease target blank particle filter a p table border 0 cellpadding 1 cellspacing 1 tbody tr td align center width 50 a href https www youtube com watch v lsojxcqiavq target blank img alt kalman filter 2d src deployment readme resources kalmanfiler2d jpg width 250 a td td align center width 50 a href https www youtube com watch v p9fxmf hrw0 target blank img alt kalman filter tracking src deployment readme resources kalmanfiltertracking jpg width 250 a td tr tr td align center width 50 a href https www youtube com watch v sv6cmeha51k target blank img alt particle filter color tracking src deployment readme resources particlefiltercolortracking jpg width 250 a td td align center width 50 a href https www youtube com watch v 5vwtoimnlto target blank img alt particle filter template matching src deployment readme resources particlefiltertemplatematching jpg width 250 a td tr tbody table li ul h3 support libraries h3 ul li p b a href https www nuget org packages accord extensions imaging aforgeinterop accord extensions imaging aforgeinterop package a b br interoperability extensions between net array and aforge s unmanagedimage p li li p b a href https www nuget org packages accord extensions core accord extensions core package a b br element caching parallel extensions shared structures p li ul h2 getting started h2 p align justify browse through some included samples install nuget packages and enjoy br b take a look at b ul li a href https github com dajuric dot imaging dotimaging a a portable imaging library the foundation of accord net extensions li li a href https github com dajuric more collections morecollections a a set of portable collections installable as source files via nuget li ul p h2 how to engage contribute and provide feedback h2 p align justify the best ways to contribute and provide feedback is to try things out file bugs and propose enhancements your opinion is important and will define the future roadmap if you have any questions comments or you would like to propose an enhancement please leave the message on github or write to darko juric2 at gmail com p h2 final word h2 p align justify if you like the project please b star it b in order to help to spread the word that way you will make the framework more significant and in the same time you will motivate me to improve it so the benefit is mutual p | ai |
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FreeRTOS---ARM-Cortex-A9-VersatileExpress-Quad-Core-port | freertos arm cortex a9 versatileexpress quad core port this port runs four separate freertos instances on the versatileexpress with the coretile 9x4 daughter board and the arm cortex a9 mpcore quad core cpu each serial port on the versatileexpress prints out its related core id as well as the demo output of each instance of freertos each core uses its l1 i and d cache and the mmu cached pages range from 0 to 64 mb 1 gb of memory in total in the physical memory space shared memory can be used for core to core communication between the freertos instances in the non cached memory for more information on how to compile and boot the freertos port on the versatileexpress see the readme file this port was created by the department of information technologies at bo akademi university https research it abo fi within the recomp project http www recomp project eu githalytics com alpha https cruel carlota pagodabox com 2f005e48c99a4aeb9ef2a93ae6de0b68 githalytics com http githalytics com eslab freertos arm cortex a9 versatileexpress quad core port | os |
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react-lds | react lds travis https travis ci org react lds react lds svg branch master react lds provides react components for the salesforce lightning design system http lightningdesignsystem com installation to install the stable version with npm http npmjs com run bash npm install save react react dom moment moment timezone moment range bash npm install react lds save usage react lds exports components as modules you can consume these via import in your own react components js import react from react import badge from react lds const helloworld props badge theme warning label props message head over to the storybook docs https react lds github io react lds to see a list of available components and their usage as well as interactive sample implementations of each component es modules by default react lds transpiles to commonjs modules you can import es modules directly by importing components from react lds es this enables tree shaking when using webpack or similiar bundlers js commonjs import supported browsers ie11 last 2 versions import badge from react lds es import supported browsers last 2 versions import badge from react lds es do not mix imports from react lds and react lds es in your codebase this will duplicate code context in order to use reactlds you will have to provide assetbasepath via the react context https facebook github io react docs context html js import children component from react import proptypes from prop types class assetpathprovider extends component getchildcontext return assetbasepath assets render const children this props return div children div assetpathprovider proptypes children proptypes node isrequired assetpathprovider childcontexttypes assetbasepath proptypes string development yarn install and yarn start add or modify stories in stories happy hacking scaffold components new components can be scaffolded with hygen https hygen io templates to add a component run npx hygen component new name foo developing while embedded into a react project npm link in this folder after you changed stuff run npm build to update the files inside the dist folder because that s the entry point for external react applications in your react app npm link react lds publish open a new pull request from release version adjust version in package json write changelog md merge into master and add a new tag travis will do the rest react lds started as a propertybase https www propertybase com project in 2016 and was maintained by propertybase between 2016 2020 | salesforce-lightning react-components javascript lightning-design-system | os |
workout-movement-counting | workout movement counting app using dense optical flow and convolutional neural networks this repository contains my coursework as 3rd year bsc in hse i had to create a web app which helps sportsmen to count their movements during the workout br also checkout my medium writeup regarding this problem here https medium com artkulakov how i created the workout movement counting app using deep learning and optical flow 89f9d2e087ac source friends link sk e14ec243ea1ff3bb42c3c4d05067e85c for this purpose i combined the dense optical flow algorithm with a simple cnn network written in pytorch as you can see it is pretty easy to get the idea of what one push up is if we look at how frames are converted to dense optical flow representation in my algorithm images push up jpg thus dense optical flow converts frames to color coded representation and cnn solves a multiclass problem which is to classify each frame as move down move up or not a move to wrap my algorithm in something which can really work i also created a web interface using django here is how it looks like images interface jpg to run the web app follow the instructions below instructions 1 clone the repo and run pip r install requirements txt 2 cd workoutapp 3 run the app with python manage py runserver 4 choose the predefined workout and test | ai |
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onechain | us english https github com lukepark327 onechain kr korean https github com lukepark327 onechain tree korean license https img shields io badge license apache 202 0 blue svg https opensource org licenses apache 2 0 version https img shields io badge version v3 0 0 orange svg https github com lukepark327 onechain blob master package json node https img shields io badge node 3e 3d4 3 2 yellow svg https nodejs org en onechain onechain https github com lukepark327 onechain blob master images icon png assist in developing blockchain core efficiently 2018 oss grand developers challenge award https www oss kr 2019 oss grand developers challenge award https www oss kr inspired by lhartikk naivechain https github com lhartikk naivechain live demo run on ainize https ainize ai static images run on ainize button svg https ainize web app redirect git repo github com lukepark327 onechain run onechain on ainize you can access the live onechain with the endpoint https onechain lukepark327 endpoint ainize ai provided by ainize a serverless platform for open source projects for example you can see all blocks in blockchain with a blocks get request i e here https onechain lukepark327 endpoint ainize ai blocks onechain explorer explorer https github com lukepark327 onechain blob master images comparison png https github com lukepark327 onechain explorer click on the above image to go to the onechain explorer repo a onechain explorer is the front end used to visualize the state of the blockchain this blockchain explorer allows users to see the latest blocks and details about a particular block onechain already has multiple functions with http restful api endpoints so the web page calls those endpoints and visualizes the results the full ui code is located in lukepark327 onechain explorer https github com lukepark327 onechain explorer repo vue js and vuetify are used abstract structure https github com lukepark327 onechain blob master images structure png as blockchain based services grew so open source that assists in developing blockchain core was needed there are open source projects like bitcoin and ethereum but those are too hard to learn and to use we solve the above problems with a onechain simple implementation of blockchain core the onechain adopts modular design dividing layers into blockchain network apis and wallet for clarity also detailed comments and documents are provided to facilitate learning and reusing both front end and back end of onechain is written in javascript node js and vue js but written in simple without async et al so that other language developers can understand them use cases chapter 1 https github com lukepark327 onechain tree chapter 1 chapter 2 https github com lukepark327 onechain tree chapter 2 chapter 3 https github com lukepark327 onechain tree chapter 3 chapter 4 https github com lukepark327 onechain tree chapter 4 p align center a href http www yes24 com product goods 75235536 img width 480 src https github com lukepark327 onechain blob master images book jpeg a p click on the above image to go to the bookstore governance simulator on blockchain based on smart city cases paper http www dbpia co kr journal articledetail node07614082 blockchain policy simulator bbr hackathon http www breview kr excellence award http decenter sedaily com newsview 1s639fv540 video demonstration https www youtube com watch v afcnpzit4fe code https github com lukepark327 blockchain simulator edu chain code https github com lukepark327 educhain plasma dag code https github com plasma dag plasma client noonsatae avalanche implementation https github com noonsatae docker quick start bash docker run it p 3001 3001 p 6001 6001 lukepark327 onechain how to start environments node js v8 11 3 curl 7 55 1 or postman v6 4 4 install dependencies bash npm install run nodes start node 1 bash npm start start node 2 set http port for http communication env http port 3002 or export http port 3002 set p2p port for p2p communication among peers env p2p port 6002 or export p2p port 6002 option set pre connected peers before running env peers ws 127 0 0 1 6001 ws 127 0 0 1 6003 or export peers ws 127 0 0 1 6001 ws 127 0 0 1 6003 option set private key where private key is located env private key second or export private key second now private key is located in wallet second instead of default location wallet default bash npm start how to use p align center a href https youtu be ubeutrtggxi img width 480 src https img youtube com vi ubeutrtggxi 0 jpg a p click on the above image to play the video get blockchain bash curl http 127 0 0 1 3001 blocks use pretty print json for better readability bash curl http 127 0 0 1 3001 blocks python m json tool python 2 6 is required get a particular block bash curl http 127 0 0 1 3001 block number for example let us get a block whose number index is 3 bash curl http 127 0 0 1 3001 block 3 add new block bash curl x post http 127 0 0 1 3001 mineblock curl h content type application json data data anything you want anything you need http 127 0 0 1 3001 mineblock get current version bash curl http 127 0 0 1 3001 version get the version of particular block bash curl http 127 0 0 1 3001 blockversion number for example let s get a version of a block whose number index is 3 bash curl http 127 0 0 1 3001 blockversion 3 get connected peer s bash curl http 127 0 0 1 3001 peers add peer s bash curl h content type application json data peers ws 127 0 0 1 6002 ws 127 0 0 1 6003 http 127 0 0 1 3001 addpeers get address bash curl http 127 0 0 1 3001 address stop bash curl x post http 127 0 0 1 3001 stop license the onechain project is licensed under the apache license version 2 0 https opensource org licenses apache 2 0 also included in our repository in the license https github com lukepark327 onechain blob master license file | blockchain javascript nodejs opensource p2p vue blockchain-explorer | blockchain |
Meal_Charge_Project | meal charge project cloud engineering project tip calculator project this is the tip calculator project script it helps to generating the grand total amount of a meal purchased with tips and the sales tax written by bolarinwa oyerinde cloud engineer trainee | cloud |
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mcte4342-embedded-system-design | mcte 4342 embedded system design this repository contains the source code for the course mcte 4342 embedded system design at iium the course is taught by dr zulkifli bin zainal abidin weekly activities details of each week tasks is available in their respective folders no title status week 4 week4 gpio white check mark week 5 week5 analog white check mark week 6 week6 timer port white check mark week 7 week7 interrupts white check mark week 8 week8 memory white check mark week 9 week9 power management white check mark week 10 week10 motors white check mark week 11 week11 serial com white check mark legend white check mark completed arrows counterclockwise wip negative squared cross mark not started mini project go to https github com esd shooting mini game sub fareez 1914577 sub | embedded engineering iium mcte4342 koe-iium | os |
CMPE2750 | cmpe2750 embedded system design coding for atmega 328p using c language | os |
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udacity-DEND-project-3-Cloud-Data-Warehouse | project 3 data warehouse introduction a music streaming startup sparkify has grown their user base and song database and want to move their processes and data onto the cloud their data resides in s3 in a directory of json logs on user activity on the app as well as a directory with json metadata on the songs in their app as their data engineer i will be building an etl pipeline that extracts their data from s3 stages them in redshift and transforms data into a set of dimensional tables for their analytics team to continue finding insights in what songs their users are listening to i tested my database and etl pipeline by running test queries and compared my results with the expected results project datasets the two datasets that reside in s3 with links to each below song data s3 udacity dend song data log data s3 udacity dend log data log data json path s3 udacity dend log json path json song dataset the first dataset is a subset of real data from the million song dataset each file is in json format and contains metadata about a song and the artist of that song the files are partitioned by the first three letters of each song s track id for example here are filepaths to two files in this dataset song data a b c trabcei128f424c983 json song data a a b traabjl12903cdcf1a json and below is an example of what a single song file traabjl12903cdcf1a json looks like num songs 1 artist id arjie2y1187b994ab7 artist latitude null artist longitude null artist location artist name line renaud song id soupiru12a6d4fa1e1 title der kleine dompfaff duration 152 92036 year 0 log dataset the second dataset consists of log files in json format generated by this event simulator based on the songs in the dataset above these simulate app activity logs from an imaginary music streaming app based on configuration settings the log files in the dataset are partitioned by year and month for example here are filepaths to two files in this dataset log data 2018 11 2018 11 12 events json log data 2018 11 2018 11 13 events json and below is an example of what the data in a log file 2018 11 12 events json looks like schema https video udacity data com topher 2019 february 5c6c3ce5 log data log data png redshift staging tables the song data is loaded from s3 into staging songs and the log data is loaded into staging events below is a diagram of the two staging tables alt text staging tables png schema for song play analysis using the song and event datasets the following star schema was created for songplay analysis with the tables listed below data for these tables are loaded from the 2 staging tables namely staging songs and staging events fact table 1 songplays records in event data associated with song plays i e records with page nextsong songplay id start time user id level song id artist id session id location user agent dimension tables 2 users users in the app user id first name last name gender level 3 songs songs in music database song id title artist id year duration 4 artists artists in music database artist id name location lattitude longitude 5 time timestamps of records in songplays broken down into specific units start time hour day week month year weekday schema https udacity reviews uploads s3 us west 2 amazonaws com attachments 38715 1599555988 song erd png star database schema project template the following files python files are included in the repository for this project sql queries py sql statement definitions for creating and inserting data staging tables plus the fact and dimensions on redshift this file will be imported into the two other files below create table py runs the commands inside sql queries py to create the necessary tables in redshift etl py runs the actual etl process where it loads the data from s3 into staging tables on redshift and then process that data into the analytics tables on redshift readme md readme file with information about this project running files for this project before running the above python scripts the redshift cluster will need to be created first with the propoer database and access credentials and the appropriate iam role defined after this step is completed the 3 python scripts above are to be executed from the command prompt in the order below python sql queries py python create tables py python etl py sample queries select count from songplays select count from users select count from songs select count from artists select count from time | cloud |
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apparatus | asto apparatus software tool archive notice after the completion of my research degree the repository has been archived asto has made a significant impact to my research i want to express my gratitude to everyone that helped make it better however this is not the end active development with continue on cyberlens s https github com cyberlens disc0very git repository please report issues and feature requests there an iot network security analysis and visualization tool js standard style https img shields io badge code 20style standard brightgreen svg http standardjs com styled with prettier https img shields io badge styled with prettier ff69b4 svg https github com prettier prettier travis build https travis ci org or3stis apparatus svg branch master dependencies status https david dm org or3stis apparatus svg https david dm org or3stis apparatus devdependencies status https david dm org or3stis apparatus dev status svg https david dm org or3stis apparatus type dev asto is security analysis tool for iot networks it is developed to support the apparatus security framework asto is based on electron http electron atom io and cytoscape js http js cytoscape org the icons are provided by google s material design https material io icons the application is in alpha stage the focus now is to improve the core functionality of the application along with the introduction of additional features to reach beta stage features 1 graph based visualization of iot systems 1 model iot systems in design and implementation engineering phases 1 an automatic model transition between the two engineering phases 1 model iot system state 1 automate implementation phase models generation using pcap ng files 1 perform model based vulnerability identification through cve databases 1 generate automated model based security insights 1 attribute based pattern identification 1 search through graphs using a variety of options concepts modules attributes 1 togglable light and dark theme some screenshots asto home https raw githubusercontent com or3stis apparatus master assets screenshot1 png asto ui 1 https raw githubusercontent com or3stis apparatus master assets screenshot2 png asto ui 2 https raw githubusercontent com or3stis apparatus master assets screenshot3 png asto ui 2 https raw githubusercontent com or3stis apparatus master assets screenshot4 png console asto has a command line console available on the bottom right corner of the app you gain focus on the console by pressing the keybinding cmd l for macos and ctrl l for windows linux if you type help it will display a list of console options the console can be used to search for specific objects in the graph or perform operations raw text is used as search input for example if you type device asto will highlight all the nodes in the graph that have the word device as an attribute all console commands must be preceded with a for example typing insights will perform the security insights functions on the other hand typing insights without the will perform a search operation on the graph elements with the keyword insights color themes asto supports a light and a dark color theme the colors themes are based on atom s one dark https github com atom one dark syntax and one light https github com atom one light syntax to switch between themes use the toggle button on the bottom left corner to use to clone and run this repository you ll need git https git scm com and node js https nodejs org en download installed on your computer to download and install the app type the following in your terminal bash clone this repository git clone https github com or3stis apparatus git navigate into the repository cd apparatus install dependencies npm install different mode operations of the app bash to run the app in the default mode npm start to run the app in developer mode npm run dev to build the app into binary npm run dist because the app is still in prototype stage it is best to keep up to date with the most recent commits to do so before starting the app type bash inside the apparatus directory update to the latest git pull once the app starts the first window home screen will ask you to choose which modeling phase would you like to use after you select a phase you will be presented with three choices the first is to create a new graph the second choice is to load an existing graph the third option is the debug app which loads a default graph used for debugging purposes you will find some example graphs in the sample folder note in performance if you render a graph with more than a thousand nodes depending on your hardware you might detect some performance issues the reason is that the default label rendering of nodes and edges in asto is quite expensive rendering label on nodes and edges along with directional arrows is cpu expensive to improve performance you can hide the labels and the directional arrows by pressing the label button you can find more information about cytoscape s performance optimizations in this link http js cytoscape org performance privacy notice the software does not collect personal information of any kind the only network operation the application performs is when the vulnerability identification process is used the vulnerability identification makes a network request to https cve circl lu api search can be changed in the settings which maintains its own analytics contributing if you want to contribute to the project that s great check the contributing https github com or3stis apparatus blob master contributing md guide the application is being developed on macos that means that new commits might introduce breaking changes in other platforms especially commits that involve access to the file system if something is not working don t hesitate to create an issue https github com or3stis apparatus issues if you want to find out how the app works check the wiki https or3stis github io apparatus wiki you can check the project s planned features in the roadmap https or3stis github io apparatus roadmap thanks a shoutout to nomnuds https github com nomnuds and nickarg https github com nickarg who provide the much needed feedback on windows license mit license md | security-analysis cytoscape iot security-iot asto apparatus | server |
awesome-openzeppelin | awesome openzeppelin awesome https awesome re badge svg https awesome re standard readme compliant https img shields io badge readme 20style standard brightgreen svg https github com richardlitt standard readme blockchain educational resources curated by the openzeppelin team openzeppelin https openzeppelin com builds key infrastructure to develop and operate smart contract systems we work on openzeppelin contracts https openzeppelin com contracts a package for secure smart contract development openzeppelin sdk https openzeppelin com sdk a platform to develop deploy and operate smart contract projects on ethereum and every other evm and ewasm powered blockchain audits https openzeppelin com security audits verification of key projects for the decentralized ecosystem this repository contains links to resources that have been important parts of our learnings and that influence how we work on our projects if you want to join the openzeppelin team we are hiring https grnh se f0e8f9db3us table of contents bitcoin bitcoin community management community management cryptoeconomics cryptoeconomics cryptography cryptography cypherpunk cypherpunk design design diversity diversity ethereum ethereum legal legal linux linux organizations organizations programming languages programming languages go go javascript javascript python python solidity solidity typescript typescript remote remote security security software development software development bitcoin https en wikipedia org wiki bitcoin books mastering bitcoin https github com bitcoinbook bitcoinbook by andreas antonopoulos courses introduction to digital currencies https digitalcurrency unic ac cy free introductory mooc by andreas antonopoulos and antonis polemitis community management https en wikipedia org wiki community management talk about this topic with elopio https github com elopio books the art of community http www artofcommunityonline org by jono bacon cryptoeconomics https en wikipedia org wiki cryptoeconomics talk about this topic with alejo salles https github com fiiiu books cryptoassets https www goodreads com en book show 36197082 cryptoassets by chris burniske and jack tatar good basic book with a broad view of the crypto world addressed to investors new to the space governing the commons the evolution of institutions for collective action https www goodreads com book show 1048424 governing the commons by elinor ostrom courses game theory by matthew o jackson kevin leyton brown and yoav shoham part i https www coursera org learn game theory 1 part ii https www coursera org learn game theory 2 cryptoeconomics study https cryptoeconomics study by karl floersch community efforts posts cryptoasset valuations https medium com cburniske cryptoasset valuations ac83479ffca7 by chris burniske governance in 0x protocol https blog 0xproject com governance in 0x protocol 86779ae5809e by will warren leverage points places to intervene in a system http donellameadows org archives leverage points places to intervene in a system by donella meadows on medium of exchange token valuations https vitalik ca general 2017 10 17 moe html by vitalik buterin on value velocity and monetary theory https medium com blockchannel on value velocity and monetary theory a new approach to cryptoasset valuations 32c9b22e3b6f by alex evans thoughts on liberal radicalism https medium com lkngtn thoughts on liberal radicalism 2c76eaa397ec by luke duncan token curated registries 1 0 https medium com ilovebagels token curated registries 1 0 61a232f8dac7 by mike goldin token engineering series by trent mcconaghy can blockchains go rogue https blog oceanprotocol com can blockchains go rogue 5134300ce790 towards a practice of token engineering https blog oceanprotocol com towards a practice of token engineering b02feeeff7ca token engineering case studies https blog oceanprotocol com token engineering case studies b44267e68f4 talks introduction to cryptoeconomics https www youtube com watch v pkqdjah1dro by vitalik buterin the pretence of knowledge https www nobelprize org prizes economic sciences 1974 hayek lecture by friedrich von hayek whitepapers livepeer whitepaper https github com livepeer wiki blob master whitepaper md by doug petkanics and eric tang details the cryptoeconomics of the livepeer protocol users pay either for broadcasting or consuming transcoders are chosen through dpos panvala https www panvala com panvala whitepaper pdf introduces a system of token mediated auditing token capacitor is a cryptoeconomic primitive that progressively releases tokens which can be directed to different destinations by the token holders and michelin stars are used for code safety cryptography https en wikipedia org wiki cryptography posts how not to use ecdsa https yondon blog 2019 01 01 how not to use ecdsa by yondon fu bitcoin transaction malleability https eklitzke org bitcoin transaction malleability by evan klitzke courses cryptography i https www coursera org learn crypto by dan boneh challenges cryptopals https cryptopals com learn crypto by doing challenges where you build and break popular ciphers cypherpunk https en wikipedia org wiki cypherpunk books free culture http www free culture cc by lawrence lessig free software free society https www gnu org philosophy fsfs rms essays pdf by richard stallman proposed roads to freedom http www gutenberg org ebooks 690 by bertrand russell talks the art of asking https www youtube com watch v xmj p 6h69g by amanda palmer decentralize democratize or die https youtu be je4you6ssi8 by corey doctorow design books the design of everyday things https en wikipedia org wiki the design of everyday things by donald norman less is enough on architecture and asceticism https www goodreads com book show 21167757 less is enough by pier vittorio aureli courses interaction design specialization https www coursera org specializations interaction design by scott klemmer elizabeth gerber and jacob o wobbrock posts designing welcome mats to invite user privacy https www eff org deeplinks 2019 02 designing welcome mats invite user privacy 0 by alexis hancock diversity courses inclusive speaker orientation https training linuxfoundation org training inclusive speaker orientation by the linux foundation and the national center for women information technology posts how duolingo achieved a 50 50 gender ratio for new software engineer hires http making duolingo com how duolingo achieved a 5050 gender ratio for new software engineer hires by jeesoo sohn how i work with someone who is learning https blog mapbox com how i work with someone who is learning d6c53e460625 by emily mcafee how to answer questions in a helpful way https jvns ca blog answer questions well by julia evans how to ask good questions https jvns ca blog good questions by julia evans not applicable what your job post is really saying https where coraline codes blog not applicable by coraline ada ehmke ethereum https en wikipedia org wiki ethereum see also solidity solidity books mastering ethereum https github com ethereumbook ethereumbook by andreas antonopoulos in progress posts a prehistory of the ethereum protocol https vitalik ca general 2017 09 14 prehistory html by vitalik buterin decentralised oracles a comprehensive overview https medium com fabric ventures decentralised oracles a comprehensive overview d3168b9a8841 by julien thevenard ethereum in depth series https blog openzeppelin com ethereum in depth part 1 968981e6f833 by openzeppelin making sense of ethereum s layer 2 scaling solutions state channels plasma and truebit https medium com l4 media making sense of ethereums layer 2 scaling solutions state channels plasma and truebit 22cb40dcc2f4 by josh stark stablecoins designing a price stable cryptocurrency https hackernoon com stablecoins designing a price stable cryptocurrency 6bf24e2689e5 by haseeb qureshi why another stablecoin https medium com reserve currency why another stablecoin 866f774afede nevin freeman videos smart accounts https www youtube com watch v gc cqgpcl8 by philippe castonguay and panashe mahachi legal courses intellectual property law and policy by r polk wagner part 1 https www edx org course intellectual property law policy part 1 pennx iplaw1x 0 part 2 https www edx org course intellectual property law policy part 2 pennx iplaw2x 0 linux courses introduction to linux https www edx org course introduction to linux by the linux foundation organizations talk about this topic with demi https twitter com demibrener books the manager s path https www goodreads com book show 34616805 the manager s path by camille fournier the sovereign individual https www goodreads com book show 82256 the sovereign individual by james dale davidson and william rees mogg talks the network state https www youtube com watch v kilupvusdxg by balaji srinivasan voice vs exit https www youtube com watch v coubchlxt6a by balaji srinivasan programming languages go books the go programming language https www oreilly com library view the go programming 9780134190570 by brian w kernighan and alan a a donovan javascript books javascript the good parts http shop oreilly com product 9780596517748 do by douglas crockford python books dive into python 3 https www apress com gp book 9781430224150 by mark pilgrim effective python 59 specific ways to write better python https www oreilly com library view effective python 59 9780134034416 by brett slatkin solidity websites cryptozombies https cryptozombies io by loom network solidity koans https soliditykoans org by nicole zhu challenges ctf ethernaut https ethernaut openzeppelin com by openzeppelin blockchain ctf https blockchain ctf securityinnovation com by security innovation goat casino https github com nccgroup goatcasino by ncc group posts deconstructing a solidity contract series https blog openzeppelin com deconstructing a solidity contract part i introduction 832efd2d7737 by alejandro santander from the openzeppelin team smart contract security openzeppelin audits reports https blog openzeppelin com security audits by openzeppelin smart contract security bibliography https consensys github io smart contract best practices bibliography by consensys smart contract weakness classification registry https smartcontractsecurity github io swc registry maintained by the mythril https mythril ai team list of known attack vectors and common anti patterns in smart contracts https github com sigp solidity security blog by agemanning list of risks and vulnerabilities in erc20 token contracts https github com sec bit awesome buggy erc20 tokens blob master erc20 token issue list md by secbit def con 25 hacking smart contracts talk https www youtube com watch v wieessi3ntk by konstantinos karagiannis typescript courses understanding typescript https www udemy com understanding typescript by maximilian schwarzm ller remote posts the presence prison https m signalvnoise com the presence prison 4c776292c8d2 by jason fried don t work remotely http blairreeves me 2018 11 09 dont work remotely by blair reeves security posts trusted third parties are security holes https nakamotoinstitute org trusted third parties by nick szabo why openness is the greatest path to security https www forbes com sites martenmickos 2018 09 26 why openness is the greatest path to security aadef305f7f5 by marten mickos talks reflections on trusting trust https www archive ece cmu edu ganger 712 fall02 papers p761 thompson pdf by ken thompson opsec operations security https en wikipedia org wiki operations security from wikipedia a modest privacy protection proposal https medium com s story a modest privacy protection proposal 5b47631d7f4c by jameson lopp ignorance is strength https grugq github io blog 2013 06 13 ignorance is strength by grugq https github com grugq operational security and the real world https medium com thegrugq operational security and the real world 3c07e7eeb2e8 by grugq https github com grugq new york s finest opsec https grugq github io blog 2014 02 13 new yorks finest opsec by grugq https github com grugq a fistful of surveillance https grugq github io blog 2014 02 10 a fistful of surveillance by grugq https github com grugq operations security intelligence threat handbook section 1 introduction https fas org irp nsa ioss threat96 part01 htm by the interagency opsec support staff https fas org irp nsa ioss index html why you should get a burner phone number even if you aren t a spy https www popsci com burner phone number by david nield burner phone best practices https b3rn3d herokuapp com blog 2014 01 22 burner phone best practices by b3rn3d https b3rn3d herokuapp com some remarkably good opsec advice for a 1996 non computer book https blog cyberwar nl 2015 02 some remarkably good opsec advice from a 1996 book by matthijs r koot how the intercept outed reality winner https blog erratasec com 2017 06 how intercept outed reality winner html by robert graham extremist forums provide digital opsec training https ctc usma edu extremist forums provide digital opsec training by aaron brantly muhammad al ubaydi some elements of intelligence work https grugq github io resources some elements of intelligence work dulles txt by grugq https github com grugq twitter activist security https medium com thegrugq twitter activist security 7c806bae9cb0 by grugq https github com grugq how to vanish http www howtovanish com new to how to vanish start here agent handling https en wikipedia org wiki agent handling from wikipedia online privacy through opsec and compartmentalization part 2 https www ivpn net privacy guides online privacy through opsec and compartmentalization part 2 it was dpr in the tor hs with the btc https grugq github io blog 2013 10 09 it was dpr by grugq https github com grugq hacker opsec with the grugq https blogsofwar com hacker opsec with the grugq by blogs of war digital security https gijn org digital security by the global investigative journalism network managing pseudonyms better than dpr https b3rn3d herokuapp com blog 2014 01 08 managing pseudonyms by b3rn3d https b3rn3d herokuapp com did you ask him about his threat model https b3rn3d herokuapp com blog 2014 01 10 ask him about his threat model by b3rn3d https b3rn3d herokuapp com opsec required reading section https grugq github io blog 2013 11 06 required reading by grugq https github com grugq intelligence intelligence literature suggested reading list https www cia gov library intelligence literature by the united state s cia spycraft cia operative teaches spycraft and psychology https b3rn3d herokuapp com blog 2014 03 09 irl spycraft by b3rn3d https b3rn3d herokuapp com cryptocurrency related mycrypto s security guide for dummies and smart people too https medium com mycrypto mycryptos security guide for dummies and smart people too ab178299c82e by taylor monahan for developers defeating forensic analysis on unix https grugq github io docs phrack 59 06 txt by grugq https github com grugq private browsing tor https www torproject org you can use brave browser https brave com to open a private window with tor with tor you can browse dark web sites like hidden wiki http zqktlwiuavvvqqt4ybvgvi7tyo4hjl5xgfuvpdf6otjiycgwqbym2qad onion some deep web forums dread forum http dreadditevelidot onion privacy related useful services online payments privacy https privacy com private communications hushed https hushed com email protonmail https protonmail com thatoneprivacysite s email comparison https thatoneprivacysite net email section virtual private networks thatoneprivacysite s vpn comparison https thatoneprivacysite net vpn section software development talk about this topic with elopio https github com elopio books agile estimating and planning https www mountaingoatsoftware com books agile estimating and planning by mike cohn agile testing https www oreilly com library view agile testing a 9780321616944 by janet gregory and lisa crispin clean code https www oreilly com library view clean code 9780136083238 by robert c martin refactoring improving the design of existing code https martinfowler com books refactoring html by martin fowler succeeding with agile software development using scrum https www mountaingoatsoftware com books succeeding with agile software development using scrum by mike cohn test driven development by example https www oreilly com library view test driven development 0321146530 by kent beck user stories applied https www mountaingoatsoftware com books user stories applied by mike cohn working effectively with legacy code https www oreilly com library view working effectively with 0131177052 by michael feathers xunit test patterns https martinfowler com books meszaros html by gerard meszaros posts methodologies for measuring project health https nadiaeghbal com project health by nadia eghbal maintainers elopio https github com elopio tinchoabbate https github com tinchoabbate contributing share your awesome links with us https github com openzeppelin awesome openzeppelin issues new license cc0 http mirrors creativecommons org presskit buttons 88x31 svg cc zero svg https creativecommons org publicdomain zero 1 0 to the extent possible under law openzeppelin https openzeppelin com has waived all copyright and related or neighboring rights to this work | blockchain ethereum awesome-list openzeppelin | blockchain |
BLE-Watch | bluetooth le watch language https img shields io badge made 20with c blue svg https shields io img src https img shields io badge cmake 064f8c style for the badge logo cmake logocolor white height 20px build https github com charlesdias ble watch actions workflows build yml badge svg https github com charlesdias ble watch actions workflows build yml quality gate status https sonarcloud io api project badges measure project charlesdias ble watch metric alert status https sonarcloud io dashboard id charlesdias ble watch this is a sample project using the zephyr rtos and nordic nrf connect to create a ble watch this device is composed with nrf52840 dk https www nordicsemi com products development hardware nrf5340 dk nordic board oled monochrome displays ssd1306 128x64 pixels and the ds3231 real time clock rtc both display and rtc communicates with the nrf52840 mcu via i2c bus some topics covered tested on nrf52840 dk https www nordicsemi com products development hardware nrf5340 dk board embedded system with zephyr rtos https zephyrproject org and nrf connect sdk https www nordicsemi com products development software nrf connect sdk bluetooth low energy le technology the device works as gap peripheral and gatt server implemented ble services officially adopted by bluetooth sig and custom services too continuous integration ci with github actions as sonarcloud integration use of docker container img src docs images project gif alt drawing width 600 bluetooth services device information service dis exposes the device information device information service uuid 0x180a characteristic model number string uuid 0x2a24 data format string properties read characteristic manufacturer name string uuid 0x2a29 data format string properties read characteristic serial number string uuid 0x2a25 data format string properties read characteristic firmware revision string uuid 0x2a26 data format string properties read characteristic hardware revision string uuid 0x2a27 data format string properties read characteristic software revision string uuid 0x2a28 data format string properties read battery service bas simulates the battery level voltage battery service uuid 0x180f characteristic battery level uuid 0x2a19 data format uint8 1 byte percentage battery level properties notify read custom service receives messages to be shown on the display screen unknown service uuid 3c134d60 e275 406d b6b4 bf0cc712cb7c characteristic unknown uuid 3c134d61 e275 406d b6b4 bf0cc712cb7c data format text utf 8 limit up to 31 characters properties read write project structure text app inc device information service h display ssd1306 h gatt central h rtc ds3231 h src device information service c display ssd1306 c gatt central c rtc ds3231 c build nrf52840dk cmakelists txt docs assigned numbers pdf ds3231 pdf dts v1 0 pdf kconfig makefile nrf52840dk nrf52840 overlay prj conf readme md sample yaml src main c building and running building with docker image on interactive mode access the project folder console cd ble watch and run the docker image console docker run rm it v pwd workdir project w workdir project charlesdias nrfconnect sdk bin bash after that run the command below to build the firmware console make build running test the ble watch application with the nrf connect app which is available for ios app store and android google play ble watch docs images ble watch gif next improvements create dedicate task to update the display implement bluetooth service to set the date and time flash the application project running west on docker container add doxygen configuration | ble nordic zephyr-rtos nrf52840 github-actions docker-image | os |
ase-boilerplate | this code is used for the purposes of coms 4156 advanced software engineering course at columbia university new york this is a boilerplate python flask code along with configurations for circle ci and google cloud setup instructions please log into github before progressing with the next steps step 1 first things first grab your free google credits by following a href www google com this post a in piazza step 2 fork fork this repository into your github from now on we will be working on your forked repository how to fork br look out for a button called fork in the top right of this page it will create a replica of this repository and put it in your github account step 3 clone open termnal go to any folder clone your forked repository into your local folder how to clone br look out for a button called clone in the top right of the repository once you click that you will get a link like this https github com your name your repository name git copy that and go to your folder and type the command br git clone your forked repository git url step 4 set repository as origin now set this repository as your origin how br git remote add origin your forked repository git url step 5 set up google cloud project app engine login into a href https cloud google com google cloud a using the account that you used for free credits note if you signed in using another account switch account to the one that you used to grab free credits 1 if you are using google cloud first time it will ask you to accept some terms please do so 2 on the top left click on select a project if you have already used google cloud before and you have also created a project before then you may see another project name there even then click on it 3 it will open a small pane ensure that organization is columbia edu if not then select that 4 then click on button 4 give a project name say in this case ase boilerplate 5 also edit the project id and set it also to ase boilerplate other wise google adds some randome digits to the project id and we need this project id through out so it is better if project id and project names are same 6 click create 7 search for app engine create one step 6 set up circle ci 1 go to a href https circleci com circleci a 2 sign up for a free account you can login using github easy way else you can signup and later authorize github account 3 click on projects on the left pane add project setup project note it might take sometime to sync the projects in your github into the circle ci 4 choose the following operating system linux platform 2 0 language python 5 click start building step 7 enable app engine admin api retrieve client secret 1 click on console 2 in the search bar search for google app engine admin api click enable 3 click on credentials click on create credential select service account key 4 from the drop down of service account select compute engine default service account 5 for the key type select json 6 click create 7 a json file with name similar to ase boilerplate some number json will be downloaded on to your system remember the folder it is downloaded to we need this later step 8 setup client secret in circle ci 1 go to a href https circleci com circleci a click on app 2 click on projects 3 click on little gear notation for settings that is next to your project which is ase boilerplate 4 click on environment variables 5 click on add variable 6 for name client secret 7 now go to the folder where json file is downloaded previously and run this command br base64 ase boilerplate some number json pbcopy br it encodes the file into base64 format and copies into your clipboard 9 now go back to browser and for value press the paste buttons cmd v on mac ctrl v on windows step 9 update circle yml file with google cloud project id in your local repository update circle yml file by replacing the your gcloud project id at two places with your project id from google cloud then git add circle yml br git commit m updating circle yml with project id br git push origin master br how do i find my project id br 1 go to a href https cloud google com google cloud a and on top left select the project 2 it opens a new pane in that look for the project id corresponding to the project name you gave in our case it is ase boilerplate step 10 verify build in circleci step 8 would trigger a new build and release in circle ci to verify 1 go to projects your project open the lates build which will be like master some number 2 if everything is good build will succeed and the app will be deployed into google app engine 3 you can also verify that by going to https your gcloud project id appspot com which will show a message like starting with hello step 11 setup google cloud sdk app engine sdk in your local system 1 following this link and perform the 3 steps under the interactive installer section corresponging to your operating system for our case it is mac os vice versa you can choose linux windows based on your os br a href https cloud google com sdk downloads interactive google cloud sdk a 2 following this link a href https cloud google com appengine docs standard python download app engine extension for python a and perform the steps below and skip if you already have it 1st step check your python version by typing python version in you terminal shell if it is python 2 7 then skip the step else check out optional step 12 in this file 3rd step perform this step 4th step check if you have git installed by typing git if yes then skip else perfrom this step 5th step perform this step 3 in your local system cd your repository in our case cd ase boilerplate br 4 mkdir t lib 5 pip install r requirements txt t lib 6 dev appserver py 7 now you will be running the application locally in locally mimicked app engine framework so now checkout a href http localhost 8080 localhost 8080 a you should be greeted with hello message 8 after you are done press ctrl c to stop the local server step 12 optional setup anaconda if you don t have python 2 7 in your system then follow along 1 download a href https www anaconda com download macos anaconda a with python version 3 6 don t get confused about 3 6 here we will be creating virtual environment with 2 7 instead of using anaconda with python 2 7 version which is a good practice 2 install anaconda by double clicking the dmg file 3 after installation terminal conda create n python2env python 2 7 4 source activate python2env 5 at this point you have activated virtual environment named python2env with packages python 2 7 so now if you test python version you will see python 2 7 as output 6 at this point continue your previous work with this virtual environment active 7 once you are done deactivate the environment with source deactivate 8 fun check try python version now it will show your default python version installed in your system licensing copyright c 2018 columbia university | continuous-integration continous-deployment circleci googlecloud gcloud appengine flask columbia-university coms4156 google-cloud | cloud |
mst | mst information about mean stack technologies soc 3 | server |
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penjs | penjs logo penjs png npm version npm image npm url build status travis image travis url coverage status coverage image coverage url mobile web small development framework api constructer js function penjs element options example html div id app script type text jhtmls var info createcount 0 destroycount 0 h1 jhtmls title time h1 h3 bind info create info createcount destroy info destroycount h3 var asc 1 button tap now now button button tap ajax ajax button button tap sort items asc asc sort button button tap push items push button ul bind items items foreach function item li bind item create info createcount destroy info destroycount if item editing jhtmls input type text value item title create this focus keydown enter this blur focusout item title this value item editing false else span dblclick item editing jhtmls item title span button tap remove items item remove button li ul script div license mit zswang http weibo com zswang npm url https npmjs org package penjs npm image https badge fury io js penjs svg travis url https travis ci org zswang penjs travis image https travis ci org zswang penjs svg branch master coverage url https coveralls io github zswang penjs branch master coverage image https coveralls io repos zswang penjs badge svg branch master service github | mobile-web framework mvvm | front_end |
machine-learning-yearning | readme ng 58 http www mlyearning org http www mlyearning org update 2018 02 02 1 14 done update 2018 04 25 ng 15 19 done tips 12 13 13 build your first system quickly then iterate chapter13 14 chapter14 15 update 2018 05 02 20 22 done update 2018 05 09 23 27 done update 2018 05 16 28 30 done update 2018 05 23 31 32 done update 2018 05 30 33 35 done update 2018 06 06 36 39 done update 2018 06 13 40 43 done update 2018 06 20 44 46 done update 2018 06 27 47 49 done update 2018 07 04 50 52 done update 2018 09 29 53 58 done 58 10 setting up development and test sets basic error analysis bias and variance learning curves comparing to human level performance training and testing on different distributions debugging inference algorithms end to end deep learning error analysis by parts conclusion gitbooks machine learning yearning https xiaqunfeng gitbooks io machine learning yearning content draft 01 14 ng mly01 01 14 pdf draft ng mly01 01 14 pdf 15 19 ng mly02 15 19 pdf draft ng mly02 15 19 pdf 20 22 ng mly03 20 22 pdf draft ng mly03 20 22 pdf 23 27 ng mly04 23 27 pdf draft ng mly04 23 27 pdf 28 30 ng mly05 28 30 pdf draft ng mly05 28 30 pdf 31 32 ng mly06 31 32 pdf draft ng mly06 31 32 pdf 33 35 ng mly07 33 35 pdf draft ng mly07 33 35 pdf 36 39 ng mly08 36 39 pdf draft ng mly08 36 39 pdf 40 43 ng mly09 40 43 pdf draft ng mly09 40 43 pdf 44 46 ng mly10 44 46 pdf draft ng mly10 44 46 pdf 47 49 ng mly11 47 49 pdf draft ng mly11 47 49 pdf 50 52 ng mly12 50 52 pdf draft ng mly12 50 52 pdf 53 58 ng mly13 53 58 pdf draft ng mly13 53 58 pdf | ai |
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fxrtos-examples | fxrtos examples fx rtos demonstation examples for different platforms build instructions 1 download and extract example project from the links in table below 2 look at readme file 3 optionally edit example code in app directory 4 open project from appropriate directory name for your ide or run build bat make command to build the firmware in command line prebuilt projects the following are references to ready to run projects for most popular mcus architecture manufacturer part no board link m0 milandr 1986 1 ldm ldm bb fxrtos milandr k1986ve1 zip https github com eremex fxrtos examples releases download v0 1 fxrtos milandr k1986ve1 zip m3 milandr 1986 92 ldm ldm bb fxrtos milandr k1986ve92 zip https github com eremex fxrtos examples releases download v0 1 fxrtos milandr k1986ve92 zip m4f niiet 1921 035 ldm bb k1921bk035 fxrtos niiet k1921vk035 zip https github com eremex fxrtos examples releases download v0 1 fxrtos niiet k1921vk035 zip m33 elvees 1892 268 eliot1 mo fxrtos elvees eliot zip https github com eremex fxrtos examples releases download v0 1 fxrtos elvees eliot zip m4f stm stm32f411 blackpill fxrtos st stm32f411 zip https github com eremex fxrtos examples releases download v0 1 fxrtos st stm32f411 zip m4f stm stm32f302 nucleo 64 fxrtos st stm32f302 zip https github com eremex fxrtos examples releases download v0 1 fxrtos st stm32f302 zip m7f stm stm32f746 nucleo 144 fxrtos st stm32f746 zip https github com eremex fxrtos examples releases download v0 1 fxrtos st stm32f746 zip rv32imc milandr 1986 025 demoboard fxrtos milandr k1986vk025 zip https github com eremex fxrtos examples releases download v0 1 fxrtos milandr k1986vk025 zip rv32imac gigadevice gd32vf103 longan nano fxrtos gigadevice gd32vf103 zip https github com eremex fxrtos examples releases download v0 1 fxrtos gigadevice gd32vf103 zip rv32imac wch ch32v307 ch32v307v r1 fxrtos wch ch32v307 zip https github com eremex fxrtos examples releases download v0 1 fxrtos wch ch32v307 zip prebuilt fx rtos static libraries are available at releases page https github com eremex fxrtos lite releases | fx-rtos demonstation | os |
Img2Prompt | evaluation code of img2prompt from images to textual prompts zero shot vqa with frozen large language models img src illustration png width 700 img src questiongeneration png width 700 img src caption png width 700 this is the eveluation code for a href https arxiv org abs 2212 10846 img2prompt vqa paper a we public it evaluation codes demo we include an interactive demo colab notebook https colab research google com github salesforce lavis blob main projects img2prompt vqa img2prompt vqa ipynb to show img2prompt vqa inference workflow 1 image question matching compute the relevancy score of the image patches wrt the question and remove the generated noisy captions with low relevancy score 2 image captioning generate question guided captions based on the relevancy score 3 question generation generate questions based on the synthetic answers and captions 4 large language model pre trained lagre language models e g opt gpt 3 zero shot evaluation table thead tr th rowspan 2 model th th rowspan 2 end to end training th th colspan 1 vqav2 val th th colspan 1 vqav2 test th th colspan 1 ok vqa test th th colspan 1 aok vqa val th th colspan 1 aok vqa test th tr thead tbody tr td frozen 7b td td td td 29 5 td td td td 5 9 td td td td td tr tr td flamingo 9b td td td td td td 51 8 td td 44 7 td td td td td tr tr td flamingo 80b td td td td td td 56 3 td td 50 6 td td td td td tr tr td img2prompt vqa opt sub 13b sub td td x td td 57 1 td td 57 3 td td 39 9 td td 33 3 td td 33 0 td tr tr td img2prompt vqa opt sub 30b td td x td td 59 5 td td 60 4 td td 41 8 td td 36 9 td td 36 0 td tr tr td img2prompt vqa opt sub 66b td td x td td 59 9 td td 60 3 td td 43 2 td td 38 7 td td 38 2 td tr tr td img2prompt vqa opt sub 175b td td x td td 60 6 td td 61 9 td td 45 6 td td 42 9 td td 40 7 td tr tbody table to reproduce these evaluation results of img2llm vqa with different llms you can follow the next steps firstly you should download the generated caption question files from this link https drive google com drive folders 1kbbrwtac5yug b6cvewm4jywpr ybceo usp sharing and put them in the caption question files folder for example you can download okvqa question json okvqa caption json and okvqa ans to cap dict json for reproducing results of okvqa results then download the 2014 coco val anotation file in link https cocodataset org download url and put it in annotation new folder then you can run the shell in folder vl captioning to reproduce results e g run okvqa sh citation if you find this code to be useful for your research please consider citing article guo2022images title from images to textual prompts zero shot vqa with frozen large language models author guo jiaxian and li junnan and li dongxu and tiong anthony meng huat and li boyang and tao dacheng and hoi steven ch journal arxiv preprint arxiv 2212 10846 year 2022 | ai |
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GD32F103_FreeRTOS_GCC | gd32f103 freertos gcc make clean make | os |
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CS301-MCU-BlueTooth-Communication | cs301 mcu bluetooth communication arm embedding system programming design bluetooth module configuration | os |
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Natural-Language-Processing-with-AWS-AI-Services | natural language processing with aws ai services a href https www packtpub com product natural language processing with aws ai services 9781801812535 utm source github utm medium repository utm campaign 9781801812535 img src https static packt cdn com products 9781801812535 cover smaller alt natural language processing with aws ai services height 256px align right a this is the code repository for natural language processing with aws ai services https www packtpub com product natural language processing with aws ai services 9781801812535 utm source github utm medium repository utm campaign 9781801812535 published by packt derive strategic insights from unstructured data with amazon textract and amazon comprehend what is this book about the book includes python code examples for amazon textract amazon comprehend and other aws ai services to build a variety of serverless nlp workflows at scale with little prior machine learning knowledge packed with real life business examples this book will help you to navigate a day in the life of an aws ai specialist with ease this book covers the following exciting features automate various nlp workflows on aws to accelerate business outcomes use amazon textract for text tables and handwriting recognition from images and pdf files gain insights from unstructured text in the form of sentiment analysis topic modeling and more using amazon comprehend set up end to end document processing pipelines to understand the role of humans in the loop develop nlp based intelligent search solutions with just a few lines of code create both real time and batch document processing pipelines using python if you feel this book is for you get your copy https www amazon com dp 1801812535 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 the code will look like the following define iam role role get execution role print rolearn format role sess sagemaker session s3bucketname your s3 bucket name prefix chapter5 following is what you need for this book if you re an nlp developer or data scientist looking to get started with aws ai services to implement various nlp scenarios quickly this book is for you it will show you how easy it is to integrate ai in applications with just a few lines of code a basic understanding of machine learning ml concepts is necessary to understand the concepts covered experience with jupyter notebooks and python will be helpful with the following software and hardware list you can run all code files present in the book chapter 1 18 software and hardware list software required os required access and signing up to an aws account windows mac os x and linux any creating a sagemaker jupyter notebook windows mac os x and linux any creating an amazon s3 bucket 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 9781801812535 colorimages pdf the code in action videos for this book can be viewed at https bit ly 3vpvdkj related products other books you may enjoy machine learning with amazon sagemaker cookbook packt https www packtpub com product machine learning with amazon sagemaker cookbook 9781800567030 utm source github utm medium repository utm campaign 9781800567030 amazon https www amazon com dp 1800567030 amazon sagemaker best practices packt https www packtpub com product amazon sagemaker best practices 9781801070522 utm source github utm medium repository utm campaign 9781801070522 amazon https www amazon com dp 1801070520 get to know the authors mona m is a senior ai ml specialist solutions architect at aws she is a highly skilled it professional with more than 10 years experience in software design development and integration across diverse work environments as an aws solutions architect her role is to ensure customer success in building applications and services on the aws platform she is responsible for crafting a highly scalable flexible and resilient cloud architecture that addresses customer business problems she has published multiple blogs on ai and nlp on the aws ai channel along with research papers on ai powered search solutions premkumar rangarajan is an enterprise solutions architect specializing in ai ml at amazon web services he has 25 years of experience in the it industry in a variety of roles including delivery lead integration specialist and enterprise architect he has significant architecture and management experience in delivering large scale programs across various industries and platforms he is passionate about helping customers solve ml and ai problems 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 9781801812535 https packt link free ebook 9781801812535 a p | ai |
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Data-Analysis-and-Machine-Learning-Projects | python 2 7 https img shields io badge python 2 7 blue svg python 3 5 https img shields io badge python 3 5 blue svg license https img shields io badge license mit 20license blue svg randy olson s data analysis and machine learning projects 2016 current randal s olson this is a repository of teaching materials code and data for my data analysis and machine learning projects each repository will usually correspond to one of the blog posts on my web site http www randalolson com blog be sure to check the documentation usually in ipython notebook format in the directory you re interested in for the notes on the analysis data usage terms etc if you don t have the necessary software installed to run ipython notebook don t fret you can use nbviewer http nbviewer ipython org to view a notebook on the web for example if you want to view the notebook in the wheres waldo path optimization directory copy the full link https github com rhiever data analysis and machine learning projects blob master wheres waldo path optimization where s 20waldo 20path 20optimization ipynb to the notebook then paste it into nbviewer http nbviewer ipython org github rhiever data analysis and machine learning projects blob master wheres waldo path optimization where 27s 20waldo 20path 20optimization ipynb license instructional material all instructional material in this repository is made available under the creative commons attribution license https creativecommons org licenses by 4 0 the following is a human readable summary of and not a substitute for the full legal text of the cc by 4 0 license https creativecommons org licenses by 4 0 legalcode you are free to share copy and redistribute the material in any medium or format adapt remix transform and build upon the material for any purpose even commercially the licensor cannot revoke these freedoms as long as you follow the license terms under the following terms attribution you must give appropriate credit mentioning that your work is derived from work that is randal s olson and where practical linking to http www randalolson com provide a link to the license https creativecommons org licenses by 4 0 and indicate if changes were made you may do so in any reasonable manner but not in any way that suggests the licensor endorses you or your use no additional restrictions you may not apply legal terms or technological measures that legally restrict others from doing anything the license permits notices you do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation no warranties are given the license may not give you all of the permissions necessary for your intended use for example other rights such as publicity privacy or moral rights may limit how you use the material software except where otherwise noted the example programs and other software provided in this repository are made available under the osi http opensource org approved mit license http opensource org licenses mit license html 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 | machine-learning python data-analysis data-science ipython-notebook evolutionary-algorithm | ai |
ML | ml machine learning inaction you will see the whole code of machine learning inaction in here and the version of python i am using is python 3 6 2 amd64 | ai |
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nlptextclassification | nlptextclassification this is the source code for the natural language processing for text classification with nltk amp scikit learn video | ai |
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pwa-studio | coverage status https coveralls io repos github magento pwa studio badge svg branch develop https coveralls io github magento pwa studio branch develop pwa studio magento pwa studio is a collection of tools that lets developers build complex progressive web applications on top of magento 2 stores contributions are you interested in contributing to the pwa studio project check out the community wiki to learn how to contribute to pwa studio if you are looking for an issue to work on visit our community backlog board and look at the ready for development column for more information about contributing to this repository see the contribution guide useful links pwa studio documentation site documentation site the best place to start learning about the tools and the technologies that pwa studio provides here you can learn pwa studio concepts find api reference docs and read tutorials on how to use pwa studio to create your own pwa storefront here are some popular topics to help you get started pwa studio overview a high level overview of pwa studio and what it provides to developers pwa studio beginner guide https www youtube com watch v n8c87kqt bg tools and libraries a list of tools and libraries developers need to be familiar with to use pwa studio pwa studio fundamentals a series of tutorials covering common storefront development tasks pwa studio best practices https developer adobe com commerce pwa studio guides best practices venia venia https raw githubusercontent com wiki magento pwa studio images venia png venia venia is a magento pwa storefront created and powered by pwa studio tools and libraries developers can use venia as a reference storefront for their own projects or as a starting point for customization about this repository to facilitate local development testing and versioning pwa studio is structured as a monorepo using yarn workspaces packages in this repository are independently published to npm install individual packages as needed instead of installing the entire pwa studio project as a dependency of your project note if you are installing the whole pwa studio monorepo please be aware that the project uses yarn workspaces and does not support npm install please use yarn install instead packages this repository includes the following packages peregrine https developer adobe com commerce pwa studio guides packages peregrine a component library for adding logic to visual components venia ui a library of visual components for pwa storefront projects venia concept a concept storefront project built using pwa studio tools pwa buildpack https developer adobe com commerce pwa studio guides packages buildpack a tooling library to help with pwa storefront development upward spec https developer adobe com commerce pwa studio guides packages upward upward specification and test suite upward js https developer adobe com commerce pwa studio guides packages upward javascript a reference implementation of the upward specification babel preset peregrine a babel preset plugin that is required to use peregrine components graphql cli validate magento pwa queries a script to validate your project s graphql queries against a schema pwa devdocs pwa devdocs project source for the documentation site if you have an issue that cannot be resolved please create an issue pwa studio ui kit for adobe xd adobe xd makes handoff between designers and engineers more efficient through easy to use collaboration tools the pwa studio ui kit contains a collection of templates and components compatible with adobe commerce ui kit https raw githubusercontent com wiki magento pwa studio images xd ui kit png join the conversation if you have any project questions concerns or contribution ideas join our pwa slack channel here you can find a public calendar with events that magento pwa team runs with community you can also add that calendar to your calendar app to stay up to date with the changes and get notifications community maintainers a community maintainer is a point of contact from the community approved by the core team to help with community outreach and project administration the following members are the community maintainers for this project larsroettig image larsroettig jordaneisenburger image jordaneisenburger jordaneisenburger https github com jordaneisenburger jordaneisenburger image https avatars0 githubusercontent com u 19858728 v 4 s 60 larsroettig https github com larsroettig larsroettig image https avatars0 githubusercontent com u 5289370 v 4 s 60 top community contributors the pwa studio project welcomes all codebase and documentation contributions we would like to recognize the following community members for their efforts on improving the pwa studio project in our latest release author commits added lines removed lines avg files justin conabree 94 15932 4837 7 117 mikha l bois 46 7675 2000 12 226 pankhuri goel 28 11402 9343 13 606 lars roettig 15 2350 862 8 067 pedro chiossi 9 9063 7625 38 571 allan yanik 6 834 677 25 sofia hernandez 6 781 145 21 oleksandr krasko 2 193 1 4 antoine fontaine 1 44 45 14 shikha mishra 1 584 5 13 small last updated october 14 2021 small source statistic magento engineering statistic magento engineering https statistic magento engineering app kibana dashboard fe6a4960 8adf 11ea b035 e1712195ddd1 g filters refreshinterval pause t value 0 time from 2021 08 01t05 00 00 000z mode absolute to 2021 10 14t18 14 39 005z a description custom 20overview 20panel 20by 20magento filters state store appstate meta alias empty 20commits disabled f index git key files negate t params query 0 type phrase type phrase value 0 query match files query 0 type phrase state store appstate meta alias bots disabled f index github issues key author bot negate t params query t type phrase type phrase value true query match author bot query t type phrase state store appstate meta alias n disabled f index 0211efb0 14ca 11e9 8aac ef7fd4d8cbad key author domain negate t params magento com adobe com type phrases value magento com 20adobe com query bool minimum should match 1 should match phrase author domain magento com match phrase author domain adobe com state store appstate meta alias n disabled f index git key author name negate t params revanth 20kumar 20annavarapu revanth 20kumar devagouda dependabot 5bbot 5d jimbo tommy 20wiebell stephen 20rugh anthoula 20wojczak james 20calcaben andy 20terranova hwashiang 20 michael 20yu bruce 20denham oleksandr 20rykh type phrases value revanth 20kumar 20annavarapu 20revanth 20kumar 20devagouda 20dependabot 5bbot 5d 20jimbo 20tommy 20wiebell 20stephen 20rugh 20anthoula 20wojczak 20james 20calcaben 20andy 20terranova 20hwashiang 20 michael 20yu 20bruce 20denham 20oleksandr 20rykh query bool minimum should match 1 should match phrase author name revanth 20kumar 20annavarapu match phrase author name revanth 20kumar match phrase author name devagouda match phrase author name dependabot 5bbot 5d match phrase author name jimbo match phrase author name tommy 20wiebell match phrase author name stephen 20rugh match phrase author name anthoula 20wojczak match phrase author name james 20calcaben match phrase author name andy 20terranova match phrase author name hwashiang 20 michael 20yu match phrase author name bruce 20denham match phrase author name oleksandr 20rykh fullscreenmode f options darktheme f usemargins t panels embeddableconfig title commits vis legendopen f griddata h 8 i 2 w 16 x 0 y 36 id git evolution commits panelindex 2 title git 20commits type visualization version 6 8 6 embeddableconfig title github 20issues griddata h 8 i 31 w 24 x 0 y 28 id github issues main metrics panelindex 31 title github 20issues type visualization version 6 8 6 embeddableconfig title github 20issues vis legendopen f griddata h 8 i 32 w 24 x 0 y 20 id github issues evolutionary panelindex 32 title github 20issues type visualization version 6 8 6 embeddableconfig title github 20issues 20submitters griddata h 8 i 33 w 16 x 32 y 36 id github issues evolutionary submitters panelindex 33 title github 20issues 20submitters type visualization version 6 8 6 embeddableconfig title github 20pull 20requests griddata h 8 i 34 w 24 x 24 y 28 id github pullrequests main metrics panelindex 34 title github 20pull 20requests type visualization version 6 8 6 embeddableconfig title pull 20requests vis legendopen f griddata h 8 i 35 w 24 x 24 y 20 id github pullrequests pullrequests panelindex 35 title github 20pull 20requests type visualization version 6 8 6 embeddableconfig title pull 20request 20submitters vis legendopen f griddata h 8 i 36 w 16 x 16 y 36 id github pullrequests submitters evolutionary panelindex 36 title github 20pull 20request 20submitters type visualization version 6 8 6 embeddableconfig title git 20top 20authors vis params config searchkeyword sort columnindex n direction n griddata h 20 i 111 w 24 x 0 y 0 id git overview top authors panelindex 111 title top 20code 20contributors type visualization version 6 8 6 embeddableconfig title vis params config searchkeyword sort columnindex 1 direction desc griddata h 20 i 114 w 24 x 24 y 0 id f747c010 9041 11ea b035 e1712195ddd1 panelindex 114 title magento 20projects type visualization version 6 8 6 query language lucene query pwa studio timerestore f title overview viewmode view contribution guide github contributing md coverage status https coveralls io repos github magento pwa studio badge svg branch main create an issue https github com magento pwa studio issues new documentation site https developer adobe com commerce pwa studio git hook https git scm com book en v2 customizing git git hooks npm https www npmjs com org magento selective dependency resolutions https yarnpkg com lang en docs selective version resolutions troubleshooting https developer adobe com commerce pwa studio troubleshooting venia storefront setup https developer adobe com commerce pwa studio tutorials setup storefront pwa studio fundamentals https developer adobe com commerce pwa studio tutorials setup storefront workspace commands https yarnpkg com en docs cli workspace yarn workspaces https yarnpkg com en docs workspaces community wiki https github com magento pwa studio wiki pwa studio overview https developer adobe com commerce pwa studio guides tools and libraries https developer adobe com commerce pwa studio guides project tools libraries venia storefront setup https developer adobe com commerce pwa studio tutorials setup storefront project coding standards and conventions https github com magento pwa studio wiki project coding standards and conventions community backlog board https github com magento pwa studio projects 1 pwa slack channel https magentocommeng slack com messages c71hnkys2 babel https babeljs io venia https venia magento com public calendar https opensource magento com community calendar add that calendar https calendar google com calendar ical sn3me3pduhd92hhk9s7frkn57o 40group calendar google com public basic ics pwa studio ui kit https developer adobe com commerce xd kits | pwa-studio pwa-storefront venia magento pwa | front_end |
VkCup2022 | vkcup2022 vk cup for 2022 mobile development first round zen apk task the zen mobile app shows users a feed of the most interesting posts for first time users entering the application we show a selection of many categories that they might like design a selection screen for interesting categories below is a layout of the basic version of such a screen develop a single screen mobile application where the user can choose interesting topics layouts in figma will help you navigate https vk cc cjh0ho screenshots p align center img src img zenlogo jpg alt size limit cli width 250 img src img zenstart jpg alt size limit cli width 250 img src img zenchecked jpg alt size limit cli width 250 p second round start 4 january at 10 00 | front_end |
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esp-open-rtos | esp open rtos a community developed open source freertos http www freertos org based framework for esp8266 wifi enabled microcontrollers intended for use in both commercial and open source projects originally based on but substantially different from the espressif iot rtos sdk https github com espressif esp8266 rtos sdk resources build status https travis ci org superhouse esp open rtos svg branch master https travis ci org superhouse esp open rtos email discussion list https groups google com d forum esp open rtos irc channel esp open rtos on freenode web chat link http webchat freenode net channels 23esp open rtos uio d4 github issues list bugtracker https github com superhouse esp open rtos issues please note that this project is released with a contributor code of conduct https github com superhouse esp open rtos blob master code of conduct md by participating in this project you agree to abide by its terms quick start install esp open sdk https github com pfalcon esp open sdk build it with make toolchain esptool libhal standalone n then edit your path and add the generated toolchain bin directory the path will be something like path to esp open sdk xtensa lx106 elf bin despite the similar name esp open sdk has different maintainers but we think it s fantastic other toolchains may also work as long as a gcc cross compiler is available on the path and libhal and libhal headers are compiled and available to gcc the proprietary tensilica xcc compiler will probably not work install esptool py https github com themadinventor esptool and make it available on your path if you used esp open sdk then this is done already the esp open rtos build process uses gnu make and the utilities sed and grep if you built esp open sdk then you have these already use git to clone the esp open rtos project note the recursive git clone recursive https github com superhouse esp open rtos git cd esp open rtos to build any examples that use wifi create include private ssid config h defining the two macro defines c define wifi ssid mywifissid define wifi pass my secret password build an example project found in the examples directory and flash it to a serial port make flash j4 c examples http get espport dev ttyusb0 run make help c examples http get for a summary of other make targets note the c option to make is the same as changing to that directory then running make the build process wiki page https github com superhouse esp open rtos wiki build process has in depth details of the build process goals provide professional quality framework for wifi enabled rtos projects on esp8266 open source code for all layers above the mac layer ideally lower layers if possible this is a work in progress see issues list https github com superhouse esp open rtos issues leave upstream source clean for easy interaction with upstream projects flexible build and compilation settings current status is alpha quality actively developed ap station mode ie wifi client mode and udp tcp client modes are tested other functionality should work contributors and testers are welcome code structure examples contains a range of example projects one per subdirectory check them out include contains header files from espressif rtos sdk relating to the binary libraries xtensa core core contains source headers for low level esp8266 functions peripherals core include esp contains useful headers for peripheral access etc minimal to no freertos dependencies extras is a directory that contains optional components that can be added to your project most extras components will have a corresponding example in the examples directory extras include mbedtls mbedtls https tls mbed org is a tls ssl library providing up to date secure connectivity and encryption support i2c software i2c driver upstream project https github com kanflo esp open rtos driver i2c rboot ota ota support over the air updates including a tftp server for receiving updates for rboot by raburton http richard burtons org 2015 05 18 rboot a new boot loader for esp8266 bmp180 driver for digital pressure sensor upstream project https github com angus71 esp open rtos driver bmp180 freertos contains freertos implementation subdirectory structure is the standard freertos structure freertos source portable esp8266 contains the esp8266 port lwip contains the lwip tcp ip library see third party libraries https github com superhouse esp open rtos wiki third party libraries wiki page for details libc contains the newlib libc libc details here https github com superhouse esp open rtos wiki libc configuration open source components freertos http www freertos org v10 2 0 lwip http lwip wikia com wiki lwip wiki v2 0 3 with some modifications https github com ourairquality lwip newlib https github com ourairquality newlib v3 0 0 with patches for xtensa support and locking stubs for thread safe operation on freertos for details of how third party libraries are integrated see the wiki page https github com superhouse esp open rtos wiki third party libraries binary components binary libraries inside the lib dir are all supplied by espressif as part of their rtos sdk these parts were mit licensed as part of the esp open rtos build process all binary sdk symbols are prefixed with sdk this makes it easier to differentiate binary open source code and also prevents namespace conflicts espressif s rtos sdk provided a libssl based on axtls this has been replaced with the more up to date mbedtls library see below some binary libraries appear to contain unattributed open source code libnet80211 a libwpa a appear to be based on freebsd net80211 wpa or forks of them see this issue https github com superhouse esp open rtos issues 4 libudhcp has been removed from esp open rtos it was released with the espressif rtos sdk but udhcp is gpl licensed licensing bsd license as described in license applies to original source files lwip http lwip wikia com wiki lwip wiki lwip is copyright c swedish institute of computer science freertos since v10 is provided under the mit license license details in files under freertos dir freertos is copyright c amazon source binary components from the espressif iot rtos sdk https github com espressif esp iot rtos sdk were released under the mit license source code components are relicensed here under the bsd license the original parts are copyright c espressif systems newlib is covered by several copyrights and licenses as per the files in the libc directory mbedtls https tls mbed org is provided under the apache 2 0 license as described in the file extras mbedtls mbedtls apache 2 0 txt mbedtls is copyright c arm limited components under extras may contain different licenses please see those directories for details contributions contributions are very welcome if you find a bug please raise an issue to report it https github com superhouse esp open rtos issues if you have feature additions or bug fixes then please send a pull request there is a list of outstanding enhancements in the issues list https github com superhouse esp open rtos issues contributions to these as well as other improvements are very welcome if you are contributing code please ensure that it can be licensed under the bsd open source license specifically code from espressif iot sdk cannot be merged as it is provided under either the espressif general public license or the espressif mit license which are not compatible with the bsd license recent releases of the espressif iot rtos sdk cannot be merged as they changed from mit license to the espressif mit license which is not bsd compatible the espressif binaries used in esp open rtos were taken from revision ec75c85 as this was the last mit licensed revision https github com espressif esp8266 rtos sdk commit 43585fa74550054076bdf4bfe185e808ad0da83e for code submissions based on reverse engineered binary functionality please either reverse engineer functionality from mit licensed espressif releases or make sure that the reverse engineered code does not directly copy the code structure of the binaries it cannot be a derivative work of an incompatible binary the best way to write suitable code is to first add documentation somewhere like the esp8266 reverse engineering wiki http esp8266 re foogod com describing factual information gained from reverse engineering such as register addresses bit masks orders of register writes etc then write new functions referring to that documentation as reference material coding style for new contributions in c please use bsd style and indent using 4 spaces for assembly please use the following instructions indented using 8 spaces inline comments use as a comment delimiter comments on their own line s use first operand of each instruction should be vertically aligned where possible for xtensa special registers prefer wsr ax sr over wsr sr ax if you re an emacs user then there is a dir locals el file in the root which configures cc mode and asm mode you will need to approve some variable values as safe see also the additional comments in dir locals el if you re editing assembly code upstream code is left with the indentation and style of the upstream project sponsors work on parts of esp open rtos has been sponsored by superhouse automation http superhouse tv | os |
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IT-tips | it tips tips in information technology | server |
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BlogingApp | blogingapp backend development for blogging app api which contains api s about creating a user logging in of a user post and adding comments on post using spring boot application structure https user images githubusercontent com 61879886 216837477 2ca59159 4584 43eb 86f9 535de9d1f2e6 png 2459546 middle https user images githubusercontent com 61879886 216837618 bb0e27ae 7cca 49be 9aca 2c757db177da png | server |
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mslearn-aml-labs | page type sample languages python products azureml sdk description hands on labs for content on microsoft learn urlfragment https github com microsoftdocs mslearn aml labs microsoft learn azure machine learning labs important notice this repo has been replaced by a new repo https aka ms mslearn dp100 we re making this change to consolidate the labs used in the dp 100 instructor led course https docs microsoft com learn certifications courses dp 100t01 and the self paced exercises in the equivalent online modules on microsoft learn https docs microsoft com learn paths build ai solutions with azure ml service update labs to reflect recent changes in the azure machine learning service and sdk add new labs on topics related to responsible machine learning guidelines on readme format https review docs microsoft com help onboard admin samples concepts readme template branch master guidance on onboarding samples to docs microsoft com samples https review docs microsoft com help onboard admin samples process onboarding branch master taxonomies for products and languages https review docs microsoft com new hope information architecture metadata taxonomies branch master this repository contains the files necessary for hands on labs to support content on microsoft learn https docs microsoft com learn paths build ai solutions with azure ml service contents file folder description data sample data for use in the labs labdocs the lab exercise instructions nn xxxx xx ipynb lab code notebooks gitignore define what to ignore at commit time changelog md list of changes to the labs readme md this readme file license the license for this repo security md security guidance prerequisites azure machine learning azure ml is a microsoft azure based service for running data science and machine learning workloads at scale in the cloud to use azure machine learning you will need an azure subscription if you do not already have one you can sign up for a free trial at https azure microsoft com https azure microsoft com setup you must follow the instructions in the first lab getting started with azure machine learning labdocs lab01 md to create an azure machine learning workspace and set up a compute instance to work on before completing any subsequent labs in this repository completing the labs complete the labs in order by following the links on the lab exercises labdocs readme md page before completing each lab we recommend you complete the associated training on microsoft learn https docs microsoft com learn paths build ai solutions with azure ml service | ai |
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dsfr | syst me de design de l tat github release https img shields io github v release gouvernementfr dsfr svg https github com gouvernementfr dsfr releases generic badge https img shields io badge npm yellow svg https www npmjs com package gouvfr dsfr generic badge https img shields io badge license grey svg https github com gouvernementfr dsfr blob main license md npm package monthly downloads https badgen net npm dm gouvfr dsfr https npmjs com package gouvfr dsfr le syst me de design de l tat ci apr s le dsfr est un ensemble de composants web html css et javascript pour faciliter le travail des quipes projets des sites internet publics et cr er des interfaces num riques de qualit et accessibles l outil est d velopp maintenu et g r par le service d information du gouvernement sig https www gouvernement fr service d information du gouvernement sig son utilisation par les administrations est soumise une demande d agr ment voir partie 5 des conditions g n rales d utilisation voir la documentation officielle https www systeme de design gouv fr licence et droit d utilisation le contenu de ce projet est plac sous licence mit license l exception de la fonte marianne voir license md https github com gouvernementfr dsfr blob main license md utilisation interdite en dehors des sites internet de l tat il est formellement interdit tout autre acteur d utiliser le syst me de design de l tat les administrations territoriales ou tout autre acteur priv le syst me de design de l tat repr sente l identit num rique de l tat en cas d usage des fins trompeuses ou frauduleuses l tat se r serve le droit d entreprendre les actions n cessaires pour y mettre un terme voir les conditions g n rales d utilisation doc legal cgu md prohibited use outside government websites this design system is only meant to be used by official french public services websites and apps its main purpose is to make it easy to identify governmental websites for citizens see terms installation l installation du syst me de design de l tat ci apr s le dsfr peut se faire de mani res diff rentes en t l chargeant l ensemble des fichiers n cessaires son utilisation en utilisant le gestionnaire de paquets npm ou encore via git fichiers statiques il est possible de t l charger l ensemble du dsfr au format zip ci dessous le zip contient un ensemble de fichiers css et javascript ainsi que les diff rentes polices web utilis es marianne et spectral et un ensemble d ic nes et de pictogrammes vous trouverez sur la page release de github https github com gouvernementfr dsfr releases toutes les sources des versions pr c dentes et la derni re en date npm le dsfr est disponible sur npm via un ensemble de packages qu il est possible d ajouter directement votre projet il est de ce fait n cessaire d installer nodejs https nodejs org et d avoir un fichier package json la racine de votre projet il est possible d en cr er un directement via la commande npm init une fois en place il suffit d installer le package gouvfr dsfr contenant l ensemble des composants npm install gouvfr dsfr il est galement possible d installer le package avec yarn https yarnpkg com yarn add gouvfr dsfr une fois termin le dsfr sera alors install dans le dossier node modules gouvfr dsfr pour visualiser les exemples il est n cessaire de lancer un serveur local npm run serve une fois le serveur lanc les exemples sont disponibles l adresse http localhost 8080 example structure du dsfr la structure que nous mettons disposition sur le zip ou npm est la suivante dist contient les fichiers css et js importer en fonction des packages utilis s src contient les sources sass et js des diff rents composants example contient des snippets html d example des composants que vous pouvez consulter en local configuration de votre projet lors de la cr ation de votre projet il est n cessaire d adopter l arborescence pr vue par celui ci savoir les fichiers html la racine du projets et les diff rentes sources du r pertoire dist dans des dossiers sp cifiques une structure minimale serait racine du projet index html dsfr min css dsfr module min js dsfr nomodule min js icons favicon fonts utility utilities min css les polices de caract res utilis es sur le ds savoir la marianne et la spectral sont des fichiers woff et woff2 ils doivent se trouver dans le r pertoire fonts les dossiers fonts et favicon doivent tre plac s au m me niveau que le dossier contenant le css du core du dsfr ou au m me niveau que le css dsfr min css la racine de dist qui contient le core le fichier utilities min css doit tre plac un niveau plus bas que le dossier icons dans dossier utility par exemple pour respecter les chemins d acc s vers les ic nes le html le point de d part de l utilisation du dsfr est la cr ation de fichiers html afin de pouvoir utiliser les diff rents composants ces fichiers sont mettre la racine de votre projet l exemple ci dessous est le code minimal afin de pouvoir utiliser le dsfr l ajout de l attribut data fr scheme sur la balise html permet d activer la gestion des th mes clair et sombre les valeurs possibles sont system light dark la valeur system permet d utiliser la configuration d finie sur le syst me d exploitation de l utilisateur consulter la documentation des param tres d affichage https www systeme de design gouv fr elements d interface composants parametre d affichage afin d en savoir plus html doctype html html lang fr data fr scheme system head meta charset utf 8 meta name viewport content width device width initial scale 1 shrink to fit no meta name format detection content telephone no meta name theme color content 000091 d fini la couleur de th me du navigateur safari android link rel apple touch icon href favicon apple touch icon png 180 180 link rel icon href favicon favicon svg type image svg xml link rel shortcut icon href favicon favicon ico type image x icon 32 32 link rel manifest href favicon manifest webmanifest crossorigin use credentials modifier les chemins relatifs des favicons en fonction de la structure du projet dans le fichier manifest webmanifest aussi modifier les chemins vers les images link rel stylesheet href dsfr min css link rel stylesheet href utility utility min css title titre de la page nom du site title head body code de la page script en version es6 module et nomodule pour les navigateurs le ne supportant pas script type module src dsfr module min js script script type text javascript nomodule src dsfr nomodule min js script body html les css afin d inclure la totalit des composants et des styles du syst me de design il est n cessaire d inclure la feuille de style dist dsfr min css les classes utilitaires notamment les ic nes sont disponibles dans un fichier part dans dist utility utility scss html html head link rel stylesheet href dsfr min css link rel stylesheet href utility utility min css il est aussi possible d importer uniquement ce que l on souhaite utiliser en effet pour ajouter un composant seul il suffit d importer son css ainsi que celui de chacune des d pendances de ce composant ces d pendances sont list s dans le readme md de chaque package html html head link rel stylesheet href core min css link rel stylesheet href link min css link rel stylesheet href button min css le javascript l ensemble du code javascript n cessaire au bon fonctionnement du ds se trouve dans deux fichiers dist dsfr module min js et dist dsfr nomodule min js le fichier dsfr module min js utilise les modules javascript natifs sa balise script d appel doit avoir l attribut type module le fichier dsfr nomodule min js est utilis par les anciens navigateurs ne supportant pas les modules javascript es6 sa balise script doit contenir l attribut nomodule il est imp ratif d appeler les deux fichiers javascript afin que le code s ex cute correctement sur l ensemble des navigateurs support s html script type module src dsfr module min js script script type text javascript nomodule src dsfr nomodule min js script body html nb le package analytics est g r ind pendament et doit tre ajout apr s le js du dsfr voir documention analytics https github com gouvernementfr dsfr blob main src analytics doc analytics md de la m me fa on que le css il est possible d importer uniquement le js des composants utilis s et leurs d pendances ic nes les ic nes sont stock es dans dist icons et class es par cat gories le design syst me utilise principalement des ic nes de la librairie remixicon il existe aussi des ic nes personnalis es celles ci sont pr fix e par fr afin d utiliser ces ic nes des classes utilitaires css sont associ s chaque ic ne par ex fr icon error fill ces classes sont disponible dans utility qui importe dist utility icons icons css il est aussi possible d importer uniquement certaines cat gories d ic nes afin d optimiser le poids par ex dist utility icons system system css pour les ic nes system pour plus d informations voir la documentation des ic nes https www systeme de design gouv fr elements d interface fondamentaux techniques icone favicon la documentation des favicons https www systeme de design gouv fr elements d interface fondamentaux techniques icone de favoris d taille la fa on de les impl menter dans vos pages fonctionnement bem le dsfr utilise la m thodologie bem https css tricks com bem 101 http getbem com naming http getbem com naming block element modifier comme convention de nommage des classes css elle permet aux d veloppeurs une meilleure compr hension de la relation entre html et css dans un projet donn selon cette m thodologie un block repr sente le plus haut niveau d abstraction d un nouveau composant par exemple parent des l ments ou enfants peuvent tre plac s l int rieur de ces blocks et sont d sign s par deux underscore pr c d s du nom du block parent element les modifiers quant eux servent manipuler les blocs de mani re les styliser de mani re ind pendante en s assurant de ne pas induire de changement des blocks sans aucun rapport avec celui ci ils sont not s l aide de deux tirets pr c d s du nom du block comme suit parent modifier utilisation le dsfr est constitu de diff rents composants que vous pouvez utiliser ind pendamment au sein de votre projet une documentation sp cifique est pr vue pour chaque composant pr cisant ses principes d utilisation ainsi que les snippets de code html utiliser pour votre projet vous tes maintenant pr t e utiliser le dsfr contribution le processus de contribution est d taill sur la page contributing md contributing md documentation documentation d veloppeurs https www systeme de design gouv fr utilisation et organisation developpeurs | css design-system html js dsfr government | os |
sam-design-system-site | sam design system site this project was generated with angular cli https github com angular angular cli version 1 4 10 setup local development follow these steps to checkout sam ui elements in a separate location on your local environment git clone https github helix gsa gov gsa iae apps sam ui elements cd sam ui elements npm link only production to link and setup your cloned environment git clone https github helix gsa gov gsa iae apps web standards site cd web standards site npm install npm link gsa sam sam ui elements npm run start development server run npm run start for a dev server navigate to http localhost 4201 the app will automatically reload if you change any of the source files build run npm run build to build the project the build artifacts will be stored in the dist directory | os |
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reasoning-on-graphs | reasoning on graphs rog official implementation of reasoning on graphs faithful and interpretable large language model reasoning https arxiv org abs 2310 01061 img src resources rog png width 800 reasoning on graphs rog synergizes llms with kgs to enable faithful and interpretable reasoning we present a planning retrieval reasoning framework where rog first generates relation paths grounded by kgs as faithful plans these plans are then used to retrieve valid reasoning paths from the kgs for llms to conduct faithful reasoning and generate interpretable results requirements pip install r requirements txt pre trained weights you can find the pre trained weights here https huggingface co rmanluo rog datasets rog webqsp https huggingface co datasets rmanluo rog webqsp rog cwq https huggingface co datasets rmanluo rog cwq inference step1 planning generate relation paths run scripts planning sh bash python src qa prediction gen rule path py model name rog model path rmanluo rog d rog webqsp rog cwq split test n beam 3 generated rules will be saved at results gen rule path dataset model name split step2 reasoning generate answers with rog run scripts rog reasoning sh bash python src qa prediction predict answer py model name rog model path rmanluo rog d rog webqsp rog cwq prompt path prompts llama2 predict txt add rul rule path rule path answers will be saved at results kgqa dataset model name split plug and play reasoning generate answers with different llms note you need to set your openai key at env to use chatgpt run scripts plug and play sh bash python src qa prediction predict answer py model name gpt 3 5 turbo alpaca llama2 chat hf flan t5 d rog webqsp rog cwq prompt path prompt path add rule rule path rule path interpretable reasoning run python scripts interpretable example py python from transformers import pipeline autotokenizer import torch model path or name rmanluo rog tokenizer autotokenizer from pretrained model path or name use fast false model pipeline text generation model model path or name tokenizer tokenizer device map auto torch dtype torch float16 print example 1 input text 1 based on the reasoning paths please answer the given question and explain why reasoning paths northern district location administrative division first level division of israel government form of government countries parliamentary system question what type of government is used in the country with northern district outputs model input text 1 return full text false print outputs 0 generated text training training code will be available soon results img src resources results png width 600 img src resources plug and play png width 600 img src resources lack of knowledge png width 600 img src resources hallucination png width 600 bibinfo if you found this repo helpful please help us by citing this paper article luo rog title reasoning on graphs faithful and interpretable large language model reasoning author luo linhao and li yuan fang and haffari gholamreza and pan shirui journal arxiv preprint arxiv 2310 01061 year 2023 | kg knowledge large-language-models llm reasoning | ai |
exp-up-down-counter-iitr | introduction b discipline b fill your discipline name here b lab b fill your lab name here b experiment b fill your experiment name and number here about the experiment fill a brief description of this experiment here b name of developer b fill the name of experiment owner here b institute b b email id b b department contributors list srno name faculty or student department institute email id 1 2 | ext-ph3 iitr | os |
Department_of_CSE_by_PHP-JavaScript-HTML5-CSS3 | department of cse by php javascript html5 css3 a full stack web engineering project front end development html5 css3 javascript amp bootstrap 4 back end development php database mysql coding ide visual studio code database ide sqlyog operating system microsoft windows linux android ios | os |
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system-design | s leschev system architect img itemprop image alt sergey leschev src https sergeyleschev github io sergeyleschev png width 250 google engineering level l7 awards ranking dev global top 200 certificate https leetcode com sergeyleschev a href https leetcode com sergeyleschev img src https github com sergeyleschev sergeyleschev blob main leetcode ranking png raw true alt drawing width 410 a system design design large scale systems 2022 amazon dropbox instagram facebook netflix pinterest twitter uber youtube architectures s leschev https github com sergeyleschev system design blob main sergeyleschev system architect roadmap md br licenses certifications leetcode global top 200 swift certificate https leetcode com sergeyleschev sources swift https github com sergeyleschev leetcode swift golden award for the year of the tiger challenge typescript certificate https app codility com cert view certqba3ew qesxm38dnr3sxmyz sources codility https github com sergeyleschev codility swift golden award muad dib s challenge swift certificate https app codility com cert view cert5yt6ja y9zkfefxezwgtr3g sources swift https github com sergeyleschev codility swift 2022 oct leetcode challenge 2022 10 31 https leetcode com sergeyleschev 2022 sep leetcode challenge 2022 09 30 https leetcode com sergeyleschev 2022 aug leetcode challenge 2022 08 31 https leetcode com sergeyleschev 2022 july leetcode challenge 2022 07 31 https leetcode com sergeyleschev 2022 june leetcode challenge 2022 06 30 https leetcode com sergeyleschev 2022 may leetcode challenge 2022 05 31 https leetcode com sergeyleschev 2022 apr leetcode challenge 2022 04 30 https leetcode com sergeyleschev leetcode dynamic programming 2022 05 07 https leetcode com sergeyleschev graph theory 2022 04 30 https leetcode com sergeyleschev sql 2022 04 26 https leetcode com sergeyleschev algorithm i 2022 04 30 https leetcode com sergeyleschev algorithm ii 2022 05 21 https leetcode com sergeyleschev data structure i 2022 04 30 https leetcode com sergeyleschev data structure ii 2022 05 21 https leetcode com sergeyleschev binary search i 2022 04 28 https leetcode com sergeyleschev binary search ii 2022 05 18 https leetcode com sergeyleschev programming skills i 2022 04 28 https leetcode com sergeyleschev programming skills ii 2022 05 18 https leetcode com sergeyleschev linkedin skill asessment mobile swift programming language https www linkedin com in sergeyleschev detail assessments swift report object oriented programming oop https www linkedin com in sergeyleschev detail assessments object oriented 20programming 20 oop report objective c https www linkedin com in sergeyleschev detail assessments objective c report c https www linkedin com in sergeyleschev detail assessments c report ionic https www linkedin com in sergeyleschev detail assessments angular report json https www linkedin com in sergeyleschev detail assessments json report xml https www linkedin com in sergeyleschev detail assessments xml report android https www linkedin com in sergeyleschev detail assessments android report kotlin https www linkedin com in sergeyleschev detail assessments kotlin report maven https www linkedin com in sergeyleschev detail assessments maven report java https www linkedin com in sergeyleschev detail assessments java report rest apis https www linkedin com in sergeyleschev detail assessments rest 20apis report linkedin skill asessment front end front end development https www linkedin com in sergeyleschev detail assessments front end 20development report angular https www linkedin com in sergeyleschev detail assessments angular report react https www linkedin com in sergeyleschev detail assessments react report javascript https www linkedin com in sergeyleschev detail assessments javascript report html https www linkedin com in sergeyleschev detail assessments html report css https www linkedin com in sergeyleschev detail assessments cascading 20style 20sheets 20 css report jquery https www linkedin com in sergeyleschev detail assessments jquery report linkedin skill asessment back end node js https www linkedin com in sergeyleschev detail assessments node js report java https www linkedin com in sergeyleschev detail assessments java report spring framework https www linkedin com in sergeyleschev detail assessments spring 20framework report scala https www linkedin com in sergeyleschev detail assessments scala report c https www linkedin com in sergeyleschev detail assessments c 23 report net framework https www linkedin com in sergeyleschev detail assessments net 20framework report unity https www linkedin com in sergeyleschev detail assessments unity report python programming language https www linkedin com in sergeyleschev detail assessments python 20 programming 20language report django https www linkedin com in sergeyleschev detail assessments django report php https www linkedin com in sergeyleschev detail assessments php report c programming language https www linkedin com in sergeyleschev detail assessments c 20 programming 20language report linkedin skill asessment databases mongodb https www linkedin com in sergeyleschev detail assessments mongodb report nosql https www linkedin com in sergeyleschev detail assessments nosql report transact sql t sql https www linkedin com in sergeyleschev detail assessments transact sql 20 t sql report mysql https www linkedin com in sergeyleschev detail assessments mysql report linkedin skill asessment infra devops bash https www linkedin com in sergeyleschev detail assessments bash report git https www linkedin com in sergeyleschev detail assessments git report amazon web services aws https www linkedin com in sergeyleschev detail assessments amazon 20web 20services 20 aws report aws lambda https www linkedin com in sergeyleschev detail assessments aws 20lambda report google cloud platform gcp https www linkedin com in sergeyleschev detail assessments google 20cloud 20platform 20 gcp report microsoft azure https www linkedin com in sergeyleschev detail assessments microsoft 20azure report hadoop https www linkedin com in sergeyleschev detail assessments hadoop report it operations https www linkedin com in sergeyleschev detail assessments it 20operations report div style page break after always div contacts i have a clear focus on time to market and don t prioritize technical debt and i took part in the pre sale rfx activity as a system architect assessment efforts for mobile ios swift android kotlin frontend react typescript and backend nodejs net php kafka sql nosql and i also formed the work of pre sale as a cto from opportunity to proposal via knowledge transfer to successful delivery startups management cto swift typescript database email sergey leschev gmail com mailto sergey leschev gmail com linkedin https linkedin com in sergeyleschev https www linkedin com in sergeyleschev twitter https twitter com sergeyleschev https twitter com sergeyleschev github https github com sergeyleschev https github com sergeyleschev website https sergeyleschev github io https sergeyleschev github io dev community https dev to sergeyleschev https dev to sergeyleschev reddit https reddit com user sergeyleschev https reddit com user sergeyleschev quora https quora com sergey leschev https quora com sergey leschev medium https medium com sergeyleschev https medium com sergeyleschev pdf download https sergeyleschev github io sergeyleschev system architect roadmap pdf alt siarhei liashchou | large-scale-systems amazon dropbox instagram facebook netflix pinterest twitter uber youtube sergeyleschev algorithm algorithms api aws rest-api bash devops graphql sql | os |
nlp-in-javascript-with-natural | natural language processing in javascript with natural in this course we ll work through natural s api for natural language processing in javascript we ll look at how to process text learning how to break up language strings find the word roots work with inflectors find sequences of words and tag parts of speech we ll learn how to find important stats about a body of text how to compare strings how to classify text with machine learning how to use the tf idf tool to find relevant words we ll look at some of the extra tools natural gives us including the dictionary thesaurus of wordnet a phonetics comparer that lets us see if two words sound the same and a spellcheck feature we ll also look at tries and digraphs two data structures that help us better analyze bodies of text lessons 1 break up language strings into parts using natural we will learn about tokenizing the process of separating strings into parts we will run through the four tokenizers included in natural wordtokenizer wordpuncttokenizer treebankwordtokenizer and regexptokenizer 2 find the roots of words using stemming in natural we will learn about stemming the process of finding the root of words often in order to group words by a common base root we will look at the porter and lancaster stemmers briefly touch on natural s support for russian and spanish stemmers and introduce the function to stem and tokenize at the same time 3 pluralizing nouns and counting numbers with inflectors in natural inflectors are the modifiers of a word that indicate grammatical categories while natural s coverage of inflectors is not comprehensive we will show how natural can pluralize singularize nouns and count numbers 4 find sequences of words n grams using natural in this lesson we will see how to find bigrams trigrams and any other length n gram in a body of text 5 tag parts of speech using natural an important component of many natural language processing projects is being able to identify the grammar of a piece of text we ll learn how to do that with natural s parts of speech pos tagger 6 compare similarity of strings through string distance in natural we will learn how to compare how similar two strings are to each other examining three algorithms jaro winkler levenshtein and dice s coefficient 7 classify text into categories with machine learning in natural in this lesson we will learn how to train a naive bayes classifier a basic machine learning algorithm in order to classify text into categories 8 classify json text data with machine learning in natural in this lesson we will learn how to train a naive bayes classifier and a logistic regression classifier basic machine learning algorithms on json text data and classify it into categories 9 using machine learning classifiers in a new project by this point we ve seen that classification can take a long time and with more data it would take even longer luckily natural provides support to save your classifier in this lesson we will learn how to save our last classifier and load it into a new project in order to classify new data 10 identify the most important words in a document using tf idf in natural tf idf or term frequency inverse document frequency is a statistic that indicates how important a word is to the entire document this lesson will explain term frequency and inverse document frequency and show how we can use tf idf to identify the most relevant words in a body of text 11 find a word s definition using wordnet in natural this lesson introduces wordnet which is an important resource in natural language processing with wordnet we can look up a word s definition or find its synonyms 12 search more efficiently with tries using natural tries are a data structure that provide an efficient way to search for the existence of a word or phrase in a body of text or to search by prefix 13 include spell check in text projects using natural in this lesson we ll see how to use natural s probabilistic spell checker which uses the trie data structure 14 check if words sound alike using natural in this lesson we ll take a look at natural s phonetics feature we ll learn how to check whether two words sound alike looking at both the soundex and metaphone algorithms | ai |
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