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danlp | h1 align center img src https raw githubusercontent com alexandrainst danlp master docs docs imgs danlp logo png width 350 h1 div align center a href https pypi org project danlp img src https img shields io pypi v danlp svg a a href https travis ci com alexandrainst danlp img src https travis ci com alexandrainst danlp svg branch master a a href https coveralls io github alexandrainst danlp branch master img src https coveralls io repos github alexandrainst danlp badge svg branch master a a href https opensource org licenses bsd 3 clause img src https img shields io badge license bsd 203 blue svg a a href https danlp alexandra readthedocs io en latest badge latest img src https readthedocs org projects danlp alexandra badge version latest alt documentation status a div div align center h5 a href https github com alexandrainst danlp blob master docs docs tasks pos md part of speech tagging a span span a href https github com alexandrainst danlp blob master docs docs tasks dependency md dependency parsing a h5 h5 a href https github com alexandrainst danlp blob master docs docs tasks ner md named entity recognition a span span a href https github com alexandrainst danlp blob master docs docs tasks ned md named entity disambiguation a span span a href https github com alexandrainst danlp blob master docs docs tasks coreference md coreference resolution a h5 h5 a href https github com alexandrainst danlp blob master docs docs tasks sentiment analysis md sentiment analysis a span span a href https github com alexandrainst danlp blob master docs docs tasks hatespeech md hatespeech detection a h5 h5 a href https github com alexandrainst danlp blob master docs docs tasks embeddings md embeddings a span span a href https github com alexandrainst danlp blob master docs docs datasets md datasets a span span a href https github com alexandrainst danlp tree master examples tutorials tutorials a h5 div danlp is a repository for natural language processing resources for the danish language it is a collection of available datasets and models for a variety of nlp tasks the aim is to make it easier and more applicable to practitioners in the industry to use danish nlp and hence this project is licensed to allow commercial use the project features code examples on how to use the datasets and models in popular nlp frameworks such as spacy transformers and flair as well as deep learning frameworks such as pytorch see our documentation pages https danlp alexandra readthedocs io en latest index html for more details about our models and datasets and definitions of the modules provided through the danlp package if you are new to nlp or want to know more about the project in a broader perspective you can start on our microsite https danlp alexandra dk br help us improve danlp raising hand have you tried the danlp package then we would love to chat with you about your experiences from a company perspective it will take approx 20 30 minutes and there s no preparation english danish as you prefer please leave your details here https forms office com pages responsepage aspx id zspas4dkm0gkfxzzewsohkhc on5bmxbtrwkonvf21tuquxdq0oytvayu0tdudvdmtm4sku4sjjisi4u and then we will reach out to arrange a call news tada version 0 1 2 has been released https github com alexandrainst danlp releases with 2 new models for hate speech detection hatespeech https danlp alexandra readthedocs io en latest docs tasks hatespeech html based on bert and electra 1 new model for hate speech classification next up new model and data for discourse coherence installation to get started using danlp in your python project simply install the pip package note that installing the default pip package will not install all nlp libraries because we want you to have the freedom to limit the dependency on what you use instead we provide you with an installation option if you want to install all the required dependencies install with pip to get started using danlp simply install the project with pip bash pip install danlp note that the default installation of danlp does not install other nlp libraries such as gensim spacy flair or transformers this allows the installation to be as minimal as possible and let the user choose to e g load word embeddings with either spacy flair or gensim therefore depending on the function you need to use you should install one or several of the following pip install flair pip install spacy or and pip install gensim alternatively if you want to install all the required dependencies including the packages mentionned above you can do bash pip install danlp all you can check the requirements txt file to see what version the packages has been tested with install from source if you want to be able to use the latest developments before they are released in a new pip package or you want to modify the code yourself then clone this repo and install from source git clone https github com alexandrainst danlp git cd danlp minimum installation pip install or install all the packages pip install all to install the dependencies used in the package with the tested versions python pip install r requirements txt install from github alternatively you can install the latest version from github using pip install git https github com alexandrainst danlp git install with docker to quickly get started with danlp and to try out the models you can use our docker image to start a ipython session simply run bash docker run it rm alexandrainst danlp ipython if you want to run a script py in your current working directory you can run bash docker run it rm v pwd usr src app w usr src app alexandrainst danlp python script py quick start read more in our documentation pages https danlp alexandra readthedocs io en latest docs gettingstarted quickstart html nlp models natural language processing is an active area of research and it consists of many different tasks the danlp repository provides an overview of danish models for some of the most common nlp tasks and is continuously evolving here is the list of nlp tasks https danlp alexandra readthedocs io en latest tasks html we currently cover in the repository embedding of text docs docs tasks embeddings md part of speech tagging docs docs tasks pos md dependency parsing docs docs tasks dependency md named entity recognition docs docs tasks ner md named entity disambiguation docs docs tasks ned md coreference resolution docs docs tasks coreference md sentiment analysis docs docs tasks sentiment analysis md hatespeech detection docs docs tasks hatespeech md you can also find some of our transformers docs docs frameworks transformers md models on huggingface https huggingface co danlp if you are interested in danish support for any specific nlp task you are welcome to get in contact with us we also recommend to check out the list https github com fnielsen awesome danish of danish nlp corpora tools models maintained by finn rup nielsen warning not all items are available for commercial use check the licence datasets the number of datasets in the danish language is limited the danlp repository provides an overview of the available danish datasets https danlp alexandra readthedocs io en latest docs datasets html that can be used for commercial purposes the danlp package allows you to download and preprocess datasets examples you will find examples that shows how to use nlp in danish using our models or others in our benchmark examples benchmarks scripts and jupyter notebook tutorials examples tutorials this project keeps a danish written blog https medium com danlp on medium where we write about danish nlp and in time we will also provide some real cases of how nlp is applied in danish companies structure of the repo to help you navigate we provide you with an overview of the structure in the github danlp source files datasets code to load datasets with different frameworks models code to load models with different frameworks docker docker image docs documentation and files for setting up read the docs docs documentation for tasks datasets and frameworks tasks documentation for nlp tasks with benchmark results frameworks overview over different frameworks used gettingstarted guides for installation and getting started imgs images used in documentation library files used for read the docs examples examples tutorials and benchmark scripts benchmarks scripts for reproducing benchmarks results tutorials jupyter notebook tutorials tests tests for continuous integration with travis how do i contribute if you want to contribute to the danlp repository and make it better your help is very welcome you can contribute to the project in many ways help us write good tutorials examples tutorials on danish nlp use cases contribute with your own pretrained nlp models or datasets in danish see our contributing guidelines contributing md for more details on how to contribute to this repository create github issues with questions and bug reports notify us of other danish nlp resources or tell us about any good ideas that you have for improving the project through the discussions https github com alexandrainst danlp discussions section of this repository who is behind img align right width 150 src https raw githubusercontent com alexandrainst danlp master docs docs imgs alexandra logo png the danlp repository is maintained by the alexandra institute https alexandra dk uk which is a danish non profit company with a mission to create value growth and welfare in society the alexandra institute is a member of gts https gts net dk a network of independent danish research and technology organisations between 2019 and 2020 the work on this repository was part of the dansk for alle https bedreinnovation dk dansk alle 0 performance contract rk allocated to the alexandra institute by the danish ministry of higher education and science https ufm dk en set language en cl en since 2021 the project is funded through the dansk nlp http bedreinnovation dk dansk nlp activity plan which is part of the digital sikkerhed tillid og dataetik http bedreinnovation dk digital sikkerhed tillid og dataetik 0 performance contract an overview of the project can be found on our microsite https danlp alexandra dk cite if you want to cite this project please use the following bibtex entry inproceedings danlp2021 title danlp an open source toolkit for danish natural language processing author brogaard pauli amalie and barrett maria and lacroix oph lie and hvingelby rasmus booktitle proceedings of the 23rd nordic conference on computational linguistics nodalida 2021 month june year 2021 read the paper here https ep liu se ecp 178 053 ecp2021178053 pdf see our documentation pages https danlp alexandra readthedocs io en latest index html for references to specific models or datasets | nlp danish machine-learning nlp-library natural-language-processing named-entity-recognition part-of-speech word-embeddings | ai |
Awesome-LLM-for-RecSys | awesome llm for recsys awesome https awesome re badge svg https awesome re a collection of awesome papers and resources on the large language model llm related recommender system topics satisfied please check out our survey paper for llm enhanced rs how can recommender systems benefit from large language models a survey https arxiv org abs 2306 05817 to catch up with the latest research progress this repository will be actively maintained as well as our released survey paper newly added papers will first appear in 1 6 paper pending list to be added to our survey paper section rocket 2023 06 29 paper v4 released 7 papers have been newly added details summary b survey paper update logs b summary p ul li b 2023 06 29 paper v4 released b 7 papers have been newly added li li b 2023 06 28 paper v3 released b fix typos li li b 2023 06 12 paper v2 released b add summerization table in the appendix li li b 2023 06 09 paper v1 released b initial version li ul p details 1 papers we classify papers according to where llm will be adapted in the pipeline of rs which is summarized in the figure below img width 350 src https github com chiangel awesome llm for recsys blob main where framework 1 png details summary b 1 1 llm for feature engineering b summary p name paper llm backbone largest llm tuning strategy publication link great language models are realistic tabular data generators gpt2 medium 355m full finetuning iclr 2023 link https arxiv org abs 2210 06280 genre a first look at llm powered generative news recommendation chatgpt frozen arxiv 2023 link https arxiv org abs 2305 06566 anypredict anypredict foundation model for tabular prediction chatgpt frozen arxiv 2023 link https arxiv org abs 2305 12081 llm4kgc knowledge graph completion models are few shot learners an empirical study of relation labeling in e commerce with llms palm 540b chatgpt frozen arxiv 2023 link https arxiv org abs 2305 09858v1 taggpt taggpt large language models are zero shot multimodal taggers chatgpt frozen arxiv 2023 link https arxiv org abs 2304 03022v1 icpc large language models for user interest journeys lamda 137b full finetuning prompt tuning arxiv 2023 link https arxiv org abs 2305 15498 dpllm privacy preserving recommender systems with synthetic query generation using differentially private large language models t5 xl 3b full finetuning arxiv 2023 link https arxiv org abs 2305 05973 kar towards open world recommendation with knowledge augmentation from large language models chatgpt frozen arxiv 2023 link https arxiv org abs 2306 10933 mint large language model augmented narrative driven recommendations gpt3 175b frozen recsys 2023 link https arxiv org abs 2306 02250 p details details summary b 1 2 llm as feature encoder b summary p name paper llm backbone largest llm tuning strategy publication link u bert u bert pre training user representations for improved recommendation bert base 110m full finetuning aaai 2021 link https ojs aaai org index php aaai article view 16557 unbert unbert user news matching bert for news recommendation bert base 110m full finetuning ijcai 2021 link https www ijcai org proceedings 2021 462 plm nr empowering news recommendation with pre trained language models roberta base 125m full finetuning sigir 2021 link https arxiv org abs 2104 07413 pyramid ernie pre trained language model based ranking in baidu search ernie 110m full finetuning kdd 2021 link https arxiv org abs 2105 11108 ernie rs pre trained language model for web scale retrieval in baidu search ernie 110m full finetuning kdd 2021 link https arxiv org abs 2106 03373 ctr bert ctr bert cost effective knowledge distillation for billion parameter teacher models customized bert 1 5b full finetuning enlsp 2021 link https neurips2021 nlp github io papers 20 cameraready camera ready final pdf zesrec zero shot recommender systems bert base 110m frozen arxiv 2021 link https arxiv org abs 2105 08318 unisrec towards universal sequence representation learning for recommender systems bert base 110m frozen kdd 2022 link https arxiv org abs 2206 05941 prec boosting deep ctr prediction with a plug and play pre trainer for news recommendation bert base 110m full finetuning coling 2022 link https aclanthology org 2022 coling 1 249 mm rec mm rec visiolinguistic model empowered multimodal news recommendation bert base 110m full finetuning sigir 2022 link https dl acm org doi abs 10 1145 3477495 3531896 tiny newsrec tiny newsrec effective and efficient plm based news recommendation unilmv2 base 110m full finetuning emnlp 2022 link https arxiv org abs 2112 00944 plm4tag ptm4tag sharpening tag recommendation of stack overflow posts with pre trained models codebert 125m full finetuning icpc 2022 link https arxiv org abs 2203 10965 twhin bert twhin bert a socially enriched pre trained language model for multilingual tweet representations bert base 110m full finetuning arxiv 2022 link https arxiv org abs 2209 07562 transrec transrec learning transferable recommendation from mixture of modality feedback bert base 110m full finetuning arxiv 2022 link https arxiv org abs 2206 06190 vq rec learning vector quantized item representation for transferable sequential recommenders bert base 110m frozen www 2023 link https arxiv org abs 2210 12316 idrec vs morec where to go next for recommender systems id vs modality based recommender models revisited bert base 110m full finetuning sigir 2023 link https arxiv org abs 2303 13835 transrec exploring adapter based transfer learning for recommender systems empirical studies and practical insights roberta base 125m layerwise adapter tuning arxiv 2023 link https arxiv org abs 2305 15036 lsh improving code example recommendations on informal documentation using bert and query aware lsh a comparative study bert base 110m full finetuning arxiv 2023 link https arxiv org abs 2305 03017v1 tcf exploring the upper limits of text based collaborative filtering using large language models discoveries and insights opt 175b 175b frozen full finetuning arxiv 2023 link https arxiv org abs 2305 11700 p details details summary b 1 3 llm as scoring ranking function b summary p b 1 3 1 item scoring task b name paper llm backbone largest llm tuning strategy publication link lmrecsys language models as recommender systems evaluations and limitations gpt2 xl 1 5b full finetuning icbinb 2021 link https openreview net forum id hfx3fy7 m9b ptab ptab using the pre trained language model for modeling tabular data bert base 110m full finetuning arxiv 2022 link https arxiv org abs 2209 08060 unitrec unitrec a unified text to text transformer and joint contrastive learning framework for text based recommendation bart 406m full finetuning acl 2023 link https arxiv org abs 2305 15756 prompt4nr prompt learning for news recommendation bert base 110m full finetuning sigir 2023 link https arxiv org abs 2304 05263 recformer text is all you need learning language representations for sequential recommendation longformer 149m full finetuning kdd 2023 link https arxiv org abs 2305 13731v1 tabllm tabllm few shot classification of tabular data with large language models t0 11b few shot parameter effiecnt finetuning aistats 2023 link https arxiv org abs 2210 10723 zero shot gpt zero shot recommendation as language modeling gpt2 medium 355m frozen arxiv 2023 link https arxiv org abs 2112 04184 flan t5 do llms understand user preferences evaluating llms on user rating prediction flan 5 xxl 11b full finetuning arxiv 2023 link https arxiv org pdf 2305 06474 pdf bookgpt bookgpt a general framework for book recommendation empowered by large language model chatgpt frozen arxiv 2023 link https arxiv org abs 2305 15673v1 tallrec tallrec an effective and efficient tuning framework to align large language model with recommendation llama 7b lora recsys 2023 link https arxiv org abs 2305 00447 pbnr pbnr prompt based news recommender system t5 small 60m full finetuning arxiv 2023 link https arxiv org abs 2304 07862 b 1 3 2 item generation task b name paper llm backbone largest llm tuning strategy publication link gpt4rec gpt4rec a generative framework for personalized recommendation and user interests interpretation gpt2 110m full finetuning arxiv 2023 link https arxiv org abs 2304 03879 up5 up5 unbiased foundation model for fairness aware recommendation t5 base 223m full finetuning arxiv 2023 link https arxiv org abs 2305 12090 vip5 vip5 towards multimodal foundation models for recommendation t5 base 223m layerwise adater tuning arxiv 2023 link https arxiv org abs 2305 14302 p5 id how to index item ids for recommendation foundation models t5 small 61m full finetuning arxiv 2023 link https arxiv org abs 2305 06569 fairllm is chatgpt fair for recommendation evaluating fairness in large language model recommendation chatgpt frozen recsys 2023 link https arxiv org abs 2305 07609 palr palr personalization aware llms for recommendation llama 7b full finetuning arxiv 2023 link https arxiv org abs 2305 07622 chatgpt large language models are zero shot rankers for recommender systems chatgpt frozen arxiv 2023 link https arxiv org abs 2305 08845 agr sparks of artificial general recommender agr early experiments with chatgpt chatgpt frozen arxiv 2023 link https arxiv org abs 2305 04518 nir zero shot next item recommendation using large pretrained language models gpt3 175b frozen arxiv 2023 link https arxiv org abs 2304 03153 gptrec generative sequential recommendation with gptrec gpt2 medium 355m full finetuning gen ir sigir 2023 link https arxiv org abs 2306 11114 chatnews a preliminary study of chatgpt on news recommendation personalization provider fairness fake news chatgpt frozen arxiv 2023 link https arxiv org abs 2306 10702 b 1 3 3 hybrid task b name paper llm backbone largest llm tuning strategy publication link p5 recommendation as language processing rlp a unified pretrain personalized prompt predict paradigm p5 t5 base 223m full finetuning recsys 2022 link https arxiv org abs 2203 13366 m6 rec m6 rec generative pretrained language models are open ended recommender systems m6 base 300m option tuning arxiv 2022 link https arxiv org abs 2205 08084 instructrec recommendation as instruction following a large language model empowered recommendation approach flan t5 xl 3b full finetuning arxiv 2023 link https arxiv org abs 2305 07001 chatgpt is chatgpt a good recommender a preliminary study chatgpt frozen arxiv 2023 link https arxiv org abs 2304 10149 chatgpt is chatgpt good at search investigating large language models as re ranking agent chatgpt frozen arxiv 2023 link https arxiv org abs 2304 09542 chatgpt uncovering chatgpt s capabilities in recommender systems chatgpt frozen recsys 2023 link https arxiv org abs 2305 02182 p details details summary b 1 4 llm for rs pipeline controller b summary p name paper llm backbone largest llm tuning strategy publication link chat rec chat rec towards interactive and explainable llms augmented recommender system chatgpt frozen arxiv 2023 link https arxiv org abs 2303 14524 recllm leveraging large language models in conversational recommender systems llama 7b full finetuning arxiv 2023 link https arxiv org abs 2305 07961 p details details summary b 1 5 other related papers b summary p b 1 5 1 related survey papers b paper publication link large language models for generative recommendation a survey and visionary discussions arxiv 2023 link https arxiv org abs 2309 01157 large language models for information retrieval a survey arxiv 2023 link https arxiv org abs 2308 07107 when large language models meet personalization perspectives of challenges and opportunities arxiv 2023 link https arxiv org abs 2307 16376 recommender systems in the era of large language models llms arxiv 2023 link https arxiv org abs 2307 02046 a survey on large language models for recommendation arxiv 2023 link https arxiv org abs 2305 19860 pre train prompt and recommendation a comprehensive survey of language modelling paradigm adaptations in recommender systems arxiv 2023 link https arxiv org abs 2302 03735 self supervised learning for recommender systems a survey arxiv 2022 link https arxiv org abs 2203 15876 b 1 5 2 other papers b paper publication link evaluation of synthetic datasets for conversational recommender systems arxiv 2023 link https arxiv org abs 2212 08167v1 generative recommendation towards next generation recommender paradigm arxiv 2023 link https arxiv org abs 2304 03516 towards personalized prompt model retrieval for generative recommendation arxiv 2023 link https arxiv org abs 2308 02205 generative next basket recommendation recsys 2023 link https dl acm org doi abs 10 1145 3604915 3608823 p details details summary b 1 6 paper pending list to be added to our survey paper b summary p name paper llm backbone largest llm tuning strategy publication link large language models are competitive near cold start recommenders for language and item based preferences recsys 2023 link https recsys acm org recsys23 accepted contributions content tab 1 1 tab llm4rec large language models for recommendation via a lightweight tuning framework recsys 2023 link https recsys acm org recsys23 accepted contributions content tab 1 1 tab cr sorec bert driven consistency regularization for social recommendation recsys 2023 link https recsys acm org recsys23 accepted contributions content tab 1 1 tab leveraging large language models for sequential recommendation recsys 2023 link https arxiv org abs 2309 09261 beyond labels leveraging deep learning and llms for content metadata recsys 2023 link https recsys acm org recsys23 accepted contributions content tab 1 6 tab genrec genrec large language model for generative recommendation llama 7b lora arxiv 2023 link https arxiv org abs 2307 00457 towards personalized cold start recommendation with prompts link https arxiv org abs 2306 17256 prompt tuning large language models on personalized aspect extraction for recommendations link https arxiv org abs 2306 01475 exploring large language model for graph data understanding in online job recommendations link https arxiv org abs 2307 05722 tiger recommender systems with generative retrieval nips 2023 link https arxiv org abs 2305 05065 better generalization with semantic ids a case study in ranking for recommendations arxiv 2023 link https arxiv org abs 2306 08121 product information extraction using chatgpt arxiv 2023 link https arxiv org abs 2306 14921 enhancing job recommendation through llm based generative adversarial networks arxiv 2023 link https arxiv org abs 2307 10747 generative job recommendations with large language model arxiv 2023 link https arxiv org abs 2307 02157 large language models are competitive near cold start recommenders for language and item based preferences recsys 2023 link https arxiv org abs 2307 14225 llm rec personalized recommendation via prompting large language models arxiv 2023 link https arxiv org abs 2307 15780 heterogeneous knowledge fusion a novel approach for personalized recommendation via llm recsys 2023 link https arxiv org abs 2308 03333 a large language model enhanced conversational recommender system arxiv 2023 link https arxiv org abs 2308 06212 llama e empowering e commerce authoring with multi aspect instruction following arxiv 2023 link https arxiv org abs 2308 04913 the unequal opportunities of large language models revealing demographic bias through job recommendations eaamo 2023 link https arxiv org abs 2308 02053 bert4ctr an efficient framework to combine pre trained language model with non textual features for ctr prediction kdd 2023 link https dl acm org doi abs 10 1145 3580305 3599780 a bi step grounding paradigm for large language models in recommendation systems arxiv 2023 link https arxiv org abs 2308 08434 knowledge prompt tuning for sequential recommendation arxiv 2023 link https arxiv org abs 2308 08459 learning supplementary nlp features for ctr prediction in sponsored search kdd 2023 link https dl acm org doi abs 10 1145 3534678 3539064 leveraging large language models for pre trained recommender systems arxiv 2023 link https arxiv org abs 2308 10837 enhancing recommender systems with large language model reasoning graphs arxiv 2023 link https arxiv org abs 2308 10835 large language models as zero shot conversational recommenders cikm 2023 link https arxiv org abs 2308 10053 rah recsys assistant human a human central recommendation framework with large language models arxiv 2023 link https arxiv org abs 2308 09904 tbin modeling long textual behavior data for ctr prediction arxiv 2023 link https arxiv org abs 2308 08483 lkpnr llm and kg for personalized news recommendation framework arxiv 2023 link https arxiv org abs 2308 12028 llmrec benchmarking large language models on recommendation task arxiv 2023 link https arxiv org abs 2308 12241 rella retrieval enhanced large language models for lifelong sequential behavior comprehension in recommendation arxiv 2023 link https arxiv org abs 2308 11131 prompt distillation for efficient llm based recommendation cikm 2023 link https lileipisces github io files cikm23 pod paper pdf recmind large language model powered agent for recommendation arxiv 2023 link https arxiv org abs 2308 14296 text matching improves sequential recommendation by reducing popularity biases cikm 2023 link https arxiv org abs 2308 14029 zero shot recommendations with pre trained large language models for multimodal nudging arxiv 2023 link https arxiv org abs 2309 01026 recommender ai agent integrating large language models for interactive recommendations arxiv 2023 link https arxiv org abs 2308 16505 evaluating chatgpt as a recommender system a rigorous approach arxiv 2023 link https arxiv org abs 2309 03613 unveiling challenging cases in text based recommender systems recsys workshop 2023 link https ceur ws org vol 3476 paper5 pdf retrieval augmented recommender system enhancing recommender systems with large language models recsys doctoral symposium 2023 link https dl acm org doi abs 10 1145 3604915 3608889 user centric conversational recommendation adapting the need of user with large language models recsys doctoral symposium 2023 link https dl acm org doi abs 10 1145 3604915 3608885 an unified search and recommendation foundation model for cold start scenario cikm 2023 link https arxiv org abs 2309 08939 jobrecogpt explainable job recommendations using llms arxiv 2023 link https arxiv org abs 2309 11805 reformulating sequential recommendation learning dynamic user interest with content enriched language modeling arxiv 2023 link https arxiv org abs 2309 10435 towards efficient and effective adaptation of large language models for sequential recommendation arxiv 2023 link https arxiv org abs 2310 01612 lending interaction wings to recommender systems with conversational agents nips 2023 link https arxiv org abs 2310 04230 a multi facet paradigm to bridge large language model and recommendation arxiv 2023 link https arxiv org abs 2310 06491 musechat a conversational music recommendation system for videos arxiv 2023 link https arxiv org abs 2310 06282 ecomgpt instruction tuning large language models with chain of task tasks for e commerce arxiv 2023 link https arxiv org abs 2308 06966 clickprompt ctr models are strong prompt generators for adapting language models to ctr prediction arxiv 2023 link https arxiv org abs 2310 09234 agentcf collaborative learning with autonomous language agents for recommender systems arxiv 2023 link https arxiv org abs 2310 09233 factual and personalized recommendations using language models and reinforcement learning arxiv 2023 link https arxiv org abs 2310 06176 on generative agents in recommendation arxiv 2023 link https arxiv org abs 2310 10108 leveraging large language models llms to empower training free dataset condensation for content based recommendation arxiv 2023 link https arxiv org abs 2310 09874 collaborative contextualization bridging the gap between collaborative filtering and pre trained language model arxiv 2023 link https arxiv org abs 2310 09400 a setwise approach for effective and highly efficient zero shot ranking with large language models arxiv 2023 link https arxiv org abs 2310 09497 language models as semantic indexers arxiv 2023 link https arxiv org abs 2310 07815 thoroughly modeling multi domain pre trained recommendation as language arxiv 2023 link https arxiv org abs 2310 13540 p details 2 datasets benchmarks the datasets benchmarks for llm related rs topics should maintain the original semantic textual features instead of anonymous feature ids 2 1 datasets dataset rs scenario link reddit movie conversational movie link https github com aaronheee llms as zero shot conversational recsys large language models as zero shot conversational recommenders amazon m2 e commerce link https arxiv org abs 2307 09688 movielens movie link https grouplens org datasets movielens 1m amazon e commerce link https cseweb ucsd edu jmcauley datasets html amazon reviews bookcrossing book link http www2 informatik uni freiburg de cziegler bx goodreads book link https mengtingwan github io data goodreads html anime anime link https www kaggle com datasets cooperunion anime recommendations database pixelrec short video link https github com westlake repl pixelrec 2 2 benchmarks benchmarks webcite link paper amazon m2 kdd cup 2023 link https www aicrowd com challenges amazon kdd cup 23 multilingual recommendation challenge paper https arxiv org abs 2307 09688 openp5 link https github com agiresearch openp5 paper https arxiv org abs 2306 11134 tablet link https dylanslacks website tablet paper https arxiv org abs 2304 13188 3 related repositories repo name maintainer rs llm paper list https github com wwliu555 rs llm paper list wwliu555 https github com wwliu555 awesome recommend system pretraining papers https github com archersama awesome recommend system pretraining papers archersama https github com archersama llm4rec https github com wlik llm4rec wlik https github com wlik awesome llm4rs papers https github com nancheng58 awesome llm4rs papers nancheng58 https github com nancheng58 llm4ir survey https github com ruc nlpir llm4ir survey ruc nlpir https github com ruc nlpir contributing welcome to contribute to this repository if you have come across relevant resources or found some errors in this repesitory feel free to open an issue or submit a pull request contact chiangel dot ljh at gmail dot com citation article lin2023can title how can recommender systems benefit from large language models a survey author lin jianghao and dai xinyi and xi yunjia and liu weiwen and chen bo and li xiangyang and zhu chenxu and guo huifeng and yu yong and tang ruiming and others journal arxiv preprint arxiv 2306 05817 year 2023 | llm rs recsys large-language-models recommender-systems awesome llm4rec llm4rs | ai |
structure-and-interpretation-of-blockchain | bitcoin ethereum fabric img src https github com ice storm structure and interpretation of blockchain blob master img wechatimg294 png raw true width 50 alt https github com ice storm structure and interpretation of blockchain blob master preface md wechat ether net email wcwz020140 163 com blog qyuan top http qyuan top http product dangdang com 29260508 html https item jd com 13316098 html 2022 7 1 issue commit issue 24h https github com ysqi review it nerthus cto license book license cc by sa 3 0 license http creativecommons org licenses by sa 3 0 | blockchain-books ethereum bitcoinbook hyperledger-fabric | blockchain |
daily-cloud-md | daily cloud daily cloud track your mood everyday table of contents screenshots screenshots authentication authentication daily story daily story article article technologies and libraries technologies and libraries installation installation features features screenshots authentication onboarding login sign up assets 1 jpg raw true assets 2 jpg raw true assets 3 jpg raw true daily story home history story assets 4 jpg raw true assets 5 jpg raw true assets 6 jpg raw true camera result assets 7 jpg raw true assets 8 jpg raw true article articles article assets 9 jpg raw true assets 10 jpg raw true technologies and libraries jetpack compose material 3 dagger hilt firebase datastore coil retrofit gson tflite camerax exoplayer accompanist installation get the apk on the release https github com daily cloud daily cloud md releases page features login and sign up using email password and google detect depression based on story detect mood based on selfie content about mental health | android-studio kotlin | front_end |
Hungry-NYUAD | hungry nyuad purpose and goal of this project students at nyuad often pool together to make orders for delivery to saadiyat on a facebook page named hungry nyuad however the facebook has various drawbacks including but not limited to students cannot configure their notifications based on their interests not all students are on facebook or want to use facebook too much back and forth communication especially for large groups restaurant menu items prices and minimum orders are not shown on the facebook page our ios app by the same name hungry nyuad aims to eradicate the above problems faced by our beloved student body user stories and use cases as a student i want to sign up using my email so i can access the service acceptance criteria validate email address verify email address exists verify email address does not belong to another user validate password notify user on failed validation notify user via email on successful sign up user can exit sign up process no user data is saved unless all verification and validation succeeds definition of done passes all testing related to acceptance criteria use case primary actors students preconditions active internet connection basic flow of events the student clicks the sign up button the app responds by presenting a sign up screen with fields for name email address and password the student enters their name email address and password and clicks submit the email address is validated the password is validated the user account is created user is sent a confirmation email alternative flows email address validation fails user is prompted to enter a correct email address password validation fails user is prompted to enter a password that meets requirements confirmation email cannot be delivered user is prompted to enter an existing email address as a member of an order group i want to be able to contact other members in my group so that i can coordinate the pick up location of the order acceptance criteria only valid locations on campus premises can be selected every member of the group has the fair ability to participate in setting up the location notification about the chosen pickup location is sent to all members including those who did not participate in the process definition of done the option with most votes is set as the pick up location for the order passes testing as per acceptance criteria use case primary actors order owner members of the ordering group preconditions a member should be part of the specified ordering group basic flow of events any member of the group clicks the default location welcome center the member proposes a different pick up location the new proposed pick up location along with the default location are added as options for a poll in the conversation other members of the group can add further options members of the ordering group select one multiple or none of the multiple available options the poll for location is closed as the order owner finalizes the order for processing the option with most votes is chosen as the pick up location for the order the app includes the designated pick up location in the notification sent out when the order is placed alternative flows there is a tie between two or more locations order owner selects one of the competing options location is not validated the app discards the option and prompts the user to enter again as the person paying for the order upon delivery that is the order owner i want to send notifications to everyone that hasn t paid me back so that everyone pays their fair share and compensation is easy and efficient acceptance criteria see outstanding payments if any send push notification receive notification by student with outstanding payment see outstanding amount and to whom in notification validate that payment completed by both students definition of done passes all test as per acceptance criteria item able to demo feature use case primary actors order owner students who place orders with the order owner preconditions part of specific ordering group food has been delivered outstanding payments exist basic flow of events order has been delivered order owner opens hungry nyuad and looks at the details of their order the app will present the details of everyone in the order group including whether they have paid what they have owed to the order owner if there are outstanding orders in the group an option will appear called notify upon clicking notify a notification will be sent to users with outstanding payments notifications will give information on the amount owed group and order owner the owing student will then pay back the order in real cash and confirm that they paid through the app a notification will then be sent to the order owner that the amount has been paid upon confirming receipt the order details will be modified to show the new user state | os |
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WedocracyTest | wedocracytest mobile development | front_end |
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react-bootcamp | react js and redux bootcamp master react web development course logo course logo udemy png the official code for the react js and redux bootcamp master react web development course on udemy by david katz take the course here http udemy com react redux bootcamp http udemy com react redux bootcamp react by far has the most opportunity for frontend javascript frameworks in the industry today it s such an elegant technology learning react is very approachable with its short learning curve but it has enough depth to keep you long engaged years into your coding journey here are the main course highlights dive into react code right away create a react project from scratch without helper tools build five complete react applications learn redux the right way exploring not only what methods to use but why to use them and how they work create an app that features multi user functionality applying the publish subscribe pattern explore the optimal solution to get over the notorious cors error build your own backend apis an important layer for web development deploy your react applications to production | front_end |
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PyTorch-for-Deep-Learning-and-Computer-Vision | pytorch for deep learning and computer vision code repository for pytorch for deep learning and computer vision published by packt | ai |
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FullStack_Info_HW06 | fullstack info hw06 full stack web development hw06 sql | front_end |
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Embedded_Systems_Design | embedded systems design final work of the embedded systems design discipline of the university of santa maria | os |
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tailor | h1 img width 500 height 200 alt tailor src https rawgithub com zalando tailor master logo tailor logo svg h1 npm https nodei co npm node tailor png https npmjs org package node tailor build status https travis ci org zalando tailor svg branch master https travis ci org zalando tailor test coverage https codecov io github zalando tailor coverage svg precision 0 https codecov io github zalando tailor opentracing badge https img shields io badge opentracing enabled blue svg http opentracing io npm status downloads https img shields io npm dt node tailor svg https npmjs org package node tailor version https img shields io npm v node tailor svg https npmjs org package node tailor tailor is a layout service that uses streams to compose a web page from fragment services o reilly describes it in the title of this blog post https www oreilly com ideas better streaming layouts for frontend microservices with tailor as a library that provides a middleware which you can integrate into any node js server it s partially inspired by facebook s bigpipe https www facebook com notes facebook engineering bigpipe pipelining web pages for high performance 389414033919 but developed in an ecommerce context some of tailor s features and benefits composes pre rendered markup on the backend this is important for seo and fastens the initial render ensures a fast time to first byte tailor requests fragments in parallel and streams them as soon as possible without blocking the rest of the page enforces performance budget this is quite challenging otherwise because there is no single point where you can control performance fault tolerance render the meaningful output even if a page fragment has failed or timed out tailor is part of project mosaic https www mosaic9 org which aims to help developers create microservices for the frontend the mosaic also includes an extendable http router for service composition skipper https github com zalando skipper with related restful api that stores routes innkeeper https github com zalando innkeeper more components are in the pipeline for public release if your front end team is making the monolith to microservices transition you might find tailor and its available siblings beneficial why a layout service microservices get a lot of traction these days they allow multiple teams to work independently from each other choose their own technology stacks and establish their own release cycles unfortunately frontend development hasn t fully capitalized yet on the benefits that microservices offer the common practice for building websites remains the monolith a single frontend codebase that consumes multiple apis what if we could have microservices on the frontend this would allow frontend developers to work together with their backend counterparts on the same feature and independently deploy parts of the website fragments such as header product and footer bringing microservices to the frontend requires a layout service that composes a website out of fragments tailor was developed to solve this need installation begin using tailor with sh yarn add node tailor javascript const http require http const tailor require node tailor const tailor new tailor options const server http createserver tailor requesthandler server listen process env port 8080 options fetchcontext request function that returns a promise of the context that is an object that maps fragment id to fragment url to be able to override urls of the fragments on the page defaults to promise resolve fetchtemplate request parsetemplate function that should fetch the template call parsetemplate and return a promise of the result useful to implement your own way to retrieve and cache the templates e g from s3 default implementation lib fetch template js https github com zalando tailor blob master lib fetch template js fetches the template from the file system templatespath to specify the path where the templates are stored locally defaults to templates fragmenttag name of the fragment tag defaults to fragment handledtags an array of custom tags check tests handle tag https github com zalando tailor blob master tests handle tag js for more info handletag request tag options context receives a tag or closing tag and serializes it to a string or returns a stream filterrequestheaders attributes request function that filters the request headers that are passed to fragment request check default implementation in lib filter headers https github com zalando tailor blob master lib filter headers js filterresponseheaders attributes headers function that maps the given response headers from the primary fragment request to the final response maxassetlinks number of link header directives for css and js respected per fragment defaults to 1 requestfragment filterheaders url attributes request function that returns a promise of request to a fragment server check the default implementation in lib request fragment https github com zalando tailor blob master lib request fragment js amdloaderurl url to amd loader we use requirejs from cdnjs https cdnjs com libraries require js as default pipeinstancename pipe instance name that is available in the browser window for consuming frontend hooks pipeattributes attributes function that returns the minimal set of fragment attributes available on the frontend hooks https github com zalando tailor blob master docs hooks md tracer opentracing compliant tracer implementation https doc esdoc org github com opentracing opentracing javascript class src tracer js tracer html template tailor uses parse5 https github com inikulin parse5 to parse the template where it replaces each fragmenttag with a stream from the fragment server and handledtags with the result of handletag function html html head script type fragment src http assets domain com script head body fragment src http header domain com fragment fragment src http content domain com primary fragment fragment src http footer domain com async fragment body html fragment attributes id optional unique identifier autogenerated src url of the fragment primary denotes a fragment that sets the response code of the page timeout optional timeout of fragment in milliseconds default is 3000 async postpones the fragment until the end of body tag public to prevent tailor from forwarding filtered request headers from upstream to the fragments fallback src url of the fallback fragment in case of timeout error on the current fragment other attributes are allowed and will be passed as well to relevant functions eg filterrequestheaders filterresponseheaders etc fragment server a fragment is an http s server that renders only the part of the page and sets link header to provide urls to css and javascript resources check examples basic css and js index js https github com zalando tailor blob master examples basic css and js index js for a draft implementation a javascript of the fragment is an amd module that exports an init function that will be called with dom element of the fragment as an argument tailor will not follow redirects even if fragment response contains location header that is on purpose as redirects can introduce unwanted latency fragments with the attribute primary can do a redirect since it controls the status code of the page note for compatability with aws the link header can also be passed as x amz meta link passing information to fragments by default the incoming request will be used to selecting the template so to get the index html template you go to index if you want to listen to product my product 123 to go to product html template you can change the req url to product every header is filtered by default to avoid leaking information but you can give the original uri and host by adding it to the headers x request host and x request uri then reading in the fragment the headers to know what product to fetch and display javascript http createserver req res req headers x request uri req url req url index tailor requesthandler req res listen 8080 function console log tailor server listening on port 8080 concepts some of the concepts in tailor are described in detail on the specific docs events https github com zalando tailor blob master docs events md base templates https github com zalando tailor blob master docs base templates md hooks https github com zalando tailor blob master docs hooks md performance https github com zalando tailor blob master docs performance md opentracing tailor has out of the box distributed tracing instrumentation with opentracing https opentracing io it will pick up any span context on the ingress http request and propagate it to the existing remote procedure calls rpcs currently only the fetching of fragments is instrumented providing some additional details like the fragment tag attributes and some logging payload like the stack trace for errors examples sh get a copy of the repository git clone https github com zalando tailor git change to the folder cd tailor install dependencies yarn basic node examples basic css and js node examples basic css and js multiple fragments and amd node examples multiple fragments with custom amd fragment performance node examples fragment performance go to http localhost 8080 index http localhost 8080 index after running the specific example note please run the examples with node versions 6 0 0 single page application examples are also available https github com vigneshshanmugam tailor spa https github com tsnolan23 tailor react spa https github com shershen08 tailor vue demo benchmark to start running benchmark execute npm run benchmark and wait for couple of seconds to see the results contributing please check the contributing guidelines here https github com zalando tailor blob master contributing md | tailor layout-service streams mosaic microservice nodejs fragments web | front_end |
discord-react-clone | discord react clone preview https i imgur com 0upcir4 gif demo https rafaelalmeidatk github io discord react clone https rafaelalmeidatk github io discord react clone this project is a front end replication of discord s app made with react this is not a full replication so i added the most used parts of the app the main objective of this project was to learn react hooks and styled components so aside from normalize css this is the only library utilized some observations the styled scrollbars only work on webkit based browsers as it is a front end only project all the data is mocked in a single file i don t have the official font that discord utilizes so i utilized catamaran instead it is not very similar but isn t so bad all arts except the cats were extracted from the official app all the rights belong to discord running the project npm start or yarn start this will run the app in development mode open http localhost 3000 http localhost 3000 to view it in the browser | discord react | front_end |
Y1SEM2-IDB | sem2 idb introduction to databases in semester 2 | server |
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social-app | social network app laravel notes add accept application json in headers in all requests add authorization bearer token in headers in requests requires authentication example bearer 2 8upfgubeqirvyrht0ag72agueitceq54ethxtybb hr model endpoints requires authentication authentication login login register register no profile show show profile update update profile yes friend request friend requests friend requests send send friend request accept accept friend request remove remove friend request yes post index index post create create post update update post show show post delete delete post yes like like like yes comment comment comment yes user all users all uers single user single user yes post comments show all post comments show all post comments yes register create account url https social app laravel herokuapp com api register method post requires name name email email password password return token note save token and send in headers with authorization bearer saved token hr login login with registed account url https social app laravel herokuapp com api login method post requires email email password password return token note save token and send in headers with authorization bearer saved token hr show profile showing authenicated user data url https social app laravel herokuapp com api profile method get return data hr update profile login with registed account url https social app laravel herokuapp com api profile method post requires name string email email nullable password password avatar image return data hr friend requests all users that sent friend request to you url https social app laravel herokuapp com api friend requests method get return data hr send friend request send friend request to another user url https social app laravel herokuapp com api friend send method post requires friend id number return message note friend id is user id that you want to send friend request to him hr accept friend request accept friend request url https social app laravel herokuapp com api friend accept method post requires friend id number return message note friend id is user id u did sent friend request to him hr remove friend request remove friend request unfriended url https social app laravel herokuapp com api friend remove method delete requires friend id number return message note friend id is user id that you want to remove from your friends hr index post show all posts url https social app laravel herokuapp com api posts method get return data hr create post create post with authenticated user url https social app laravel herokuapp com api posts method post requires body text or image file png jpg return data note to create post you need to send body or image hr update post update post with authenticated user url https social app laravel herokuapp com api post id method patch requires body text or image file png jpg return data note to update post you need to send body or image hr show post show single post url https social app laravel herokuapp com api posts post id method get return data hr delete post show single post url https social app laravel herokuapp com api posts post id method delete return data hr like add like to post url https social app laravel herokuapp com api like method post requires post id number return data note if you add like to specific post and this has like from this user like will be removed from this post hr comment add comment to post url https social app laravel herokuapp com api comment method post requires post id number body text return data hr all users url https social app laravel herokuapp com api users method get return data hr single user url https social app laravel herokuapp com api users user id method get return data hr show all post comments url https social app laravel herokuapp com api posts post id comments method get return data hr | server |
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software_engineering_cloud | software engineering cloud | cloud |
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ce-gateway | spring cloud gateway ri application spring cloud gateway is in essence and already complete application framework that can be used for dynamic routing of requests from the gateway to different services downstream web spring cloud gateway local internal service spring cloud gateway also brings different features that can be of use authentication checking credentials and routing or not based on this api routing core feature used to make sure traffic is sent to the right internal service for handling the framework is highly customizable allowing developers to route traffic based on whatever rule they see fit rate limiting a common feature in front facing services so unsuprisingly rate limiting is part of the proposition this demo application configures route limiting using a redis backed datasource resiliency support for spring cloud resiliency features retry circuitbreaking timeouts run the application call the main function in the springcloudgatewayapplication class make sure you have docker running so the associated images can be bootstrapped role based routing feature spring cloud gateway does its work with filters a request comes in travels through a set of filters and the result of that pipeline is then fed back to the calling user filters allow for modifications before and after forwarding so they are very flexible in the example filter provided custom configuration is added to the application yaml the file containing the routing configuration a specific filter is applied which checks for the presence of a certain role and changes the host the request is forwarded to based on this information this is already a slightly more complex example as routing can be made dynamic based on certain available headers or any other strategies even randomization considerations the application rate limits heavily so if you try to send more than 1 request per second you will already see rate limiting being applied routing is done to non existent downstream services so you will get an error when the gateway tries to forward traffic to verify that this does in fact work configure a service to call or replace the uris with existing web addresses and see how the response is then proxied customize with care the conventions and defaults applied by spring cloud gateway generally fit most use cases in general it should not be required to customize the application too extensively try to stick with the defaults as much as possible as not doing this can cause bugs and performance issues that can have a big effect since once fully in place all requests would flow through the gateway | cloud |
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arduvision | arduvision embedded computer vision with arduino http therandomlab blogspot com es search label ov7670 | ai |
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MLQuestions | 65 machine learning interview questions 2023 a collection of technical interview questions for machine learning and computer vision engineering positions recently added natural language processing nlp interview questions 2023 https github com andrewekhalel mlquestions tree master nlp 1 what s the trade off between bias and variance src http houseofbots com news detail 2849 4 data science and machine learning interview questions if our model is too simple and has very few parameters then it may have high bias and low variance on the other hand if our model has large number of parameters then it s going to have high variance and low bias so we need to find the right good balance without overfitting and underfitting the data src https towardsdatascience com understanding the bias variance tradeoff 165e6942b229 2 what is gradient descent src http houseofbots com news detail 2849 4 data science and machine learning interview questions answer https machinelearningmastery com gradient descent for machine learning gradient descent is an optimization algorithm used to find the values of parameters coefficients of a function f that minimizes a cost function cost gradient descent is best used when the parameters cannot be calculated analytically e g using linear algebra and must be searched for by an optimization algorithm 3 explain over and under fitting and how to combat them src http houseofbots com news detail 2849 4 data science and machine learning interview questions answer https towardsdatascience com overfitting vs underfitting a complete example d05dd7e19765 ml dl models essentially learn a relationship between its given inputs called training features and objective outputs called labels regardless of the quality of the learned relation function its performance on a test set a collection of data different from the training input is subject to investigation most ml dl models have trainable parameters which will be learned to build that input output relationship based on the number of parameters each model has they can be sorted into more flexible more parameters to less flexible less parameters the problem of underfitting arises when the flexibility of a model its number of parameters is not adequate to capture the underlying pattern in a training dataset overfitting on the other hand arises when the model is too flexible to the underlying pattern in the later case it is said that the model has memorized the training data an example of underfitting is estimating a second order polynomial quadratic function with a first order polynomial a simple line similarly estimating a line with a 10th order polynomial would be an example of overfitting 4 how do you combat the curse of dimensionality src http houseofbots com news detail 2849 4 data science and machine learning interview questions feature selection manual or via statistical methods principal component analysis pca multidimensional scaling locally linear embedding src https towardsdatascience com why and how to get rid of the curse of dimensionality right with breast cancer dataset 7d528fb5f6c0 5 what is regularization why do we use it and give some examples of common methods src http houseofbots com news detail 2849 4 data science and machine learning interview questions a technique that discourages learning a more complex or flexible model so as to avoid the risk of overfitting examples ridge l2 norm lasso l1 norm the obvious disadvantage of ridge regression is model interpretability it will shrink the coefficients for least important predictors very close to zero but it will never make them exactly zero in other words the final model will include all predictors however in the case of the lasso the l1 penalty has the effect of forcing some of the coefficient estimates to be exactly equal to zero when the tuning parameter is sufficiently large therefore the lasso method also performs variable selection and is said to yield sparse models src https towardsdatascience com regularization in machine learning 76441ddcf99a 6 explain principal component analysis pca src http houseofbots com news detail 2849 4 data science and machine learning interview questions answer https towardsdatascience com a one stop shop for principal component analysis 5582fb7e0a9c principal component analysis pca is a dimensionality reduction technique used in machine learning to reduce the number of features in a dataset while retaining as much information as possible it works by identifying the directions principal components in which the data varies the most and projecting the data onto a lower dimensional subspace along these directions 7 why is relu better and more often used than sigmoid in neural networks src http houseofbots com news detail 2849 4 data science and machine learning interview questions computation efficiency as relu is a simple threshold the forward and backward path will be faster reduced likelihood of vanishing gradient gradient of relu is 1 for positive values and 0 for negative values while sigmoid activation saturates gradients close to 0 quickly with slightly higher or lower inputs leading to vanishing gradients sparsity sparsity happens when the input of relu is negative this means fewer neurons are firing sparse activation and the network is lighter src1 https medium com the theory of everything understanding activation functions in neural networks 9491262884e0 src2 https stats stackexchange com questions 126238 what are the advantages of relu over sigmoid function in deep neural networks 8 given stride s and kernel sizes for each layer of a 1 dimensional cnn create a function to compute the receptive field https www quora com what is a receptive field in a convolutional neural network of a particular node in the network this is just finding how many input nodes actually connect through to a neuron in a cnn src https www reddit com r computervision comments 7gku4z technical interview questions in cv the receptive field are defined portion of space within an inputs that will be used during an operation to generate an output considering a cnn filter of size k the receptive field of a peculiar layer is only the number of input used by the filter in this case k multiplied by the dimension of the input that is not being reduced by the convolutionnal filter a this results in a receptive field of k a more visually in the case of an image of size 32x32x3 with a cnn with a filter size of 5x5 the corresponding recpetive field will be the the filter size 5 multiplied by the depth of the input volume the rgb colors which is the color dimensio this thus gives us a recpetive field of dimension 5x5x3 9 implement connected components http aishack in tutorials labelling connected components example on an image matrix src https www reddit com r computervision comments 7gku4z technical interview questions in cv 10 implement a sparse matrix class in c src https www reddit com r computervision comments 7gku4z technical interview questions in cv answer https www geeksforgeeks org sparse matrix representation 11 create a function to compute an integral image https en wikipedia org wiki summed area table and create another function to get area sums from the integral image src https www reddit com r computervision comments 7gku4z technical interview questions in cv answer https www geeksforgeeks org submatrix sum queries 12 how would you remove outliers when trying to estimate a flat plane from noisy samples src https www reddit com r computervision comments 7gku4z technical interview questions in cv random sample consensus ransac is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers when outliers are to be accorded no influence on the values of the estimates src https en wikipedia org wiki random sample consensus 13 how does cbir https www robots ox ac uk vgg publications 2013 arandjelovic13 arandjelovic13 pdf work src https www reddit com r computervision comments 7gku4z technical interview questions in cv answer https en wikipedia org wiki content based image retrieval content based image retrieval is the concept of using images to gather metadata on their content compared to the current image retrieval approach based on the keywords associated to the images this technique generates its metadata from computer vision techniques to extract the relevant informations that will be used during the querying step many approach are possible from feature detection to retrieve keywords to the usage of cnn to extract dense features that will be associated to a known distribution of keywords with this last approach we care less about what is shown on the image but more about the similarity between the metadata generated by a known image and a list of known label and or tags projected into this metadata space 14 how does image registration work sparse vs dense optical flow http www ncorr com download publications bakerunify pdf and so on src https www reddit com r computervision comments 7gku4z technical interview questions in cv 15 describe how convolution works what about if your inputs are grayscale vs rgb imagery what determines the shape of the next layer src https www reddit com r computervision comments 7gku4z technical interview questions in cv in a convolutional neural network cnn the convolution operation is applied to the input image using a small matrix called a kernel or filter the kernel slides over the image in small steps called strides and performs element wise multiplications with the corresponding elements of the image and then sums up the results the output of this operation is called a feature map when the input is rgb or more than 3 channels the sliding window will be a sliding cube the shape of the next layer is determined by kernel size number of kernels stride padding and dialation src1 https dev to sandeepbalachandran machine learning convolution with color images 2p41 src2 https stackoverflow com questions 70231487 output dimensions of convolution in pytorch 16 talk me through how you would create a 3d model of an object from imagery and depth sensor measurements taken at all angles around the object src https www reddit com r computervision comments 7gku4z technical interview questions in cv there are two popular methods for 3d reconstruction structure from motion sfm src https www mathworks com help vision ug structure from motion html multi view stereo mvs src https www youtube com watch v zwwty2qpns8 sfm is better suited for creating models of large scenes while mvs is better suited for creating models of small objects 17 implement sqrt const double x without using any special functions just fundamental arithmetic src https www reddit com r computervision comments 7gku4z technical interview questions in cv the taylor series can be used for this step by providing an approximation of sqrt x answer https math stackexchange com questions 732540 taylor series of sqrt1x using sigma notation 18 reverse a bitstring src https www reddit com r computervision comments 7gku4z technical interview questions in cv if you are using python3 data b xad xde xde xc0 my data bytearray data my data reverse 19 implement non maximal suppression as efficiently as you can src https www reddit com r computervision comments 7gku4z technical interview questions in cv non maximum suppression nms is a technique used to eliminate multiple detections of the same object in a given image to solve that first sort bounding boxes based on their scores n logn starting with the box with the highest score remove boxes whose overlapping metric iou is greater than a certain threshold n 2 to optimize this solution you can use special data structures to query for overlapping boxes such as r tree or kd tree n logn src https towardsdatascience com non maxima suppression 139f7e00f0b5 20 reverse a linked list in place src https www reddit com r computervision comments 7gku4z technical interview questions in cv answer https www geeksforgeeks org reverse a linked list 21 what is data normalization and why do we need it src http houseofbots com news detail 2849 4 data science and machine learning interview questions data normalization is very important preprocessing step used to rescale values to fit in a specific range to assure better convergence during backpropagation in general it boils down to subtracting the mean of each data point and dividing by its standard deviation if we don t do this then some of the features those with high magnitude will be weighted more in the cost function if a higher magnitude feature changes by 1 then that change is pretty big but for smaller features it s quite insignificant the data normalization makes all features weighted equally 22 why do we use convolutions for images rather than just fc layers src http houseofbots com news detail 2849 4 data science and machine learning interview questions firstly convolutions preserve encode and actually use the spatial information from the image if we used only fc layers we would have no relative spatial information secondly convolutional neural networks cnns have a partially built in translation in variance since each convolution kernel acts as it s own filter feature detector 23 what makes cnns translation invariant src http houseofbots com news detail 2849 4 data science and machine learning interview questions as explained above each convolution kernel acts as it s own filter feature detector so let s say you re doing object detection it doesn t matter where in the image the object is since we re going to apply the convolution in a sliding window fashion across the entire image anyways 24 why do we have max pooling in classification cnns src http houseofbots com news detail 2849 4 data science and machine learning interview questions for a role in computer vision max pooling in a cnn allows you to reduce computation since your feature maps are smaller after the pooling you don t lose too much semantic information since you re taking the maximum activation there s also a theory that max pooling contributes a bit to giving cnns more translation in variance check out this great video from andrew ng on the benefits of max pooling https www coursera org learn convolutional neural networks lecture helhk pooling layers 25 why do segmentation cnns typically have an encoder decoder style structure src http houseofbots com news detail 2849 4 data science and machine learning interview questions the encoder cnn can basically be thought of as a feature extraction network while the decoder uses that information to predict the image segments by decoding the features and upscaling to the original image size 26 what is the significance of residual networks src http houseofbots com news detail 2849 4 data science and machine learning interview questions the main thing that residual connections did was allow for direct feature access from previous layers this makes information propagation throughout the network much easier one very interesting paper about this shows how using local skip connections gives the network a type of ensemble multi path structure giving features multiple paths to propagate throughout the network 27 what is batch normalization and why does it work src http houseofbots com news detail 2849 4 data science and machine learning interview questions training deep neural networks is complicated by the fact that the distribution of each layer s inputs changes during training as the parameters of the previous layers change the idea is then to normalize the inputs of each layer in such a way that they have a mean output activation of zero and standard deviation of one this is done for each individual mini batch at each layer i e compute the mean and variance of that mini batch alone then normalize this is analogous to how the inputs to networks are standardized how does this help we know that normalizing the inputs to a network helps it learn but a network is just a series of layers where the output of one layer becomes the input to the next that means we can think of any layer in a neural network as the first layer of a smaller subsequent network thought of as a series of neural networks feeding into each other we normalize the output of one layer before applying the activation function and then feed it into the following layer sub network 28 why would you use many small convolutional kernels such as 3x3 rather than a few large ones src http houseofbots com news detail 2849 4 data science and machine learning interview questions this is very well explained in the vggnet paper https arxiv org pdf 1409 1556 pdf there are 2 reasons first you can use several smaller kernels rather than few large ones to get the same receptive field and capture more spatial context but with the smaller kernels you are using less parameters and computations secondly because with smaller kernels you will be using more filters you ll be able to use more activation functions and thus have a more discriminative mapping function being learned by your cnn 29 why do we need a validation set and test set what is the difference between them src https www toptal com machine learning interview questions when training a model we divide the available data into three separate sets the training dataset is used for fitting the model s parameters however the accuracy that we achieve on the training set is not reliable for predicting if the model will be accurate on new samples the validation dataset is used to measure how well the model does on examples that weren t part of the training dataset the metrics computed on the validation data can be used to tune the hyperparameters of the model however every time we evaluate the validation data and we make decisions based on those scores we are leaking information from the validation data into our model the more evaluations the more information is leaked so we can end up overfitting to the validation data and once again the validation score won t be reliable for predicting the behaviour of the model in the real world the test dataset is used to measure how well the model does on previously unseen examples it should only be used once we have tuned the parameters using the validation set so if we omit the test set and only use a validation set the validation score won t be a good estimate of the generalization of the model 30 what is stratified cross validation and when should we use it src https www toptal com machine learning interview questions cross validation is a technique for dividing data between training and validation sets on typical cross validation this split is done randomly but in stratified cross validation the split preserves the ratio of the categories on both the training and validation datasets for example if we have a dataset with 10 of category a and 90 of category b and we use stratified cross validation we will have the same proportions in training and validation in contrast if we use simple cross validation in the worst case we may find that there are no samples of category a in the validation set stratified cross validation may be applied in the following scenarios on a dataset with multiple categories the smaller the dataset and the more imbalanced the categories the more important it will be to use stratified cross validation on a dataset with data of different distributions for example in a dataset for autonomous driving we may have images taken during the day and at night if we do not ensure that both types are present in training and validation we will have generalization problems 31 why do ensembles typically have higher scores than individual models src https www toptal com machine learning interview questions an ensemble is the combination of multiple models to create a single prediction the key idea for making better predictions is that the models should make different errors that way the errors of one model will be compensated by the right guesses of the other models and thus the score of the ensemble will be higher we need diverse models for creating an ensemble diversity can be achieved by using different ml algorithms for example you can combine logistic regression k nearest neighbors and decision trees using different subsets of the data for training this is called bagging giving a different weight to each of the samples of the training set if this is done iteratively weighting the samples according to the errors of the ensemble it s called boosting many winning solutions to data science competitions are ensembles however in real life machine learning projects engineers need to find a balance between execution time and accuracy 32 what is an imbalanced dataset can you list some ways to deal with it src https www toptal com machine learning interview questions an imbalanced dataset is one that has different proportions of target categories for example a dataset with medical images where we have to detect some illness will typically have many more negative samples than positive samples say 98 of images are without the illness and 2 of images are with the illness there are different options to deal with imbalanced datasets oversampling or undersampling instead of sampling with a uniform distribution from the training dataset we can use other distributions so the model sees a more balanced dataset data augmentation we can add data in the less frequent categories by modifying existing data in a controlled way in the example dataset we could flip the images with illnesses or add noise to copies of the images in such a way that the illness remains visible using appropriate metrics in the example dataset if we had a model that always made negative predictions it would achieve a precision of 98 there are other metrics such as precision recall and f score that describe the accuracy of the model better when using an imbalanced dataset 33 can you explain the differences between supervised unsupervised and reinforcement learning src https www toptal com machine learning interview questions in supervised learning we train a model to learn the relationship between input data and output data we need to have labeled data to be able to do supervised learning with unsupervised learning we only have unlabeled data the model learns a representation of the data unsupervised learning is frequently used to initialize the parameters of the model when we have a lot of unlabeled data and a small fraction of labeled data we first train an unsupervised model and after that we use the weights of the model to train a supervised model in reinforcement learning the model has some input data and a reward depending on the output of the model the model learns a policy that maximizes the reward reinforcement learning has been applied successfully to strategic games such as go and even classic atari video games 34 what is data augmentation can you give some examples src https www toptal com machine learning interview questions data augmentation is a technique for synthesizing new data by modifying existing data in such a way that the target is not changed or it is changed in a known way computer vision is one of fields where data augmentation is very useful there are many modifications that we can do to images resize horizontal or vertical flip rotate add noise deform modify colors each problem needs a customized data augmentation pipeline for example on ocr doing flips will change the text and won t be beneficial however resizes and small rotations may help 35 what is turing test src https intellipaat com interview question artificial intelligence interview questions the turing test is a method to test the machine s ability to match the human level intelligence a machine is used to challenge the human intelligence that when it passes the test it is considered as intelligent yet a machine could be viewed as intelligent without sufficiently knowing about people to mimic a human 36 what is precision precision also called positive predictive value is the fraction of relevant instances among the retrieved instances precision true positive true positive false positive src https en wikipedia org wiki precision and recall 37 what is recall recall also known as sensitivity is the fraction of relevant instances that have been retrieved over the total amount of relevant instances recall true positive true positive false negative src https en wikipedia org wiki precision and recall 38 define f1 score src https intellipaat com interview question artificial intelligence interview questions it is the weighted average of precision and recall it considers both false positive and false negative into account it is used to measure the model s performance f1 score 2 precision recall precision recall 39 what is cost function src https intellipaat com interview question artificial intelligence interview questions cost function is a scalar functions which quantifies the error factor of the neural network lower the cost function better the neural network eg mnist data set to classify the image input image is digit 2 and the neural network wrongly predicts it to be 3 40 list different activation neurons or functions src https intellipaat com interview question artificial intelligence interview questions linear neuron binary threshold neuron stochastic binary neuron sigmoid neuron tanh function rectified linear unit relu 41 define learning rate learning rate is a hyper parameter that controls how much we are adjusting the weights of our network with respect the loss gradient src https en wikipedia org wiki learning rate 42 what is momentum w r t nn optimization momentum lets the optimization algorithm remembers its last step and adds some proportion of it to the current step this way even if the algorithm is stuck in a flat region or a small local minimum it can get out and continue towards the true minimum src https www quora com what is the difference between momentum and learning rate 43 what is the difference between batch gradient descent and stochastic gradient descent batch gradient descent computes the gradient using the whole dataset this is great for convex or relatively smooth error manifolds in this case we move somewhat directly towards an optimum solution either local or global additionally batch gradient descent given an annealed learning rate will eventually find the minimum located in it s basin of attraction stochastic gradient descent sgd computes the gradient using a single sample sgd works well not well i suppose but better than batch gradient descent for error manifolds that have lots of local maxima minima in this case the somewhat noisier gradient calculated using the reduced number of samples tends to jerk the model out of local minima into a region that hopefully is more optimal src https stats stackexchange com questions 49528 batch gradient descent versus stochastic gradient descent 44 epoch vs batch vs iteration epoch one forward pass and one backward pass of all the training examples batch examples processed together in one pass forward and backward iteration number of training examples batch size 45 what is vanishing gradient src https intellipaat com interview question artificial intelligence interview questions as we add more and more hidden layers back propagation becomes less and less useful in passing information to the lower layers in effect as information is passed back the gradients begin to vanish and become small relative to the weights of the networks 46 what are dropouts src https intellipaat com interview question artificial intelligence interview questions dropout is a simple way to prevent a neural network from overfitting it is the dropping out of some of the units in a neural network it is similar to the natural reproduction process where the nature produces offsprings by combining distinct genes dropping out others rather than strengthening the co adapting of them 47 define lstm src https intellipaat com interview question artificial intelligence interview questions long short term memory are explicitly designed to address the long term dependency problem by maintaining a state what to remember and what to forget 48 list the key components of lstm src https intellipaat com interview question artificial intelligence interview questions gates forget memory update read tanh x values between 1 to 1 sigmoid x values between 0 to 1 49 list the variants of rnn src https intellipaat com interview question artificial intelligence interview questions lstm long short term memory gru gated recurrent unit end to end network memory network 50 what is autoencoder name few applications src https intellipaat com interview question artificial intelligence interview questions auto encoder is basically used to learn a compressed form of given data few applications include data denoising dimensionality reduction image reconstruction image colorization 51 what are the components of gan src https intellipaat com interview question artificial intelligence interview questions generator discriminator 52 what s the difference between boosting and bagging boosting and bagging are similar in that they are both ensembling techniques where a number of weak learners classifiers regressors that are barely better than guessing combine through averaging or max vote to create a strong learner that can make accurate predictions bagging means that you take bootstrap samples with replacement of your data set and each sample trains a potentially weak learner boosting on the other hand uses all data to train each learner but instances that were misclassified by the previous learners are given more weight so that subsequent learners give more focus to them during training src https www quora com whats the difference between boosting and bagging 53 explain how a roc curve works src https www springboard com blog machine learning interview questions the roc curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds it s often used as a proxy for the trade off between the sensitivity of the model true positives vs the fall out or the probability it will trigger a false alarm false positives 54 what s the difference between type i and type ii error src https www springboard com blog machine learning interview questions type i error is a false positive while type ii error is a false negative briefly stated type i error means claiming something has happened when it hasn t while type ii error means that you claim nothing is happening when in fact something is a clever way to think about this is to think of type i error as telling a man he is pregnant while type ii error means you tell a pregnant woman she isn t carrying a baby 55 what s the difference between a generative and discriminative model src https www springboard com blog machine learning interview questions a generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data discriminative models will generally outperform generative models on classification tasks 56 instance based versus model based learning instance based learning the system learns the examples by heart then generalizes to new cases using a similarity measure model based learning another way to generalize from a set of examples is to build a model of these examples then use that model to make predictions this is called model based learning src https medium com sanidhyaagrawal08 what is instance based and model based learning s1e10 8e68364ae084 57 when to use a label encoding vs one hot encoding this question generally depends on your dataset and the model which you wish to apply but still a few points to note before choosing the right encoding technique for your model we apply one hot encoding when the categorical feature is not ordinal like the countries above the number of categorical features is less so one hot encoding can be effectively applied we apply label encoding when the categorical feature is ordinal like jr kg sr kg primary school high school the number of categories is quite large as one hot encoding can lead to high memory consumption src https www analyticsvidhya com blog 2020 03 one hot encoding vs label encoding using scikit learn 58 what is the difference between lda and pca for dimensionality reduction both lda and pca are linear transformation techniques lda is a supervised whereas pca is unsupervised pca ignores class labels we can picture pca as a technique that finds the directions of maximal variance in contrast to pca lda attempts to find a feature subspace that maximizes class separability src https sebastianraschka com faq docs lda vs pca html 59 what is t sne t distributed stochastic neighbor embedding t sne is an unsupervised non linear technique primarily used for data exploration and visualizing high dimensional data in simpler terms t sne gives you a feel or intuition of how the data is arranged in a high dimensional space src https towardsdatascience com an introduction to t sne with python example 5a3a293108d1 60 what is the difference between t sne and pca for dimensionality reduction the first thing to note is that pca was developed in 1933 while t sne was developed in 2008 a lot has changed in the world of data science since 1933 mainly in the realm of compute and size of data second pca is a linear dimension reduction technique that seeks to maximize variance and preserves large pairwise distances in other words things that are different end up far apart this can lead to poor visualization especially when dealing with non linear manifold structures think of a manifold structure as any geometric shape like cylinder ball curve etc t sne differs from pca by preserving only small pairwise distances or local similarities whereas pca is concerned with preserving large pairwise distances to maximize variance src https towardsdatascience com an introduction to t sne with python example 5a3a293108d1 61 what is umap umap uniform manifold approximation and projection is a novel manifold learning technique for dimension reduction umap is constructed from a theoretical framework based in riemannian geometry and algebraic topology the result is a practical scalable algorithm that applies to real world data src https arxiv org abs 1802 03426 text umap 20 62 what is the difference between t sne and umap for dimensionality reduction the biggest difference between the output of umap when compared with t sne is this balance between local and global structure umap is often better at preserving global structure in the final projection this means that the inter cluster relations are potentially more meaningful than in t sne however it s important to note that because umap and t sne both necessarily warp the high dimensional shape of the data when projecting to lower dimensions any given axis or distance in lower dimensions still isn t directly interpretable in the way of techniques such as pca src https pair code github io understanding umap 63 how random number generator works e g rand function in python works it generates a pseudo random number based on the seed and there are some famous algorithm please see below link for further information on this src https en wikipedia org wiki linear congruential generator 64 given that we want to evaluate the performance of n different machine learning models on the same data why would the following splitting mechanism be incorrect def get splits df pd dataframe rnd np random rand len df train df rnd 0 8 valid df rnd 0 8 rnd 0 9 test df rnd 0 9 return train valid test model 1 from sklearn tree import decisiontreeclassifier train valid test get splits model 2 from sklearn linear model import logisticregression train valid test get splits the rand function orders the data differently each time it is run so if we run the splitting mechanism again the 80 of the rows we get will be different from the ones we got the first time it was run this presents an issue as we need to compare the performance of our models on the same test set in order to ensure reproducible and consistent sampling we would have to set the random seed in advance or store the data once it is split alternatively we could simply set the random state parameter in sklearn s train test split function in order to get the same train validation and test sets across different executions src https towardsdatascience com why do we set a random state in machine learning models bb2dc68d8431 text in 20scikit 2dlearn 2c 20the 20random random 20state 20instance 20from 20np 65 what is the difference between bayesian vs frequentist statistics src https www kdnuggets com 2022 10 nlp interview questions html frequentist statistics is a framework that focuses on estimating population parameters using sample statistics and providing point estimates and confidence intervals bayesian statistics on the other hand is a framework that uses prior knowledge and information to update beliefs about a parameter or hypothesis and provides probability distributions for parameters the main difference is that bayesian statistics incorporates prior knowledge and beliefs into the analysis while frequentist statistics doesn t contributions contributions are most welcomed 1 fork the repository 2 commit your questions or answers 3 open pull request preparation resources 1 all of statistics a concise course in statistical inference https amzn to 3r87wga by larry wasserman 2 machine learning https amzn to 3rdifk3 by tom mitchell 3 designing machine learning systems an iterative process for production ready applications https amzn to 3livgd2 by chip huyen | ai |
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getting-started-react-native | getting started react native getting started with react native for mobile development readme screenshot screenshots readme screenshot png presentation slides click here https docs google com presentation d 1fqmsvoddo 2zy1dfsjrbrt3v3pcikbqpt9odgpzzl58 edit usp sharing quick guide ios on mac os x you will need to have brew and xcode already installed make sure to also have the chrome browser installed install node js brew install node install watchman brew install watchman install react native comand line tools npm install g react native cli i suggest you use atom https atom io instead of sublime due to better syntax highlighting for javascript now you can create a new project react native init myproject go to the project directory and run it cd myproject react native run ios the entry point for your app will be in the file index ios js note running the finished myproject example in this repository if you wish to run the example app in this repository without creating your own project cd to myproject and run npm install that will install all the package dependencies necessary for the app then follow the instructions above to call react native run ios step by step guide building first app click here guide md | react-native | front_end |
system-design-questions | system design questions the repository contains a set of problem statements around software architecture and system design as conducted by arpit s system design masterclass https arpitbhayani me masterclass questions design a blogging platform blogging platform md design online offline indicator online offline indicator md design airline check in airline checkin md design sql backed kv store sql kv md design slack s realtime communication new design a load balancer load balancer md design synchronized queue consumers queue consumers md design an image service image service md design a hashtag service hashtag service md design onepic onepic md design photo tagging tagging photos with people md design user affinity user affinity md design newly unread message indicator newly unread indicator md design a distributed cache distributed cache md design a word dictionary word dictionary md design a superfast kv store superfast kv md design s3 s3 md design a faster superfast kv store faster superfast kv md design a video processing pipeline for steaming service video pipeline md design a text based search engine text search engine md design a service that serves recent searches for a user recent searches md design a text based cricket commentary service live commentary md design a sql backed message broker sql broker md design a distributed task scheduler task scheduler md design flash sale flash sale md design counting impressions at scale counting impressions md designing a remote file sync service file sync md designing a who s near me service near me md questions that i do not cover anymore designing a realtime db realtime db md arpit s system design masterclass a masterclass that helps you become great at designing scalable fault tolerant and highly available systems the program this is a prime and intermediate level cohort based course aimed at providing an exclusive and crisp learning experience the program will cover most of the topics under system design and software architecture including but not limited to architecting social networks building storage engines and designing high throughput systems the program will have a blend of live classes happening on weekends 4 to 6 30 pm ist 1 1 mentorship sessions happening on weekdays and assignments the program is designed to be intense and crisp to accelerate learning highlights the course has been taken up by 200 people spanning 7 countries the nps of the course is 89 people from companies like tesla amazon microsoft google yelp github flipkart practo grab paypal and many more have taken up this course hi i m arpit bhayani img width 256px src https arpitbhayani me static img arpit jpg in my last 9 years of experience i have worked at d e shaw practo amazon and unacademy and have built systems services and platforms that scaled to billions post my masters in cse from iiit hyderabad i joined d e shaw for a short stint of 2 months before moving to practo and working there as a platform engineer building and owning close to 8 different microservices post practo i worked at amazon on their primary mission critical e commerce database and built data pipelines that cold tiered the stale data after quitting amazon in 2018 i joined unacademy as their first technical architect and there i designed built managed and scaled services like search notification logging deployment engine and many more i have now transitioned into the role of a sr engineering manager leading the site reliability vertical in january 2020 i started my newsletter https arpitbhayani me newsletter where i write and share an essay about programming languages internals deep dives on some super clever algorithms and few tips on building scalable distributed systems the newsletter currently has close to 2000 subscribers recently i have started building revine https revine arpitbhayani me a programming langauge for kids helping them develop logic through animations and spark their creativity through artwork center a target blank href https arpitbhayani me masterclass img src https user images githubusercontent com 4745789 137859181 d4499cf4 ce65 4466 8b88 a078ece0f081 png width 300px a center | system-design-project system-design system-design-interview system-design-questions coursework distributed-systems hacktoberfest | os |
arduino-aliyun-iot-sdk | arduino toplevel client for aliyun iot platform aliyuniotsdk iot esp8266 nodemcu 1 0 update v0 2 buffer 5 10 buffer v0 1 arduino arduinojson crypto pubsubclient esp8266 arduino esp8266 https github com esp8266 arduino usage c wifi include esp8266wifi h static wificlient espclient iot sdk include aliyuniotsdk h define product key xxx define device name device d define device secret xxxxxxxxxxxxxx define region id cn shanghai wifi define wifi ssid xxxxx define wifi passwd xxxxx void setup serial begin 115200 wifi wifiinit wifi ssid wifi passwd iot wifi client aliyuniotsdk begin espclient product key device name device secret region id powercallback powerswitch id aliyuniotsdk binddata powerswitch powercallback lightluminance id aliyuniotsdk send lightluminance 100 void loop aliyuniotsdk loop wifi void wifiinit const char ssid const char passphrase wifi mode wifi sta wifi begin ssid passphrase while wifi status wl connected delay 1000 serial println wifi not connect serial println connected to ap void powercallback jsonvariant p int powerswitch p powerswitch if powerswitch 1 api c loop static void loop param ssid wifi param passphrase wifi static void begin client espclient const char productkey const char devicename const char devicesecret const char region param param json key value static void send const char param float param key key param number static void send char key float number int param key key param number static void send char key int number double param key key param number static void send char key double number string param key key param text static void send char key char text param eventid param param json key value static void sendevent const char eventid const char param param eventid static void sendevent const char eventid static void bind mqtt callback signature param eventid static void bindevent const char eventid mqtt callback signature key param key key static int binddata char key poniter fun fp key param key key static int unbinddata char key examples buiding limitations wifi client pubsubclient pubsubclient pubsubclient mqtt max packet size 1024 pubsubclient mqtt keepalive 60 id 5000ms check interval compatible hardware sdk pubsubclient pubsubclient the library uses the arduino ethernet client api for interacting with the underlying network hardware this means it just works with a growing number of boards and shields including arduino ethernet arduino ethernet shield arduino yun use the included yunclient in place of ethernetclient and be sure to do a bridge begin first arduino wifi shield if you want to send packets 90 bytes with this shield enable the mqtt max transfer size define in pubsubclient h sparkfun wifly shield library ti cc3000 wifi library intel galileo edison esp8266 esp32 the library cannot currently be used with hardware based on the enc28j60 chip such as the nanode or the nuelectronics ethernet shield for those there is an alternative library available license this code is released under the mit license | server |
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lqmoonxiaobaibai.github.io | welcome to github pages you can use the editor on github https github com lqmoonxiaobaibai lqmoonxiaobaibai github io edit master readme md to maintain and preview the content for your website in markdown files whenever you commit to this repository github pages will run jekyll https jekyllrb com to rebuild the pages in your site from the content in your markdown files markdown markdown is a lightweight and easy to use syntax for styling your writing it includes conventions for markdown syntax highlighted code block header 1 header 2 header 3 bulleted list 1 numbered 2 list bold and italic and code text link url and image src for more details see github flavored markdown https guides github com features mastering markdown jekyll themes your pages site will use the layout and styles from the jekyll theme you have selected in your repository settings https github com lqmoonxiaobaibai lqmoonxiaobaibai github io settings the name of this theme is saved in the jekyll config yml configuration file support or contact having trouble with pages check out our documentation https help github com categories github pages basics or contact support https github com contact and we ll help you sort it out | server |
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White-Jotter-Vue | wjlogo png https i loli net 2019 12 15 synutirduwafggo png lisense https img shields io github license antabot white jotter vue build status https www travis ci org antabot white jotter vue svg branch master vue js springboot web 0 https github com antabot white jotter vue https github com antabot white jotter vue https github com antabot white jotter https github com antabot white jotter https i loli net 2020 01 17 hyswdm7wdff8ekc png jetbrains idea java ide a href https www jetbrains com from white jotter img src https i loli net 2020 06 15 wfyv6jgx8f9rphb png width 200 height 216 alt align center a 1 slogan https img blog csdnimg cn 20190403215932913 png 2 https i loli net 2019 12 03 aglbiupct68thbd png 3 png https i loli net 2020 01 20 vasoapuwrib6rft png png https i loli net 2020 01 20 dqgbpy2lkhizc4x png 4 dashboard https img blog csdnimg cn 20191202200516251 png 1 1 vue 2 elementui 3 axios 2 1 spring boot 2 spring data jpa 3 mysql 4 shiro 1 clone git clone https github com antabot white jotter vue git clone https github com antabot white jotter 2 mysql wj application properties spring datasource initialization mode always wj sql src main resources mysql redis 6379 3 src main resources application properties mysql 8 0 15 4 intellij idea pom xml maven reimport http localhost 8080 admin 123 5 npm install localhost 8080 npm run dev wj vue springboot http localhost 8080 springboot springboot 6 webstorm wj vue wj vue npm run build wj vue dist static index html wj resources static wj http localhost 8443 https learner blog csdn net article details 97619312 csdn 1 https blog csdn net neuf soleil article details 88925013 2 cli vue js https blog csdn net neuf soleil article details 88926242 3 https blog csdn net neuf soleil article details 88955387 4 https blog csdn net neuf soleil article details 89294300 5 element https blog csdn net neuf soleil article details 89298717 6 https learner blog csdn net article details 89422585 7 https learner blog csdn net article details 89853305 8 https learner blog csdn net article details 92413933 9 https learner blog csdn net article details 95310666 10 https learner blog csdn net article details 97619312 11 https learner blog csdn net article details 100849732 12 https learner blog csdn net article details 101121899 13 shiro https learner blog csdn net article details 102690035 14 https learner blog csdn net article details 102788866 15 https learner blog csdn net article details 103114893 16 https learner blog csdn net article details 103250775 17 https learner blog csdn net article details 103603726 18 https learner blog csdn net article details 104033436 19 https learner blog csdn net article details 104763090 2020 04 05 01 20 markdown 2019 12 01 11 30 vue elment admin 11 17 04 27 mysql 8 0 15 04 13 04 11 04 08 04 06 04 05 04 04 04 03 | vue single-page-app elment-ui | front_end |
Car-License-Plate-Recognition | car license plate recognition download the video for testing from a href https drive google com open id 1jd8wvkhyxfismo390easodpkzahonrnd here a for detailed explanation and requirements check my article geekforgeeks a href https www geeksforgeeks org detect and recognize car license plate from a video in real time here a tech stack tech stack used in this projects are br python br computer vision br machine learning br dependencies pre opencv python 3 4 2 br numpy 1 17 2 br skimage 0 16 2 br tensorflow 1 15 0 br imutils 0 5 3 pre | car-license-plate-recognition machine-learning python python3 opencv alpr plate-recognition car hacktoberfest hacktoberfest2022 opencv-python | ai |
vcu | h1 align center vcu vehicle control unit h2 table of contents about about development environment development environment contributing contributing useful resources useful resources related projects related projects about the vcu is responsible for controlling the tractive system based on driver input the microcontroller used in this project is the stm32 f746zg more detailed information about this project is available to members on the sufst docs site setup development environment submodules this project depends on middlewares in the src middlewares folder some of which are git submodules when first cloning this repo run the following commands sh git submodule init git submodule update for more information on submodules see the git submodules documentation https git scm com book en v2 git tools submodules building and flashing to build this project and flash code to the microcontroller you will need the following on your path make https www gnu org software make arm gnu embedded toolchain https developer arm com downloads gnu rm stlink open source toolset https github com stlink org stlink mkdir rm tput and echo build with sh make j s flash with sh make flash for detailed toolchain setup instructions see the sufst docs site note windows users should run these commands from git bash vs code this project is set up to be edited and debugged in vs code https code visualstudio com the vscode folder includes tasks and launch configurations to improve intellisense it is recommended to use ccdgen https github com t bre ccdgen make sure it is installed with the following command which may differ depending on your environment sh python3 m pip install ccdgen then you can generate the compile commands database using the relevant makefile target sh make s ccd since the toolchain is set up to be fully command line based it is also possible to use other code editors note windows users should set git bash as the shell in vs code stm32cubemx stm32cubemx https www st com en development tools stm32cubemx html is used to generate boilerplate initialisation code for the microcontroller these configurations are stored in src vcu ioc which should not be edited manually to minimise the chance of merge conflicts changes to the ioc should be made as infrequently as possible as the ioc format is not well suited to version control note cubemx generates a makefile in the src folder this should not be used there is a custom makefile in the project root if cubemx adds something to the makefile it generates which is not in the custom makefile it should be copied over this should only happen infrequently when adding a new peripheral or cubemx managed middlewares contributing before contributing to this project make sure to familiarise yourself with the project specific contributing guidelines github contributing extra md until the stag 9 codebase is stable all branches should be created from and merged back into develop useful resources microcontroller stm32f746zg mcu datasheet https www st com resource en datasheet stm32f746zg pdf stm32f7xx hal manual https www st com resource en user manual dm00189702 description of stm32f7 hal and lowlayer drivers stmicroelectronics pdf threadx rtos threadx overview https docs microsoft com en us azure rtos threadx overview threadx threadx api https docs microsoft com en us azure rtos threadx chapter4 can inverter pm100 datasheet https www cascadiamotion com images catalog datasheets pm100 pdf cascadia motion can protocol https app box com s vf9259qlaadhzxqiqrt5cco8xpsn84hk file 27334613044 related projects vcu gui https github com sufst vcu gui vcu pcb https github com sufst pcb | stm32 vcu threadx rtos embedded | os |
open-qas | open qas building a question answering system using the wikipedia data and natural language processing file structure openqas openqas holds the main package implementation tests tests holds the test cases used to verify implementation learning phase learning phase holds all the learning phase schedule and submissions data data holds the smaller datasets used for testing notebooks notebooks holds ipython notebooks created during development useful for reference contributors anumeha agrawal https github com anumehaagrawal gurupungav narayanan https github com gurupunskill tanmai h https github com tanmai h kumar saharsh https github com saharsh007 | ai |
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Sixfab_RPi_CellularIoT_App_Shield | sixfab rpi cellulariot library repository of python library for sixfab rpi cellular iot hat https sixfab com product raspberry pi lte m nb iot egprs cellular hat and sixfab rpi cellular iot application shield https sixfab com product raspberry pi cellular iot application hat library installation manual installation git clone https github com sixfab sixfab rpi cellulariot library git cd sixfab rpi cellulariot library sudo python3 setup py install install with pip3 use pip3 to install from pypi sudo pip3 install sixfab cellulariot test enable serial hw and i2c interfaces by following instructions below 1 run sudo raspi config 2 select 5 interfacing options 3 enable p5 i2c 4 for p6 serial disable login shell to be accessible over serial enable serial port hardware 5 finish 6 reboot 7 it s done cd sample python3 sensor test py for testing sensor test example examples basicudp https github com sixfab sixfab rpi cellulariot library blob master sample basicudp py sensortest https github com sixfab sixfab rpi cellulariot library blob master sample sensor test py tutorials basic udp communication tutorial for sixfab rpi cellular iot application shield https sixfab com basic udp communication tutorial for sixfab rpi cellular iot application hat sensor test tutorial for sixfab rpi cellular iot application shield https sixfab com sensor test tutorial for sixfab rpi cellular iot application hat library documentation cellulariot class vars board shield name cellular iot or cellular iot app ip address ip address domain name domain name port number port number timeout timeout default timeout for function and methods on this library response variable for modem self responses compose variable for command self composes primary functions setupgpio needs documentation clear compose function for clearing global compose variable cleargpios function for clearing gpio s setup enable function for enable bg96 module disable function for powering down bg96 module and all peripherals from voltage regulator powerup function for powering up or down bg96 module getmodemstatus function for getting modem power status getresponse function for getting modem response senddatacommonce function for sending data to module sendatcommonce function for sending at comamand to module senddatacomm function for sending data to bg96 at sendatcomm function for sending at command to bg96 at resetmodule function for saving conf and reset bg96 at module saveconfigurations function for save configurations that be done in current session getimei function for getting imei number getfirmwareinfo function for getting firmware info gethardwareinfo function for getting hardware info setgsmband function for setting gsm band setcatm1band function for setting cat m1 band setnbiotband function for setting nb iot band getbandconfiguration function for getting current band settings setscambleconf function for setting scramble feature configuration setmode function for setting running mode getipaddress function for getting self ip address setipaddress function for setting self ip address getdomainname function for getting self domain name setdomainname function for setting domain name getport function for getting port setport function for setting port gettimeout function for getting timout in ms settimeout function for setting timeout in ms network service functions getsignalquality fuction for getting signal quality getquerynetworkinfo function for getting network information connecttooperator function for connecting to base station of operator sms functions sendsms function for sending sms gnss functions turnongnss function for turning on gnss turnoffgnss function for turning of gnss getlatitude function for getting latitude getlongitude function for getting longitude getspeedmph function for getting speed in mph getspeedkph function for getting speed in kph getfixedlocation function for getting fixed location tcp udp protocols functions activatecontext function for configurating and activating tcp context deactivatecontext function for deactivating tcp context connecttoservertcp function for connecting to server via tcp just buffer access mode is supported for now senddatatcp fuction for sending data via tcp just buffer access mode is supported for now senddatasixfabconnect function for sending data to sixfab connect senddataifttt function for sending data to ifttt senddatathingspeak function for sending data to thingspeak startudpservice function for connecting to server via udp senddataudp fuction for sending data via udp closeconnection function for closing server connection shield peripheral functions readuserbutton function for reading user button turnonuserled function for turning on user led turnoffuserled function for turning off user led cellulariotapp class primary functions setupgpio needs documentation enable function for enable bg96 module disable function for powering down bg96 module and all peripherals from voltage regulator powerup function for powering up or down bg96 module getmodemstatus function for getting modem power status readaccel function for reading accelerometer readadc function for reading adc readtemp function for reading temperature readhum function for reading humidity readlux function for reading light resolution turnonrelay function for turning on relay turnoffrelay function for turning off relay readuserbutton function for reading user button turnonuserled function for turning on user led turnoffuserled function for turning off user led pinouts sixfab rpi cellular iot application shield pinout schematic https sixfab com exactdn com wp content uploads 2018 10 rpi cellulariot app shield pinout e1545901898330 png strip all w 1170 ssl 1 sixfab rpi cellular iot hat pinout schematic https sixfab com wp content uploads 2019 04 rpi cell iot hat pinout 2 e1555326552762 jpg attention all data pins work with 3 3v reference any other voltage level should harm your hat or rpi layouts sixfab rpi cellular iot application shield layout https sixfab com wp content uploads 2018 10 rpi cellulariot application shield layout 1 png sixfab rpi cellular iot hat layout https sixfab com wp content uploads 2019 04 rpi cell iot hat layout 2 e1555326485159 jpg | rpi iot sensor lte communication bg96 | server |
machine-learning-articles | machine learning articles i wrote these articles about machine learning in the peroid between may 2019 and february 2022 as i m no longer maintaining machinecurve com i ve moved them here so that they remain available for the public enjoy star history chart https api star history com svg repos christianversloot machine learning articles type date https star history com christianversloot machine learning articles date | machine-learning deep-learning neural-networks tensorflow pytorch scikit-learn convolutional-neural-networks gan gans transformers huggingface-transformers dbscan clustering bert gpt albert keras keras-tensorflow pytorch-implementation pytorch-tutorial | ai |
FE | fe all about front end development html html readme md css css readme md javascript javascript readme md dom dom readme md browser api browser api readme md less sass stylus postcss coffeescript typescript babel font icon pwa webassembly jshint eslint csslint markdown https github com learnshare learning markdown emmet pug vs code sublime text atom bracket ide webstorm git sourcetree github desktop github bitbucket gitlab editorconfig npm yarn bower grunt gulp npm script gnu make livereload watch ci normalize css reset css font awesome material icons bootstrap foundation semantic ui sizzle jquery zepto angular ionic react jsx vue polymer d3 js highcharts three js babylonjs nativescript react native cordova nw js electron react native desktop material design photoshop illustrator adobe xd sketch invision origami studio webpack rollup parcel directive ng1 component angular vue react shadow dom w3c polymer http restful websocket socket io node js cache cdn demo readme md | front_end |
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senlinks | senlinks information technology company | server |
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acorn-kernel | license gpl v3 https img shields io badge license gplv3 blue svg https www gnu org licenses gpl 3 0 acorn kernel screenshots logo jpg http www acorn kernel net the main objective behind acorn kernel is creating an open source small robust and reliable micro kernel rtos for 8bit atmel avr microprocessors written entirely in assembler the kernel by itself is a static preemptive operating system which basically means any number of tasks can be accurately defined before run time and when the system starts up all tasks are created and this number does not change a so called scheduler organizes the tasks execution as each task is given a time quantum to execute its code based on interrupt or software driven events each task could be in one of 2 priorities device level i o related highest one or user level cpu related lowest one this simplifies the kernel and increases the real time event responsiveness provides synchronization primitives which are used for intertask communication resource counting task sleep suspend yield and critical section protection the operating system is logically divided into general and executive levels such division separates the system structures and procedures as to who and what can call them thus making the code development easier the development path being pursued is simplicity and easy to understand and apply methodology in this regard help and collaboration from engineers as well as hobbyists is needed to bring this project to perfection the micro operating system powers a number of embedded projects deployed at customer site and has been working without a glitch | os |
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icy-morning | icy morning project python https img shields io static v1 label python message fastapi color blue logo python terraform https img shields io static v1 label iac message terraform color purple logo terraform aws https img shields io static v1 label cloud message aws color orange logo amazon aws lambda https img shields io static v1 label serverless message lambda color orange logo aws lambda overview repository to showcase a cloud engineering project bringing together a simple fastapi rest api app with database backing store using sqlalchemy and targeting aws infrastructure using terraform the aws infrastraucture is provisioned with terraform for deploying the fastapi lambda function it creates an api using an aws api gateway and lambda function based on this walkthrough https towardsdatascience com fastapi aws robust api part 1 f67ae47390f9 the setup uses rest api fastapi https fastapi tiangolo com and mangum https mangum io infrastructure terraform https www terraform io requests are sent to the api gateway which handles all requests using a proxy integration with the lambda function https docs aws amazon com apigateway latest developerguide set up lambda proxy integrations html mangum acts as a wrapper which allows fastapi to handle the requests and create responses the api gateway can serve all logs are sent to cloudwatch log groups js aws region vpc 1 n azs request api gateway lambda fastapi rds cloudwatch solution notes cloudwatch is not currently implemented if performance becomes an issue a cloudfront distribution could be added for api responses that are cacheable i e the read operations however the cache would need to be refreshed after each write operation deployment start by navigating into the infrastructure directory and performing the following commands bash cd infrastructure make create deployment for tearing down the deployment bash cd infrastructure make destroy deployment note the environment variables aws secret access key and aws access key id must be set in the user environment in order to make changes to aws infrstructure pre requisites terraform if you wish to create a terraform state that is shared between users machines e g in devops scenarios then first create an s3 bucket using this guide https www golinuxcloud com configure s3 bucket as terraform backend once done make main tf can be used to guide the user into configuring the tf backend to use s3 api gateway custom domain place your ca crt certificate crt and certificate key files for your custom domain under infrastructure certs and add an infrastructure overrides tf file containing values for the following variables api gateway domain api gateway certificate name finally do a make api gateway custom domain tf to add the configuration to terraform remove with make clean note this feature is currently untested development development is assumed to be in a linux environment with the following tools available bash pyenv or python3 8 python version contains the exact version used locally make terraform use tfenv and version within terraform version if you are using linux then the toolchain can be installed using make install dev assuming the aforementioned tools are installed and available on the system path then setting up the python virtual environment for development is as simple as make init local server to start a local test server do bash make serve provided the virtual environment has been initialised correctly this will serve a local rest api under http 127 0 0 1 8000 http 127 0 0 1 8000 the openapi swagger documentation is available at http 127 0 0 1 8000 docs http 127 0 0 1 8000 docs and can be used to test endpoints with without authentication testing to run the api tests locally after setting up the dev environment as described above bash make clean test clean is currently required due to async sqlite driver needing to point to a file todo implement sso authentication with jwt send an email notification allow subscription to asset creation events use alembic for handling database migrations add fastapi exception handlers to handle different python errors intuitively ci for terraform module validation and security checks | aws cloud python terraform lambda api-gateway fastapi rds-database sqlalchemy-orm | cloud |
taiwo_recipe_app_project | this is my backend development personal project i am building a recipe app | server |
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bright-image-filter | udagram image filtering microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend covered in the course 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service covered in the course 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images your assignment tasks setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm i 2 run the development server with npm run dev create a new endpoint in the server ts file the starter code has a task for you to complete an endpoint in src server ts which uses query parameter to download an image from a public url filter the image and return the result we ve included a few helper functions to handle some of these concepts and we re importing it for you at the top of the src server ts file typescript import filterimagefromurl deletelocalfiles from util util deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service don t forget you can use eb deploy to push changes stand out optional refactor the course restapi if you re feeling up to it refactor the course restapi to make a request to your newly provisioned image server authentication prevent requests without valid authentication headers note if you choose to submit this make sure to add the token to the postman collection and export the postman collection file to your submission so we can review custom domain name add your own domain name and have it point to the running services try adding a subdomain name to point to the processing server note domain names are not included in aws free tier and will incur a cost | cloud |
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Coursera-webdev | coursera web development course repository html css javascript for web developers by johns hopkins university assignments solutions to coding assignments during the course assignment 2 assignment 2 solution https vidtho github io coursera webdev mod2 solution assignment 3 assignment 3 solution https vidtho github io coursera webdev mod3 solution assignment 4 assignment 4 solution https vidtho github io coursera webdev mod4 solution assignment 5 assignment 5 solution https vidtho github io coursera webdev mod5 solution david chu s china bistro mock restaurant website david chu s china bistro 1 https vidtho github io coursera webdev restaurantwebsite html5 css3 javascript ajax david chu s china bistro 2 https vidtho github io coursera webdev restaurantwebsite2 html5 css3 javascript jquery ajax variations added david chu s china bistro 1 copyright shows the current year assignment 5 incorporated specials will display random menu category dynamically load awards and about pages using ajax menu categories and single menu item populated from local json files change look and feel of website using css properties change menu icons and menu font weight david chu s china bistro 2 same as 1 dynamic pages load using jquery | johns-hopkins-university css javascript html coursera ajax-request bootstrap jhu website json | front_end |
qreal-web | https github com qreal wmp | front_end |
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Blockchain-based-E-Voting-Simulation | blockchain based e voting simulation description this is a python based django project to simulate a concept of blockchain based e voting protocol this project should be run only on the development server with debug mode on the simulation comprises two sections block and chain block scenario a potential voter has to present himself to the voting organizer showing his id and other legal documents finally he has to submit a public key i e ecc assume that he has successfully registered the public key via the ballot page see screenshot below the voter needs to enter his pseudonym uuid 4 and candidate number the ballot is then signed using a private key if the submitted ballot is proven to be valid it will be sealed mined in this section one block contains only the aforementioned ballot you can examine the whole process in detail using the console the public private key pair is hard coded and generated externally see technical details below ballot page https raw githubusercontent com gottfriedcp blockchain based e voting simulation master screenshots ballot png chain in this section transactions ballots with valid data will be generated they are then sealed into blocks after that you can explore the transactions and blocks try to tamper the records and verify them sealed ballot https raw githubusercontent com gottfriedcp blockchain based e voting simulation master screenshots transactions png block https raw githubusercontent com gottfriedcp blockchain based e voting simulation master screenshots block png the screenshot above shows that a node s database i e your node has been tampered you will not see this in real blockchain explorer but this should give you a glimpse of why tampering immutable ledger is futile how to run 1 get your virtual environment ready recommended 2 locate the requirements txt file install necessary packages pip install r requirements txt 3 locate the manage py file run python manage py runserver then access http localhost 8000 4 from the homepage either run block or chain section by clicking start technical details the private key used in this demo is located in bbevoting project folder demo private pem file while the corresponding public key is hard coded in settings py look for public key you may also set some config vars such as n transactions n tx per block and the puzzle difficulty puzzle and plength screenshots see the screenshots folder acknowledgement for prof abm this project uses a modified version of pymerkletools by tierion for creating merkle root using sha3 license see included mit license | python3 django blockchain e-voting | blockchain |
cv-models | computer vision models | ai |
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sloth | sloth build status https travis ci org cvhcikit sloth svg https travis ci org cvhcikit sloth sloth is a tool for labeling image and video data for computer vision research the documentation can be found at http sloth readthedocs org latest releases 2013 11 29 v1 0 2e69fdae40f89050fbaeef22491eee2a92e78b4f zip https github com cvhcikit sloth archive v1 0 zip tar gz https github com cvhcikit sloth archive v1 0 tar gz for a full list visit https github com cvhcikit sloth releases | ai |
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flex | flex flex is a flexible box layout system following the a href https www w3 org tr css flexbox 1 css flexbox module a specifications the goal is to offer a fully compliant implementation with a small and maintainable code base under a permissive license flex exposes a plain c api with the same parameters that you would use in css to customize the layout of a flexible view hierarchy the api is designed to be easily interoperable with foreign runtimes ex c and meant to be used by widget toolkits as the foundation of a view layout api flex supports both single and multiple wrap lines layouts getting started if you program in c you can go straight to the bindings csharp bindings csharp directory if you program in a c compatible environment you can simply copy the flex c and flex h files to your project the code was written to be cross platform and does not require dependencies on a mac you can also generate static and dynamic libraries for ios android and macos using make make macos make ios make android make builds everything make sure to have the android ndk environment variable set to the path where the android ndk is located in your system you can also tweak build variables by editing the makefile file on a windows machine you can generate dynamic libraires dll for x86 x64 arm and arm64 by opening the visual studio project file or running msbuild from the command line demo app under the demo directory you will find an xcode project that will build a mac demo app the app exposes the entire set of flexbox parameters that are implemented and lets you create views including nested ones similar to how you would build a more realistic user interface in practice implementation status attribute status width height ok self sizing ok padding ok margin ok justify content flex start ok justify content flex end ok justify content center ok justify content space around ok justify content space between ok justify content space evenly ok align content flex start ok align content flex end ok align content center ok align content space around ok align content space between ok align content space evenly ok align content stretch ok align items flex start ok align items flex end ok align items center ok align items stretch ok align self flex start ok align self flex end ok align self center ok align self stretch ok position relative ok position absolute ok direction column ok direction column reverse ok direction row ok direction row reverse ok wrap no wrap ok wrap wrap ok wrap wrap reverse ok grow ok shrink ok order ok basis ok tests there is a test suite in the tests directory see the tests readme md tests readme md file for more details on how to build run and contribute to the test suite license flex is distributed under the terms of the mit license see the license txt file for more information | os |
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natural-language-processing | natural language processing end to end implementation examples nlp img nlp jpg in recent years natural language processing nlp has seen quick growth in quality and usability and this has helped to drive business adoption of artificial intelligence ai solutions in the last few years researchers have been applying newer deep learning methods to nlp data scientists started moving from traditional methods to state of the art sota deep neural network dnn algorithms which use language models pretrained on large text corpora this repository contains the full implementation example of several natural language processing methods in python which can be used in any dataset of indutry to readily usage i tried to output it all as jupyter notebook so that it s easy to read and follow through the goal of this repo is to provide a comprehensive set of tools and examples that leverage recent advances in nlp algorithms when properly consumed in order of the notebooks it will guide you through the basics of nlp concepts and skills through several different libraries keras tenforfow and eventually will help build production level systems like chatbot recommendation system based on the language data hr aa img natural language processing introduction jpg table of contents requirements requirements usage usage data data implementation implementation to do extralearning refernece refernece requirements python 3 tensorflow 2 x numpy pandas sklearn transformers huggingface usage install required packages follow along each jupyter notebooks data each dataset needed for each notebook can be downloaded through wget also full dataset can be found in data folder in this repo hr implementation 01 news category classification https github com hyunjoonbok natural language processing blob master 01 news category classification ipynb we try to classify the category of the news using the pre trained embedding model use keras to build model from scrath and start training 02 sentiment analysis in keras https github com hyunjoonbok natural language processing blob master 02 sentiment analysis in keras ipynb we perform a sentiment analysis using google bert model on the movie data with tf keras api we use keras api this time to do the analysis as pytorch version examples are already a lot also we will use korean movie review dataset as analysis done in english movie review imdb are easy to be find online you should be able to have a firm grasp of how google s language model bert works and fine tune it to apply to any of custom business problems 03 sentiment analysis in tensorflow https github com hyunjoonbok natural language processing blob master 03 sentiment analysis in tensorflow ipynb same sentiment analysis using google bert model on the movie data with tensorflow 2 0 here we utilize transformers berttokenizer and bertmodel to easily load functions necessary in bert and use it in training 04 squad in keras https github com hyunjoonbok natural language processing blob master 04 squad in keras ipynb build squad model using keras and bert stanford question answering dataset squad is a reading comprehension dataset consisting of questions posed by crowdworkers on a set of wikipedia articles where the answer to every question is a segment of text or span from the corresponding reading passage or the question might be unanswerable 05 squad in tensorflow https github com hyunjoonbok natural language processing blob master 05 squad in tensorflow ipynb this time we are going to solve the same squad problem with tensorflow by fine tuning pretrained bert large model 06 named entity recognition in tensorflow https github com hyunjoonbok natural language processing blob master 06 named entity recognition ner in tensorflow ipynb solve the another application of natural language processing names entity recognition ner named entity recognition ner also called entity identification or entity extraction is an information extraction technique that automatically identifies named entities in a text and classifies them into predefined categories 07 end to end speech recognition in tensorflow https github com hyunjoonbok lets learn new skills blob master end to end 20speech 20recognition 20in 20tensorflow ipynb build a simpel end to end speech to text model using librosa library the model takes recordings of 10 different classes or words data from kaggle speech recognition challenge trains algorithm that is in convolutional 1d and predicts the sound in text 08 generate fluent english text using gpt2 https github com hyunjoonbok lets learn new skills blob master generate 20fluent 20english 20text 20using 20transformers 20gpt2 ipynb a simple example to look at different decoder method provided by transformer library we use gpt2 specifically to to see which decoder gerenates the most human like languages when given texts hr extralearning 1 novel generator using kogpt2 and pytorch link https github com shbictai narrativekogpt2 fbclid iwar1srxetzavypb5ez3txt4m1dxzs3sq24kywkaxr3qdy 6gkr2xl18kd3g4 2 text generation lyric generation squad fine tuning link https github com mrbananahuman korgpt2tutorial 3 make a simple chat bot in korean language using korean language data and pre trained kogpt2 model link https github com haven jeon kogpt2 chatbot refernece https github com kimwoonggon publicservant ai https github com microsoft nlp recipes https github com nirantk nlp python deep learning https github com monologg kobert ner | deeplearning natural-language-processing language-model keras tensorflow2 transformer transfer-learning sentiment-analysis squad category namedentityrecognizer speech-recognition | ai |
RTduino | rt thread arduino star pr fork english readme en md rtduino logo rtduino rt thread png 1 rtduino rt thread https www rt thread org arduino rt thread https github com rt thread rt thread arduino arduino rt thread arduino arduino rt thread rt thread rtduino arduino rt thread bsp arduino https www arduino cc reference en libraries rt thread rt thread rt thread studio ide https www rt thread org page studio html keil mdk arduino gcc rt thread studio ide https www rt thread org page studio html 1 1 arduino rt thread bsp bsp bsp es32f3696 https github com rt thread rt thread tree master bsp essemi es32f369x applications arduino pinout stm32f427 robomaster a https github com rt thread rt thread tree master bsp stm32 stm32f427 robomaster a applications arduino pinout stm32f103 bluepill https github com rt thread rt thread tree master bsp stm32 stm32f103 blue pill applications arduino pinout stm32f407 robomaster c https github com rt thread rt thread tree master bsp stm32 stm32f407 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applications arduino pinout stm32f410 nucleo https github com rt thread rt thread tree master bsp stm32 stm32f410 st nucleo applications arduino pinout nxp lpc55s69 evk https github com rt thread rt thread tree master bsp lpc55sxx lpc55s69 nxp evk applications arduino pinout stm32f411 nucleo https github com rt thread rt thread tree master bsp stm32 stm32f411 st nucleo applications arduino pinout stm32l476 nucleo https github com rt thread rt thread tree master bsp stm32 stm32l476 st nucleo applications arduino pinout stm32f412 nucleo https github com rt thread rt thread tree master bsp stm32 stm32f412 st nucleo applications arduino pinout stm32g474 nucleo https github com rt thread rt thread tree master bsp stm32 stm32g474 st nucleo applications arduino pinout stm32f469 discovery https github com rt thread rt thread tree master bsp stm32 stm32f469 st disco applications arduino pinout stm32u575 nucleo https github com rt thread rt thread tree master bsp stm32 stm32u575 st nucleo applications arduino pinout rtduino bsp rt thread bsp 5 rtduino 1 3 bsp bsp stm32f401 nucleo https github com rt thread rt thread tree master bsp stm32 stm32f401 st nucleo applications arduino pinout stm32l476 nucleo https github com rt thread rt thread tree master bsp stm32 stm32l476 st nucleo applications arduino pinout stm32f411 nucleo https github com rt thread rt thread tree master bsp stm32 stm32f411 st nucleo applications arduino pinout 1 4 https packages rt thread org software package rt thread rt thread https www arduino cc reference en libraries library arduino arduino 2 rtduino rtduino rt thread 4 1 1 2 1 rtduino docs zh 2022 rt thread https www bilibili com video bv1wa411l7b4 2 2 rtduino rtduino rtduino 2 2 1 rtduino rtduino rt thread rtduino rt thread bsp rt thread rtduino 1 vscode env docs zh 6 vscode env md 2 rt thread studio ide rtduino docs zh 5 rt thread 20studio 20ide md 51 rtduino 2 2 2 rtduino rtduino rtduino rt thread bsp rt thread studio bsp rt thread studio rt thread studio ide rtduino docs zh 5 rt thread 20studio 20ide md 52 rtduino 2 3 arduino setup loop arduino setup loop bsp applications arduino main cpp bsp stm32 stm32l475 atk pandora applications arduino main cpp rtduino applications 2 3 1 docs figures 2 3 1 png 2 4 led c include arduino h void setup void put your setup code here to run once pinmode led builtin output void loop void put your main code here to run repeatedly digitalwrite led builtin digitalread led builtin serial println hello arduino delay 100 led rt thread main c led led led main c 1000ms 200ms stm32l475 bsp main c rtduino arduino 2 5 rtduino rtduino core libraries 2 5 1 docs figures 2 5 1 png core arduino api analogwrite analogread arduino https www arduino cc reference en libraries arduino buildin arduino servo wire i2c user arduino 2 6 arduino bsp bsp applications arduino readme md stm32f401 nucleo arduino https github com rt thread rt thread tree master bsp stm32 stm32f401 st nucleo applications arduino pinout stm32f411 nucleo arduino https github com rt thread rt thread tree master bsp stm32 stm32f411 st nucleo applications arduino pinout stm32l475 arduino https github com rt thread rt thread tree master bsp stm32 stm32l475 atk pandora applications arduino pinout 3 arduino arduino issue 3 1 arduino rtduino arduino https github com arduino arduinocore avr tree master libraries libraries buildin servo rtduino using servo pwm bsp spi spi rtduino using spi spi bsp wire i2c rtduino using wire i2c bsp usbserial usb rtduino using usbserial tinyusb for rt thread https github com rt thread packages tinyusb 3 2 rt thread arduino rt thread rt thread arduino rt thread docs zh 7 rt thread arduino rt thread md 3 3 arduino rt thread arduino rt thread arduino arduino rt thread rtduino arduino 5000 rt thread arduino rtduino arduino rt thread arduino github c zip rtduino libraries user 3 3 docs figures 3 3 png rt thread studio ide sync sconscript to project rt studio rt studio zip arduino ide examples c h arduino rt thread env vscode scons 3 4 docs figures 3 4 png rtduino libraries user arduino 4 rtduino bsp 4 1 setup loop setup loop arduino arduino ide rtduino 4 setup loop env rt thread studio rt thread settings setup loop kconfig rt thread online packages system packages rtduino arduino ecological compatibility layer don t use setup loop structure core arduino thread c arduino entry cpp arduino api arduino cpp 4 2 rtduino rtduino rt thread bsp arduino pinout arduino io api analogread rtduino i2c io env rt thread studio rt thread settings enable tiny mode arduino io 5 1 setup loop cpp arduino api kconfig rt thread online packages system packages rtduino arduino ecological compatibility layer don t use setup loop structure enable tiny mode 4 3 4 vs 5 arduino api docs zh 1 arduino 20api e5 8f af e5 85 bc e5 ae b9 e6 80 a7 e4 b8 80 e8 a7 88 e8 a1 a8 md rtduino arduino api 5 ino sketch 5 1 ino arduino sketch ino arduino ide sketch arduino cli https arduino github io arduino cli 0 33 sketch build process rtduino ino scons j20 sketch ino j20 20 cpu sketch ino rtduino sketch 5 2 rtduino sketch loader rtduino rt thread arduino rtduino sketch loader sketch rtduino rtduino sketch loader rtduino sketch loader prio rtduino sketch loader stacksize rtduino sketch loader stacksize prio rtduino sketch loader loader sketch sketch cpp cpp include rtduino h rtduino h arduino h static void my setup void static setup serial println hello my sketch static void my loop void static loop my sketch my setup my loop stacksize 2048 prio rtduino sketch loader my sketch my setup my loop rtduino sketch loader prio my sketch my setup my loop 10 10 rtduino sketch loader stacksize my sketch my setup my loop 1024 1024 rtduino sketch loader stacksize prio my sketch my setup my loop 1024 10 1024 10 msgq c cpp demo https github com rtduino msgq c cpp demo blob master arduino producer cpp 6 6 1 pwm pinmode pwm adc dac c void setup pinmode led pin output output pwm void loop fading the led for int i 0 i 255 i analogwrite led pin i delay 5 for int i 255 i 0 i analogwrite led pin i delay 5 pwm adc dac io pinmode io pwm adc dac analogread analogwrite pinmode pinmode analogwrite analogread io arduino https www arduino cc reference en language functions analog io analogwrite markdown you do not need to call pinmode to set the pin as an output before calling analogwrite the analogwrite function has nothing to do with the analog pins or the analogread function pwm adc dac pinmode pwmwarning docs figures pwmwarning png pwm adc dac pinmode io 6 2 serial begin arduino c serial begin 9600 9600 rt thread rt thread 115200 serial begin 9600 115200 9600 115200 serial begin serial begin 9600 serial begin rtduino rt thread 6 3 spi begin wire begin spi wire i2c rt thread spi i2c arduino pin h spi wire spi i2c arduino spi i2c rt thread spi begin spi1 wire begin i2c1 6 4 pwm spi arduino uno r3 d10 d13 spi d10 d11 pwm rtduino arduino uno r3 bsp d10 d11 pwm spi begin pwm spi d10 d11 pwm rtduino bsp pins arduino c switchtospi pr https github com rt thread rt thread pull 7901 7 7 1 https github com rtduino rtduino https gitee com rtduino rtduino 7 2 rt thread bsp rtduino rt thread bsp rtduino docs zh 8 rt thread 20bsp rtduino md 7 3 arduino rtduino arduino rtduino docs zh 9 arduino rtduino md 7 4 a href https github com rtduino rtduino graphs contributors img src https contrib rocks image repo rtduino rtduino a | arduino rt-thread rtos compatibility-layer rtduino | os |
ruby-spacy | ruby spacy overview ruby spacy is a wrapper module for using spacy https spacy io from the ruby programming language via pycall https github com mrkn pycall rb this module aims to make it easy and natural for ruby programmers to use spacy this module covers the areas of spacy functionality for using many varieties of its language models not for building ones functionality tokenization lemmatization sentence segmentation part of speech tagging and dependency parsing named entity recognition syntactic dependency visualization access to pre trained word vectors openai chat completion embeddings api integration current version 0 2 2 spacy 3 7 0 supported openai api integration installation of prerequisites important make sure that the enable shared option is enabled in your python installation you can use pyenv https github com pyenv pyenv to install any version of python you like install python 3 10 6 for instance using pyenv with enable shared as follows shell env configure opts enable shared pyenv install 3 10 6 remember to make it accessible from your working directory it is recommended that you set global to the version of python you just installed shell pyenv global 3 10 6 then install spacy https spacy io if you use pip the following command will do shell pip install spacy install trained language models for a starter en core web sm will be the most useful to conduct basic text processing in english however if you want to use advanced features of spacy such as named entity recognition or document similarity calculation you should also install a larger model like en core web lg shell python m spacy download en core web sm python m spacy download en core web lg see spacy models languages https spacy io usage models for other models in various languages to install models for the japanese language for instance you can do it as follows shell python m spacy download ja core news sm python m spacy download ja core news lg installation of ruby spacy add this line to your application s gemfile ruby gem ruby spacy and then execute bundle install or install it yourself as gem install ruby spacy usage see examples examples below examples many of the following examples are python to ruby translations of code snippets in spacy 101 https spacy io usage spacy 101 for more examples look inside the examples directory tokenization spacy tokenization https spacy io usage spacy 101 annotations token ruby code ruby require ruby spacy require terminal table nlp spacy language new en core web sm doc nlp read apple is looking at buying u k startup for 1 billion row doc each do token row token text end headings 1 2 3 4 5 6 7 8 9 10 table terminal table new rows row headings headings puts table output 1 2 3 4 5 6 7 8 9 10 11 apple is looking at buying u k startup for 1 billion part of speech and dependency spacy part of speech tags and dependencies https spacy io usage spacy 101 annotations pos deps ruby code ruby require ruby spacy require terminal table nlp spacy language new en core web sm doc nlp read apple is looking at buying u k startup for 1 billion headings text lemma pos tag dep rows doc each do token rows token text token lemma token pos token tag token dep end table terminal table new rows rows headings headings puts table output text lemma pos tag dep apple apple propn nnp nsubj is be aux vbz aux looking look verb vbg root at at adp in prep buying buy verb vbg pcomp u k u k propn nnp dobj startup startup noun nn advcl for for adp in prep sym quantmod 1 1 num cd compound billion billion num cd pobj part of speech and dependency japanese ruby code ruby require ruby spacy require terminal table nlp spacy language new ja core news lg doc nlp read 1983 14 800 headings text lemma pos tag dep rows doc each do token rows token text token lemma token pos token tag token dep end table terminal table new rows rows headings headings puts table output text lemma pos tag dep propn nsubj adp case 1983 1983 num nummod noun obl adp case noun obj adp case 14 800 14 800 num fixed noun obl adp case verb root aux aux aux aux punct punct morphology pos and morphology tags https github com explosion spacy blob master spacy glossary py ruby code ruby require ruby spacy require terminal table nlp spacy language new en core web sm doc nlp read apple is looking at buying u k startup for 1 billion headings text shape is alpha is stop morphology rows doc each do token morph token morphology map do k v k v end join n rows token text token shape token is alpha token is stop morph end table terminal table new rows rows headings headings puts table output text shape is alpha is stop morphology apple xxxxx true false nountype prop br number sing is xx true true mood ind br number sing br person 3 br tense pres br verbform fin looking xxxx true false aspect prog br tense pres br verbform part at xx true true buying xxxx true false aspect prog br tense pres br verbform part u k x x false false nountype prop br number sing startup xxxx true false number sing for xxx true true false false 1 d false false numtype card billion xxxx true false numtype card visualizing dependency spacy visualizers https spacy io usage visualizers ruby code ruby require ruby spacy nlp spacy language new en core web sm sentence autonomous cars shift insurance liability toward manufacturers doc nlp read sentence dep svg doc displacy style dep compact false file open file join test dep svg w do file file write dep svg end output https github com yohasebe ruby spacy blob main examples get started outputs test dep svg visualizing dependency compact ruby code ruby require ruby spacy nlp spacy language new en core web sm sentence autonomous cars shift insurance liability toward manufacturers doc nlp read sentence dep svg doc displacy style dep compact true file open file join test dep compact svg w do file file write dep svg end output https github com yohasebe ruby spacy blob main examples get started outputs test dep compact svg named entity recognition spacy named entities https spacy io usage spacy 101 annotations ner ruby code ruby require ruby spacy require terminal table nlp spacy language new en core web sm doc nlp read apple is looking at buying u k startup for 1 billion rows doc ents each do ent rows ent text ent start char ent end char ent label end headings text start char end char label table terminal table new rows rows headings headings puts table output text start char end char label apple 0 5 org u k 27 31 gpe 1 billion 44 54 money named entity recognition japanese ruby code ruby require ruby spacy require terminal table nlp spacy language new ja core news lg sentence 1983 14 800 doc nlp read sentence rows doc ents each do ent rows ent text ent start char ent end char ent label end headings text start end label table terminal table new rows rows headings headings print table output text start end label 0 3 org 1983 4 9 date 10 15 product 14 800 16 23 money checking availability of word vectors spacy word vectors and similarity https spacy io usage spacy 101 vectors similarity ruby code ruby require ruby spacy require terminal table nlp spacy language new en core web lg doc nlp read dog cat banana afskfsd rows doc each do token rows token text token has vector token vector norm token is oov end headings text has vector vector norm is oov table terminal table new rows rows headings headings puts table output text has vector vector norm is oov dog true 7 0336733 false cat true 6 6808186 false banana true 6 700014 false afskfsd false 0 0 true similarity calculation ruby code ruby require ruby spacy nlp spacy language new en core web lg doc1 nlp read i like salty fries and hamburgers doc2 nlp read fast food tastes very good puts doc 1 doc1 text puts doc 2 doc2 text puts similarity doc1 similarity doc2 output text doc 1 i like salty fries and hamburgers doc 2 fast food tastes very good similarity 0 7687607012190486 similarity calculation japanese ruby code ruby require ruby spacy nlp spacy language new ja core news lg ja doc1 nlp read puts doc1 ja doc1 text ja doc2 nlp read puts doc2 ja doc2 text puts similarity ja doc1 similarity ja doc2 output text doc1 doc2 similarity 0 8684192637149641 word vector calculation tokyo japan france paris ruby code ruby require ruby spacy require terminal table nlp spacy language new en core web lg tokyo nlp get lexeme tokyo japan nlp get lexeme japan france nlp get lexeme france query tokyo vector japan vector france vector headings rank text score rows results nlp most similar query 10 results each with index do lexeme i index i 1 to s rows index lexeme text lexeme score end table terminal table new rows rows headings headings puts table output rank text score 1 france 0 8346999883651733 2 france 0 8346999883651733 3 france 0 8346999883651733 4 paris 0 7703999876976013 5 paris 0 7703999876976013 6 paris 0 7703999876976013 7 toulouse 0 6381999850273132 8 toulouse 0 6381999850273132 9 toulouse 0 6381999850273132 10 marseille 0 6370999813079834 word vector calculation japanese ruby code ruby require ruby spacy require terminal table nlp spacy language new ja core news lg tokyo nlp get lexeme japan nlp get lexeme france nlp get lexeme query tokyo vector japan vector france vector headings rank text score rows results nlp most similar query 10 results each with index do lexeme i index i 1 to s rows index lexeme text lexeme score end table terminal table new rows rows headings headings puts table output rank text score 1 0 7376999855041504 2 0 7221999764442444 3 0 6697999835014343 4 0 631600022315979 5 0 5939000248908997 6 paris 0 574400007724762 7 0 5683000087738037 8 0 5679000020027161 9 0 5644999742507935 10 0 5547999739646912 openai api integration this feature is currently experimental details are subject to change please refer to openai s api reference https platform openai com docs api reference and ruby openai https github com alexrudall ruby openai for available parameters max tokens temperature etc easily leverage gpt models within ruby spacy by using an openai api key when constructing prompts for the doc openai query method you can incorporate the following token properties of the document these properties are retrieved through function calls made internally by gpt when necessary and seamlessly integrated into your prompt note that function calls need gpt 3 5 turbo 0613 or higher the available properties include surface lemma tag pos part of speech dep dependency ent type entity type morphology gpt prompting translation ruby code ruby require ruby spacy api key env openai api key nlp spacy language new en core web sm doc nlp read the beatles released 12 studio albums default parameter values max tokens 1000 temperature 0 7 model gpt 3 5 turbo 0613 res1 doc openai query access token api key prompt translate the text to japanese puts res1 output 12 gpt prompting elaboration ruby code ruby require ruby spacy api key env openai api key nlp spacy language new en core web sm doc nlp read the beatles were an english rock band formed in liverpool in 1960 default parameter values max tokens 1000 temperature 0 7 model gpt 3 5 turbo 0613 res doc openai query access token api key prompt extract the topic of the document and list 10 entities names concepts locations etc that are relevant to the topic output topic the beatles entities 1 the beatles band 2 english nationality 3 rock band 4 liverpool city 5 1960 year 6 john lennon member 7 paul mccartney member 8 george harrison member 9 ringo starr member 10 music gpt prompting json output using rag with token properties ruby code ruby require ruby spacy api key env openai api key nlp spacy language new en core web sm doc nlp read the beatles released 12 studio albums default parameter values max tokens 1000 temperature 0 7 model gpt 3 5 turbo 0613 res doc openai query access token api key prompt list token data of each of the words used in the sentence add meaning property and value brief semantic definition to each token data output as a json object output json tokens surface the lemma the pos det tag dt dep det ent type morphology definite def prontype art meaning used to refer to one or more people or things already mentioned or assumed to be common knowledge surface beatles lemma beatle pos noun tag nns dep nsubj ent type gpe morphology number plur meaning a british rock band formed in liverpool in 1960 surface released lemma release pos verb tag vbd dep root ent type morphology tense past verbform fin meaning to make something available or known to the public surface 12 lemma 12 pos num tag cd dep nummod ent type cardinal morphology numtype card meaning a number representing a quantity surface studio lemma studio pos noun tag nn dep compound ent type morphology number sing meaning a place where creative work is done surface albums lemma album pos noun tag nns dep dobj ent type morphology number plur meaning a collection of musical or spoken recordings gpt prompting generate a syntaxt tree using token properties ruby code ruby require ruby spacy api key env openai api key nlp spacy language new en core web sm doc nlp read the beatles released 12 studio albums default parameter values max tokens 1000 temperature 0 7 res doc openai query access token api key model gpt 4 prompt generate a tree diagram from the text using given token data use the following bracketing style s np det the n cat vp v sat pp p on np the mat puts res output s np det the n beatles vp v released np num 12 n n studio n albums gpt text completion ruby code ruby require ruby spacy api key env openai api key nlp spacy language new en core web sm doc nlp read vladimir nabokov was a default parameter values max tokens 1000 temperature 0 7 model gpt 3 5 turbo 0613 res doc openai completion access token api key puts res output russian american novelist and lepidopterist he was born in 1899 in st petersburg russia and later emigrated to the united states in 1940 nabokov is best known for his novel lolita which was published in 1955 and caused much controversy due to its controversial subject matter throughout his career nabokov wrote many other notable works including pale fire and ada or ardor a family chronicle in addition to his writing nabokov was also a passionate butterfly collector and taxonomist publishing several scientific papers on the subject he passed away in 1977 leaving behind a rich literary legacy text embeddings ruby code ruby require ruby spacy api key env openai api key nlp spacy language new en core web sm doc nlp read vladimir nabokov was a russian american novelist poet translator and entomologist default model text embedding ada 002 res doc openai embeddings access token api key puts res output 0 00208362 0 01645165 0 0110955965 0 012802119 0 0012175755 author yoichiro hasebe yohasebe gmail com acknowlegments i would like to thank the following open source projects and their creators for making this project possible explosion spacy https github com explosion spacy mrkn pycall rb https github com mrkn pycall rb license this library is available as open source under the terms of the mit license https opensource org licenses mit | nlp parsing ruby spacy natural-language word-embeddings gpt openai | ai |
node-blockchain | blockchain in node js a simple blockchain and cryptocurrency wallet implemented in node js and typescript for learning purposes watch the blockchain in 100 seconds https youtu be qf7dkrce mq video usage git clone this repo npm install npm start | blockchain |
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3rd-semester-iac-project | 3rd semester iac project for altschool cloud engineering 3rd semeter exam ptoject | cloud |
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Udagram | udagram cloud application udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend frontend readme md a basic ionic client web application which consumes the restapi backend 2 the restapi backend main server readme md a node express server which can be deployed to a cloud service 3 the image filtering microservice image filter service readme md it is a node express application which runs a simple script to process images | cloud |
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Hands-On-ML | hands on ml 1 hands on machine learning cover png 2 part 1 chap01 https github com excelsiorcjh hands on ml blob master chap01 the machine learning landscape chap01 ml intro pdf chap02 https github com excelsiorcjh hands on ml blob master chap02 end to end ml project chap02 end to end ml project ipynb chap03 https github com excelsiorcjh hands on ml blob master chap03 classification chap03 classification ipynb chap04 https github com excelsiorcjh hands on ml blob master chap04 training models chap04 training models ipynb chap05 support vector machine https github com excelsiorcjh hands on ml blob master chap05 svm chap05 svm ipynb chap06 decision tree https github com excelsiorcjh hands on ml blob master chap06 decision tree chap06 decision tree ipynb chap07 https github com excelsiorcjh hands on ml blob master chap07 ensemble learning and random forests chap07 ensemble learning and random forests ipynb chap08 https github com excelsiorcjh hands on ml blob master chap08 dimensionality reduction chap08 dimensionality reduction ipynb part 2 chap09 https github com excelsiorcjh hands on ml tree master chap09 up and running with tensorflow chap10 https github com excelsiorcjh hands on ml blob master chap10 introduction to ann chap10 introduction to ann ipynb chap11 https github com excelsiorcjh hands on ml blob master chap11 training dnn chap11 1 training dnn ipynb https github com excelsiorcjh hands on ml blob master chap11 training dnn chap11 2 training dnn ipynb https github com excelsiorcjh hands on ml blob master chap11 training dnn chap11 3 training dnn ipynb chap12 chap13 https github com excelsiorcjh hands on ml blob master chap13 convolutional neural networks chap13 convolutional neural networks ipynb chap14 rnn 1 rnn https github com excelsiorcjh hands on ml blob master chap14 recurrent neural networks chap14 1 recurrent neural networks ipynb 2 rnn deep rnn https github com excelsiorcjh hands on ml blob master chap14 recurrent neural networks chap14 2 recurrent neural networks ipynb 3 lstm gru https github com excelsiorcjh hands on ml blob master chap14 recurrent neural networks chap14 3 recurrent neural networks ipynb chap15 autoencoder https github com excelsiorcjh hands on ml blob master chap15 autoencoders chap15 autoencoders ipynb chap16 rl reinforcement learning https github com excelsiorcjh hands on ml blob master chap16 reinforcement learning chap16 reinforcement learning ipynb 3 github https github com rickiepark handson ml scikit learn http scikit learn org tensorflow https www tensorflow org | machine-learning deep-learning tensorflow scikit-learn python3 hands-on-machine-learning | ai |
iot-dc | https img shields io github license theembers iot dc https img shields io badge e4 b8 ad e5 9b bd e5 8a a0 e6 b2 b9 20 e6 ad a6 e6 b1 89 e5 8a a0 e6 b2 b9 e4 b8 ba e6 b0 91 e8 80 8c e7 94 9f e4 b8 8e e6 b0 91 e5 85 b1 e7 94 9f 20 e4 bc 97 e5 bf 97 e6 88 90 e5 9f 8e e5 85 b1 e5 85 8b e6 97 b6 e8 89 b0 20 red iot dc framework a iot data collector framework and power by springboot netty rabbitmq kafka springboot netty rabbitmq iot src https github com theembers iot dc thanks for star the obsolete version branch s 0 1 will be not update yet except bug fix the master will be rebuild as new one thanks follow s 0 1 bug master branch s 0 1 https github com theembers iot dc tree s 0 1 new framework had be done you can running with iot example me theembers iot testcollector to debug it iot example me theembers iot testcollector iot platform framework iot https image 1257148187 cos ap chengdu myqcloud com picgo img 20190926173357 jpg about the new iot dc framework iot dc framework netty iot framework dc iot framework dc https image 1257148187 cos ap chengdu myqcloud com picgo img 20191111134357 jpg router device shadow router rule processor router https image 1257148187 cos ap chengdu myqcloud com picgo img 20191111131757 jpg processor link router selector route dispatcher processor processor processor link https image 1257148187 cos ap chengdu myqcloud com picgo img processor link 1 jpg java processor headin beforetransform transform receive tailout aftertransform 1 buildslotdata param shadow param sourcedata void run shadow shadow sourcedata sourcedata output output null slotdata slotdata null iterator processor processors this link iterator while processors hasnext processor p processors next if p getfirst output p headin shadow sourcedata else output p receive shadow slotdata if p getlast output p tailout shadow output return slotdata p passon shadow output 1 iot dc netty server netty 2019 11 07 | server |
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parte | parte design system parte ds ui https www npmjs com package parte ds ui parte parte ds icons https www npmjs com package parte ds icons parte parte ds utils parte bash pnpm install pnpm dev parte ds ui pretendard https github com orioncactus pretendard html link css import html link rel stylesheet as style crossorigin href https cdn jsdelivr net gh orioncactus pretendard v1 3 6 dist web variable pretendardvariable dynamic subset css useful commands pnpm build build all packages including the storybook site pnpm dev run all packages locally and preview with storybook pnpm lint lint all packages pnpm changeset generate a changeset pnpm clean clean up all node modules and dist folders runs each package s clean script this repository is powered by turborepo https turbo build repo high performance build system for monorepos react https reactjs org javascript library for user interfaces tsup https github com egoist tsup typescript bundler powered by esbuild storybook https storybook js org ui component environment powered by vite as well as a few others tools preconfigured typescript https www typescriptlang org for static type checking eslint https eslint org for code linting prettier https prettier io for code formatting changesets https github com changesets changesets for managing versioning and changelogs github actions https github com changesets action for fully automated package publishing this monorepo is based on this example turborepo design system example https github com vercel turbo tree main examples design system | os |
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iwgtk | about iwgtk is a wireless networking gui for linux it is a front end for iwd inet wireless daemon https iwd wiki kernel org with supported functionality similar to that of iwctl features include viewing and connecting to available networks managing known networks provisioning new networks via wps or wi fi easy connect and an indicator tray icon displaying connection status and signal strength screenshot screenshot iwgtk station mode png usage launch the application window iwgtk launch the indicator daemon iwgtk i autostarting the most common use case for iwgtk is to start the indicator daemon every time you log into your desktop if your desktop environment supports the xdg autostart standard this should happen automatically due to the iwgtk indicator desktop file which is placed in etc xdg autostart during installation a systemd unit file to start the indicator daemon is also provided if your distro uses systemd and your desktop environment supports systemd s graphical session target unit then iwgtk can be started at the beginning of every desktop session by enabling the iwgtk service unit configuration icon colors and other options can be customized by editing the application s configuration file the system wide configuration file is located at sysconfdir iwgtk conf sysconfdir is usually either etc or usr local etc depending on your build time prefix setting iwgtk conf can be copied to config iwgtk conf if desired in which case the system wide configuration file will be ignored for further instructions please see man 5 iwgtk and refer to the comments in iwgtk conf dependencies runtime dependencies iwd 1 29 gtk4 4 6 libqrencode adwaita icon theme or an equivalent icon package build dependencies meson 0 60 0 scdoc installation to build iwgtk and install to usr local run meson setup build cd build meson compile sudo meson install to install to usr instead of usr local replace meson setup build with meson setup prefix usr build troubleshooting the indicator icon doesn t show up iwgtk s icon should show up on any system tray which supports the statusnotifieritem api if your tray only supports the older xembed api then a compatibility layer such as snixembed https git sr ht steef snixembed is required the following trays support statusnotifieritem kde plasma swaybar xfce4 panel must be built with the optional libdbusmenu gtk3 dependency the following trays only support xembed and require a compatibility layer awesomewm i3bar iwgtk and iwctl only work with superuser privileges as of iwd 1 23 membership in either the netdev or wheel group is required to control iwd usermod a g netdev your user account if no netdev group exists on your system then you ll need to create it prior to running the above usermod command groupadd netdev license copyright 2020 2023 jesse lentz jesse twosheds org and contributors see below iwgtk is licensed under the gpl version 3 or later the application icon is from the wifi states https thenounproject com iconsguru collection wifi states collection by i cons https thenounproject com iconsguru from the noun project this icon is licensed under the creative commons by license https creativecommons org licenses by 3 0 us legalcode contributors jove yu jaron vi tor thulinma tinywrkb rico nogueira rolim vaguepenguin andrew benson alex piechowski grepsedawk luigi baldoni | gtk4 iwd linux wifi | front_end |
Celebrity-Website | celebrity website celebrity website as an information technology project there are 6 students works on the project and this is my work using front end design html css js jquery | server |
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Front-End-Toolkit | front end toolkit tweet https img shields io twitter url http shields io svg style social https twitter com intent tweet text front end 20boilerplate 20with 20lots 20of 20automation 20 url https www devbridge com articles the power of our new front end toolkit via devbridge hashtags front end html sass automated accessibility webdev css starter template this project is a set of best practices brought to you by the team at devbridge group https www devbridge com articles the power of our new front end toolkit which will allow you to start project quickly non depending from any javascript framework features that are already here recommended sass structure with good practices from our team live server with auto reload feature sass linting integration and configuration automated svg spriting automated image optimization recommended structure for html layout automated html linting task automated accessibility check with generated reports pre commit hooks to keep project clean component based development approach which will help to transfer code on whatever front end framework you want or just use it as a plain html site generator prepared code hygiene tools like editor config nvmrc npmrc files and small things like npm scripts in development html components with basic styling states and best accessibility practices more detailed instructions and explanations why we are doing things in this way installation to setup new project clone this repo git clone https github com devbridge front end toolkit git switch to right node version using nvm https github com creationix nvm which node version is required defined in nvmrc https github com devbridge front end toolkit blob v2 dev nvmrc file nvm use version number from nvmrc file inside project folder install dependencies from package json npm i to start developing run npm script command npm run start or launch command with check dependencies mode which will check if all needed dependencies are installed npm run start safe it will start all development tasks prepare assets compile html and css bundle javascript and add file watchers now you can configure your project license the mit license mit | front_end |
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ESD_Lab | esd lab embedded systems design i lab | os |
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fire-detection-system-in-python-opencv | fire detection system in python using hsv color introduction the fire detection system is a security system the primary function of this system is to detect fires and turn on alarm to warn fire accidents this system is written in python with opencv computer vision module it is using the hsv color algorithm to detect fires this project provides a computer vision based technique for detecting fire and identifying hazardous fire by processing the video data generated by an ordinary camera technologies python opencv smtplib hsv color algorithm github readme content image 1 jpg what is hsv color algorithm github readme content hsv 1 jpg github readme content hsv 0 png color isolation can be achieved by extracting a particular hsv hue saturation value from an image the algorithm is simple and the main steps are as follows step 1 rgb to hsv conversion step 2 apply a threshold mask we want to convert the image to hsv because working with hsv values is much easier to isolate colors in the hsv representation of color hue determines the color you want saturation determines how intense the color is and value determines the lightness of the image as can be seen in the image below 0 on the wheel would specify a mild red color and 240 would specify a blue color configuration setup install playsound pip install playsound install opncv pip install opencv python execution python fire detector py social links linkedin profile https www linkedin com in gunarakulangunaretnam facebook profile https www facebook com gunarakulangunaratnam instagram profile https www instagram com gunarakulangunaretnam twitter profile https twitter com gunarakulangr kaggle profile https www kaggle com gunarakulangr tiktok profile https www tiktok com gunarakulangunaretnam youtube profile https www youtube com channel ucmwked5sabgvzsckjzurjxa | computer-vision fire-detection-opencv image-processing opencv opencv-python python | ai |
token-core-ios | tokencore tokencore is a blockchain library tokencore provides the relatively consistent api that allows you to manage your wallets and sign transactions in btc eth and eos chains simultaneously in addition tokencore introduces the concept of identity you can use the same mnemonic to manage wallets on the three chains installation to install tokencore use cocoapods and add this to your podfile pod tokencore git https github com consenlabs token core ios git branch master run the example the example show how to manager the wallet and sign transaction the eos sign will coming soon cd example pod install try the api create new identity and derive the eth btc wallets swift you should create or recover identity first before you create other wallets do var metadata walletmeta source newidentity metadata network network mainnet metadata segwit p2wpkh p2wpkh means that the derived btc wallet is a segwit wallet metadata name myfirstidentity let mnemonic identity try identity createidentity password testdata password metadata metadata let ethereumwallet identity wallets 0 let bitcoinwalelt identity wallets 1 catch print createidentity failed error error export wallet swift let prvkey try walletmanager exportprivatekey walletid ethereumwallet walletid password testdata password print privatekey prvkey let mnemonics try walletmanager exportmnemonic walletid ethereumwallet walletid password testdata password print mnemonic mnemonics let keystore try walletmanager exportkeystore walletid ethereumwallet walletid password testdata password print keystore keystore output privatekey f653be3f639f45ea1ed3eb152829b6d881ce62257aa873891e06fa9569a8d9aa mnemonic tide inmate cloud around wise bargain celery cement jungle melody galaxy grocery keystore id c7575eba 3ae3 4cc3 86ba 2eb9c6839cad version 3 crypto ciphertext 7083ba3dd5470ba4be4237604625e05fa6b668954d270beb848365cbf6933ec5 mac f4f9ea8d42ff348b11fc146c396da446cc975309b3538e08a58c0b218bddd15d cipher aes 128 ctr cipherparams iv db3f523faf4da4f1c6edcd7bc1386879 kdf pbkdf2 kdfparams dklen 32 c 10240 prf hmac sha256 salt 0ce830e9f888dfe33c31e6cfc444d6f588161c9d4128d4066ee5dfdcbc5d0079 address 4a1c2072ac67b616e5c578fd9e2a4d30e0158471 signtransaction swift let signresult walletmanager ethsigntransaction walletid string nonce string gasprice string gaslimit string to string value string data string password string chainid int let signedtx signresult signedtx this is the signature result which you need to broadcast let txhahs signresult txhash this is txhash which you can use for locating your transaction record troubleshooting for macos 10 14 mojave and xcode 10 install library developer commandlinetools packages macos sdk headers for macos 10 14 pkg since xcode command line tools don t install to usr include anymore todo test on objective c upgrade the bigint from 3 0 to 3 1 copyright and license copyright 2018 imtoken pte ltd licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license thanks and more info thanks bitcoinj corebitcoin and others library | ethereum eos bitcoin blockchain | blockchain |
ID-entify | alt tag https github com billyv4 id entify blob master images id entify logo jpg by carlos ram rez l billyv4 v 1 0 about id entify is a tool that allows you to search for information in the passive way related to a domain search for information related to a domain emails ip addresses subdomains information on web technology type of firewall ns and mx records the tools used in the information search are fierce dnsrecon dnsenum dig blindcrawl owasp amass nslookup whatweb wafw00f nmap http waf detect http waf fingerprint whois theharvester to execute this script download the script git clone https github com billyv4 id entify git cd id entify chmod x id entify sh run the script step 1 the next command is to create a workspace with tmux in which programs are executed in the background storing the information in the id domain raw data folder id entify d google com step 2 when the programs are finished the tmux workspace will be closed automatically the filtered information will be stored in id domain greep data to filter the information manually execute the following command id entify g google com screenshot step 1 alt tag https github com billyv4 id entify blob master images id entify 20search png step 2 alt tag https github com billyv4 id entify blob master images id entify 20greep png | server |
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iota.js | iota js this library is functionally complete but it is recommended to use iota rs https github com iotaledger iota rs the rust library will be more heavily maintained and is more performant mono repo containing client and supporting packages for the iota chrysalis network implemented in typescript to strongly type the objects sent and received from the api runs in both nodejs and browser environments prerequisites shell npm install iota iota js example js const singlenodeclient require iota iota js async function run const client new singlenodeclient https chrysalis nodes iota org const info await client info console log node info console log tname info name console log tversion info version console log tis healthy info ishealthy console log tnetwork id info networkid console log tlatest milestone index info latestmilestoneindex console log tconfirmed milestone index info confirmedmilestoneindex console log tpruning index info pruningindex console log tfeatures info features console log tmin pow score info minpowscore run then console log done catch err console error err packages for more details on the main package see iota iota js packages iota readme md other packages within the framework are iota util js packages util readme md utility classes and methods iota crypto js packages crypto readme md cryptographic implementations iota mqtt js packages mqtt readme md mqtt client iota pow neon js packages pow neon readme md pow as a multi threaded neon rust binding iota pow node js packages mqtt readme md pow as a multi threaded node module iota pow wasm js packages pow wasm readme md pow as a multi threaded wasm module examples please find other examples in the packages iota examples packages iota examples folder simple performs basic api operations address demonstrates address generation from a bip39 mnemonic seed using raw and bip32 path methods transaction demonstrates how to send a transaction and call some of the other higher level functions data storing and retrieving data on the tangle browser demonstrates direct inclusion and use of the library in an html page peers demonstrates peer management pow demonstrates using one of the other pow packages supporting the project if the iota js has been useful to you and you feel like contributing consider submitting a bug report https github com iotaledger iota js issues new feature request https github com iotaledger iota js issues new or a pull request https github com iotaledger iota js pulls see our contributing guidelines github contributing md for more information joining the discussion if you want to get involved in the community need help with getting set up have any issues or just want to discuss iota feel free to join our discord https discord iota org license the separate packages all contain their own licenses | iota-javascript-library iota iota-library | server |
heart-you | heartyou class se346 j11 mobile device application development lecturer ms hu nh tu n anh apk file download https drive google com file d 1k8eqzdxfeh31w5mghdsnrzseihmdrf0 view documentation read online https drive google com file d 18eewjby3a3kkwhdbuavudidspebtj2cs view introduction heartyou is a question asking and answering social network application that gives people the opportunity to ask and answer questions totally anonymously by text or by audio team members student id full name role 15520008 nguy n ph c thi n n leader 15520105 nguy n s t member 15520312 ph m l huy member technical stack this is our final project for mobile device application development class at university of information technology it was developed using the mern stack mongodb express react native and node js dependencies dependency version node js 8 12 0 npm 6 4 1 express 4 16 4 mongodb 3 4 14 mongoose 5 3 13 react native 0 57 6 redux 4 0 1 server side node js node js https nodejs org en is a javascript runtime built on chrome s v8 javascript engine https developers google com v8 node js uses an event driven non blocking i o model that makes it lightweight and efficient node js package ecosystem npm https www npmjs com is the largest ecosystem of open source libraries in the world express express https expressjs com is a fast unopinionated minimalist web framework for node js https nodejs org en mongodb mongodb https www mongodb com is a document database with the scalability and flexibility that you want with the querying and indexing that you need client side react native ft redux react native https facebook github io react native is a javascript framework for writing real natively rendering mobile applications for ios and android redux https redux js org is a predictable state container for javascript apps | front_end |
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smart-video-workshop | optimized inference at the edge with intel tools and technologies this workshop will walk you through the workflow using intel distribution of openvino toolkit for inferencing deep learning algorithms that help accelerate vision automatic speech recognition natural language processing recommendation systems and many other applications you will learn how to optimize and improve performance with or without external accelerators and utilize tools to help you identify the best hardware configuration for your needs this workshop will also outline the various frameworks and topologies supported by intel distribution of openvino toolkit warning labs of this workshop have been validated with intel distribution of openvino toolkit 2021 3 openvino toolkit 2021 3 394 some of the videos shown below is based on openvino 2021 2 might be slightly different from the slides but the content is largely the same fpga plugin will no longer be supported by the openvino stardard release you can find the fpga content from earlier branches workshop agenda intel distribution of openvino toolkit overview training slides part1 presentations openvino training part1 2021 3 pdf part2 presentations openvino training part2 2021 3 pdf training video series intel distribution of openvino toolkit training https software intel com content www us en develop video series openvino toolkit on demand workshop html lab setup lab setup instructions lab setup md warning please make sure you have gone through all the steps in the lab setup all the labs below are based on the assumption that user has correctly installed openvino toolkit on the local development system model optimizer lab1 optimize a caffe classification model squeezenet v1 1 labs optimize caffe squeezenet md lab2 optimize a tensorflow object detection model ssd with mobilenet labs optimize tensorflow mobilenet ssd md inference engine lab3 run classfication sample application with the optimized squeezenet v1 1 labs run classification sample md lab4 run object detection sample application with the optimized ssd with mobilenet labs run object detection sample md lab5 run benchmark app with hetero plugin labs run benchmark hetero md accelerators based on intel movidius vision processing unit lab6 hw acceleration with intel movidius neural compute stick2 labs run samples with ncs2 md lab7 run benchmark app with multi plugin labs run benchmark multi md multiple models in one application lab8 run security barrier demo application labs run security barrier demo md deep learning workbench short video tutorials on youtube https www youtube com playlist list pltsehiqlifgm6ltiaeh9fl8qfxie u4fw deep learning streamer lab9 build a media analytic pipeline with dl streamer labs build dl streamer pipeline md demo dl streamer pipeline https software intel com content www us en develop videos part 14 demonstration of deep learning streamer html intel devcloud for the edge demo intel devcloud for the edge walkthrough https software intel com content www us en develop videos part 16 demonstration of intel devcloud for the edge html further reading materials support for microsoft onnx runtime in openvino slides onnx runtime and openvino presentations onnx runtime and openvino pdf disclaimer intel and the intel logo are trademarks of intel corporation or its subsidiaries in the u s and or other countries other names and brands may be claimed as the property of others | ai |
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frontend | file browser front end circleci https img shields io circleci project github filebrowser frontend svg style flat square https circleci com gh filebrowser frontend npm https img shields io npm v filebrowser frontend svg style flat square standard readme compliant https img shields io badge readme 20style standard brightgreen svg style flat square https github com richardlitt standard readme chat irc https img shields io badge freenode 23filebrowser blue svg style flat square http webchat freenode net channels 23filebrowser this is an example file with default selections install npm install filebrowser frontend usage this package is not prepared to be used by other projects than file browser https github com filebrowser filebrowser itself contribute check the community repository https github com filebrowser community for more information license apache 2 0 license file browser contributors | vue filebrowser filemanager ui ux | front_end |
dotNET-FrontEnd-to-BackEnd-with-DAPR-on-Azure-Container-Apps | asp net core front end 2 back end apis on azure container apps this repository contains a simple scenario built to demonstrate how asp net core 6 0 can be used to build a cloud native application hosted in azure container apps the repository consists of the following projects and folders store a blazor server project representing the frontend of an online store the store s ui shows a list of all the products in the store and their associated inventory status products api a simple api that generates fake product names using the open source nuget package bogus https github com bchavez bogus inventory api a simple api that provides a random number for a given product id string the values of each string integer pair are stored in memory cache so they are consistent between api calls azure folder contains azure bicep files used for creating and configuring all the azure resources github actions workflow file used to deploy the app using ci cd what you ll learn this exercise will introduce you to a variety of concepts with links to supporting documentation throughout the tutorial azure container apps https docs microsoft com azure container apps overview github actions https github com features actions azure container registry https docs microsoft com azure container registry azure bicep https docs microsoft com azure azure resource manager bicep overview tabs bicep prerequisites you ll need an azure subscription and a very small set of tools and skills to get started 1 an azure subscription sign up for free https azure microsoft com free 2 a github account with access to github actions 3 either the azure cli https docs microsoft com cli azure install azure cli installed locally or access to github codespaces https github com features codespaces which would enable you to do develop in your browser topology diagram the resultant application is an azure container environment hosted set of containers the products api the inventory api and the store blazor server front end application topology docs media topology png internet traffic should not be able to directly access either of the back end apis as each of these containers is marked as internal ingress only during the deployment phase internet traffic hitting the store your app your region azurecontainerapps io url should be proxied to the frontend container which in turn makes outbound calls to both the products and inventory apis within the azure container apps environment setup by the end of this section you ll have a 3 node app running in azure this setup process consists of two steps and should take you around 15 minutes 1 use the azure cli to create an azure service principal then store that principal s json output to a github secret so the github actions ci cd process can log into your azure subscription and deploy the code 2 edit the deploy yml workflow file and push the changes into a new deploy branch triggering github actions to build the net projects into containers and push those containers into a new azure container apps environment authenticate to azure and configure the repository with a secret 1 fork this repository to your own github organization 2 create an azure service principal using the azure cli bash subscriptionid az account show query id output tsv az ad sp create for rbac sdk auth name webandapisample role contributor scopes subscriptions subscriptionid 3 copy the json written to the screen to your clipboard json clientid clientsecret subscriptionid tenantid activedirectoryendpointurl https login microsoftonline com resourcemanagerendpointurl https brazilus management azure com activedirectorygraphresourceid https graph windows net sqlmanagementendpointurl https management core windows net 8443 galleryendpointurl https gallery azure com managementendpointurl https management core windows net 4 create a new github secret in your fork of this repository named azurespn paste the json returned from the azure cli into this new secret once you ve done this you ll see the secret in your fork of the repository the azurespn secret in github docs media secrets png note never save the json to disk for it will enable anyone who obtains this json code to create or edit resources in your azure subscription deploy the code using github actions the easiest way to deploy the code is to make a commit directly to the deploy branch do this by navigating to the deploy yml file in your browser and clicking the edit button edit the deployment workflow file docs media edit the deploy file png provide a custom resource group name for the app and then commit the change to a new branch named deploy create the deploy branch docs media deploy png once you click the propose changes button you ll be in create a pull request mode don t worry about creating the pull request yet just click on the actions tab and you ll see that the deployment ci cd process has already started build started docs media deploy started png when you click into the workflow you ll see that there are 3 phases the ci cd will run through 1 provision the azure resources will be created that eventually house your app 2 build the various net projects are build into containers and published into the azure container registry instance created during provision 3 deploy once build completes the images are in acr so the azure container apps are updated to host the newly published container images deployment phases docs media cicd phases png after a few minutes all three steps in the workflow will be completed and each box in the workflow diagram will reflect success if anything fails you can click into the individual process step to see the detailed log output note if you do see any failures or issues please submit an issue so we can update the sample likewise if you have ideas that could make it better feel free to submit a pull request deployment success docs media success png with the projects deployed to azure you can now test the app to make sure it works try the app in azure the deploy ci cd process creates a series of resources in your azure subscription these are used primarily for hosting the project code but there s also a few additional resources that aid with monitoring and observing how the app is running in the deployed environment resource resource type purpose storeai application insights this provides telemetry and diagnostic information for when i want to monitor how the app is performing or for when things need troubleshooting store an azure container app that houses the code for the front end the store app is the store s frontend app running a blazor server project that reaches out to the backend apis products an azure container app that houses the code for a minimal api this api is a swagger ui enabled api that hands back product names and ids to callers inventory an azure container app that houses the code for a minimal api this api is a swagger ui enabled api that hands back quantities for product ids a client would need to call the products api first to get the product id list then use those product ids as parameters to the inventory api to get the quantity of any particular item in inventory storeenv an azure container apps environment this environment serves as the networking meta container for all of the instances of all of the container apps comprising the app storeacr an azure container registry this is the container registry into which the ci cd process publishes my application containers when i commit code to the deploy branch from this registry the containers are pulled and loaded into azure container apps storelogs log analytics workspace this is where i can perform custom kusto https docs microsoft com azure data explorer kusto query queries against the application telemetry and time sliced views of how the app is performing and scaling over time in the environment the resources are shown here in the azure portal resources in the portal docs media azure portal png click on the store container app to open it up in the azure portal in the overview tab you ll see a url view the store s public url docs media get public url png clicking that url will open the app s frontend up in the browser the product list once the app is running docs media store ui png you ll see that the first request will take slightly longer than subsequent requests on the first request to the page the apis are called on the server side the code uses imemorycache to store the results of the api calls in memory so subsequent calls will use the cached payload rather than make live requests each time | front_end |
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practical-machine-learning-with-python | practical machine learning with python a problem solver s guide to building real world intelligent systems data is the new oil is a saying which you must have heard by now along with the huge interest building up around big data and machine learning in the recent past along with artificial intelligence and deep learning besides this data scientists have been termed as having the sexiest job in the 21st century which makes it all the more worthwhile to build up some valuable expertise in these areas getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web practical machine learning with python https github com dipanjans practical machine learning with python contents follows a structured and comprehensive three tiered approach packed with concepts methodologies hands on examples and code this book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with machine learning and deep learning by following a data driven mindset by using real world case studies that leverage the popular python machine learning ecosystem this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner the concepts techniques tools frameworks and methodologies used in this book will teach you how to think design build and execute machine learning systems and projects successfully this repository contains all the code notebooks and examples used in this book we will also be adding bonus content here from time to time so keep watching this space get the book div a target blank href https www apress com us book 9781484232064 img src media banners apress logo png alt apress align left a a target blank href http www springer com us book 9781484232064 img src media banners springer logo png alt springer align left a a target blank href https www amazon com practical machine learning python problem solvers dp 1484232062 ref sr 1 10 ie utf8 qid 1513756537 sr 8 10 keywords practical machine learning with python img src media banners amazon logo jpg alt amazon align left a br div br div div br br about the book a target blank href https www amazon com practical machine learning python problem solvers dp 1484232062 ref sr 1 10 ie utf8 qid 1513756537 sr 8 10 keywords practical machine learning with python img src media banners cover front jpg alt book cover width 250 align left a master the essential skills needed to recognize and solve complex problems with machine learning and deep learning using real world examples that leverage the popular python machine learning ecosystem this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner the concepts techniques tools frameworks and methodologies used in this book will teach you how to think design build and execute machine learning systems and projects successfully we focus on leveraging the latest state of the art data analysis machine learning and deep learning frameworks including scikit learn http scikit learn org stable pandas https pandas pydata org statsmodels http www statsmodels org stable index html spacy https spacy io nltk http www nltk org gensim https radimrehurek com gensim tensorflow https www tensorflow org keras https keras io skater https www datascience com resources tools skater and several others to process wrangle analyze visualize and model on real world datasets and problems with a learn by doing approach we try to abstract out complex theory and concepts while presenting the essentials wherever necessary which often tends to hold back practitioners from leveraging the true power of machine learning to solve their own problems div style font size 0 5em sup edition 1st emsp pages 532 emsp language english br book title practical machine learning with python emsp publisher apress a part of springer emsp copyright dipanjan sarkar raghav bali tushar sharma br print isbn 978 1 4842 3206 4 emsp online isbn 978 1 4842 3207 1 emsp doi 10 1007 978 1 4842 3207 1 br div br practical machine learning with python https github com dipanjans practical machine learning with python contents follows a structured and comprehensive three tiered approach packed with hands on examples and code part 1 https github com dipanjans practical machine learning with python contents focuses on understanding machine learning concepts and tools this includes machine learning basics with a broad overview of algorithms techniques concepts and applications followed by a tour of the entire python machine learning ecosystem brief guides for useful machine learning tools libraries and frameworks are also covered part 2 https github com dipanjans practical machine learning with python contents details standard machine learning pipelines with an emphasis on data processing analysis feature engineering and modeling you will learn how to process wrangle summarize and visualize data in its various forms feature engineering and selection methodologies will be covered in detail with real world datasets followed by model building tuning interpretation and deployment part 3 https github com dipanjans practical machine learning with python contents explores multiple real world case studies spanning diverse domains and industries like retail transportation movies music marketing computer vision and finance for each case study you will learn the application of various machine learning techniques and methods the hands on examples will help you become familiar with state of the art machine learning tools and techniques and understand what algorithms are best suited for any problem practical machine learning with python https github com dipanjans practical machine learning with python contents will empower you to start solving your own problems with machine learning today br contents https github com dipanjans practical machine learning with python tree master notebooks book contents part i understanding machine learning https github com dipanjans practical machine learning with python tree master notebooks part i understanding machine learning chapter 1 machine learning basics https github com dipanjans practical machine learning with python tree master notebooks ch01 machine learning basics chapter 1 machine learning basics chapter 2 the python machine learning ecosystem https github com dipanjans practical machine learning with python tree master notebooks ch02 the python ml ecosystem chapter 2 the python machine learning ecosystem part ii the machine learning pipeline https github com dipanjans practical machine learning with python tree master notebooks part ii the machine learning pipeline chapter 3 processing wrangling and visualizing data https github com dipanjans practical machine learning with python tree master notebooks ch03 processing wrangling and visualizing data chapter 3 processing wrangling and visualizing data chapter 4 feature engineering and selection https github com dipanjans practical machine learning with python tree master notebooks ch04 feature engineering and selection chapter 4 feature engineering and selection chapter 5 building tuning and deploying models https github com dipanjans practical machine learning with python tree master notebooks ch05 building tuning and deploying models chapter 5 building tuning and deploying models part iii real world case studies https github com dipanjans practical machine learning with python tree master notebooks part iii real world case studies chapter 6 analyzing bike sharing trends https github com dipanjans practical machine learning with python tree master notebooks ch06 analyzing bike sharing trends chapter 6 analyzing bike sharing trends chapter 7 analyzing movie reviews sentiment https github com dipanjans practical machine learning with python tree master notebooks ch07 analyzing movie reviews sentiment chapter 7 analyzing movie reviews sentiment chapter 8 customer segmentation and effective cross selling https github com dipanjans practical machine learning with python tree master notebooks ch08 customer segmentation and effective cross selling chapter 8 customer segmentation and effective cross selling chapter 9 analyzing wine types and quality https github com dipanjans practical machine learning with python tree master notebooks ch09 analyzing wine types and quality chapter 9 analyzing wine types and quality chapter 10 analyzing music trends and recommendations https github com dipanjans practical machine learning with python tree master notebooks ch10 analyzing music trends and recommendations chapter 10 analyzing music trends and recommendations chapter 11 forecasting stock and commodity prices https github com dipanjans practical machine learning with python tree master notebooks ch11 forecasting stock and commodity prices chapter 11 forecasting stock and commodity prices chapter 12 deep learning for computer vision https github com dipanjans practical machine learning with python tree master notebooks ch12 deep learning for computer vision chapter 12 deep learning for computer vision what you ll learn execute end to end machine learning projects and systems implement hands on examples with industry standard open source robust machine learning tools and frameworks review case studies depicting applications of machine learning and deep learning on diverse domains and industries apply a wide range of machine learning models including regression classification and clustering understand and apply the latest models and methodologies from deep learning including cnns rnns lstms and transfer learning powered by the following frameworks a target blank href https anaconda org img src media banners anaconda logo jpg alt anaconda a a target blank href http jupyter org img src media banners jupyter logo jpg alt jupyter a a target blank href http www numpy org img src media banners numpy logo jpg alt numpy a a target blank href https www scipy org img src media banners scipy logo jpg alt scipy a a target blank href https pandas pydata org img src media banners pandas logo jpg alt pandas a a target blank href http www statsmodels org stable index html img src media banners statsmodels logo jpg alt statsmodels a a target blank href http docs python requests org en master img src media banners requests logo jpg alt requests a a target blank href http www nltk org img src media banners nltk logo jpg alt nltk a a target blank href https radimrehurek com gensim img src media banners gensim logo jpg alt gensim a a target blank href https spacy io img src media banners spacy logo jpg alt spacy a a target blank href http scikit learn org stable img src media banners scikit learn logo jpg alt scikit learn a a target blank href https www datascience com resources tools skater img src media banners skater logo png alt skater a a target blank href https facebook github io prophet img src media banners prophet logo jpg alt prophet a a target blank href https keras io img src media banners keras logo jpg alt keras a a target blank href https www tensorflow org img src media banners tensorflow logo jpg alt tensorflow a a target blank href https matplotlib org img src media banners matplotlib logo jpg alt matplotlib a a target blank href https orange biolab si img src media banners orange logo jpg alt orange a a target blank href https seaborn pydata org img src media banners seaborn logo jpg alt seaborn a a target blank href https plot ly img src media banners plotly logo jpg alt plotly a a target blank href https www crummy com software beautifulsoup bs4 doc img src media banners bs logo jpg alt beautiful soup a br audience this book has been specially written for it professionals analysts developers data scientists engineers graduate students and anyone with an interest to analyze and derive insights from data br acknowledgements tba br | machine-learning deep-learning python classification clustering natural-language-processing computer-vision spacy nltk scikit-learn prophet time-series-analysis convolutional-neural-networks tensorflow keras statsmodels pandas jupyter notebook jupyter-notebook | ai |
CVTron | cv tron logo png http ac 5focnst0 clouddn com 1a6303279b0a69375abc png build status https travis ci org unarxiv cvtron svg branch master https travis ci org unarxiv cvtron codebeat badge https codebeat co badges 8c64c6df a1dd 40a1 9220 570b811282d8 https codebeat co projects github com unarxiv cvtron master test coverage https api codeclimate com v1 badges 721858de11e15ef33f2a test coverage https codeclimate com github unarxiv cvtron test coverage slack https img shields io badge chat on 20slack 7289da svg https cvtron slack com cvtron is a computer vision library for industry use it tries to provide an out of the box experience for our customers and help them build on top of commonly used algorithms and models currently in the first version of the cv tron it provides image classification semantic segmentation and object detection services in the box users could use it for either training on their own datasets or use some kind of out of the box models such as vgg net and inception cv tron also provides both web ui and cli tool for rapid deployment and local tests cv tron is built on top of tensorflow https github com tensorflow tensorflow and tensorlayer https github com tensorlayer tensorlayer community support get community support on slack https join slack com t cvtron shared invite enqtmzi3mdmznjm3nzy2lty1ywrizmqwnde5odayytrhngfhogm2owezyjvlnzzmzdc0yjmxymyymzk2y2fiymu4yzhmytvinju3ztjlyjq contribution please visit contributing guidelines https github com cv group cvtron blob master contributing md 0 https sourcerer io fame xzyaoi unarxiv cvtron images 0 https sourcerer io fame xzyaoi unarxiv cvtron links 0 1 https sourcerer io fame xzyaoi unarxiv cvtron images 1 https sourcerer io fame xzyaoi unarxiv cvtron links 1 2 https sourcerer io fame xzyaoi unarxiv cvtron images 2 https sourcerer io fame xzyaoi unarxiv cvtron links 2 3 https sourcerer io fame xzyaoi unarxiv cvtron images 3 https sourcerer io fame xzyaoi unarxiv cvtron links 3 4 https sourcerer io fame xzyaoi unarxiv cvtron images 4 https sourcerer io fame xzyaoi unarxiv cvtron links 4 5 https sourcerer io fame xzyaoi unarxiv cvtron images 5 https sourcerer io fame xzyaoi unarxiv cvtron links 5 6 https sourcerer io fame xzyaoi unarxiv cvtron images 6 https sourcerer io fame xzyaoi unarxiv cvtron links 6 7 https sourcerer io fame xzyaoi unarxiv cvtron images 7 https sourcerer io fame xzyaoi unarxiv cvtron links 7 side project if you are looking for the answer to build a server with cvtron built in you can refer to cvtron serve https github com cv group cvtron serve and cvtron vis https github com unarxiv cvtron vis we are still working on the cli tools for other purposes | ai |
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frontend-v1 | penguin statistics img src https penguin stats s3 ap southeast 1 amazonaws com penguin stats logo png width 150 height 150 https penguin stats io https penguin stats io repo repo angular cli https github com angular angular cli 1 npm 2 angular cli npm install g angular cli 1 npm install 2 ng serve 3 compiled successfully http localhost 4200 http localhost 4200 build ng build prod base href dist issue https penguin stats io penguin stats | penguin-statistics | front_end |
paragon | paragon build status https github com openedx paragon actions workflows release yml badge svg https github com openedx paragon actions workflows release yml npm version https img shields io npm v edx paragon svg edx paragon status https img shields io badge status maintained brightgreen license https img shields io github license openedx paragon svg codecov https codecov io gh edx paragon branch master graph badge svg token x1tzmnduy9 https codecov io gh edx paragon npm downloads https img shields io npm dw edx paragon https www npmjs com package edx paragon purpose paragon is a pattern library containing accessible https www w3 org wai standards guidelines aria react components and a scss foundation built on twitter bootstrap paragon is developed for the open edx https openedx org platform documentation lives at https paragon openedx netlify app getting started react components paragon components require react 16 or higher to install paragon into your project in terminal npm i save edx paragon in your react project import componentname from edx paragon scss foundation usage for open edx and others index scss any custom scss variables should be defined here import edx paragon scss core core scss usage on with edx brand index scss import edx brand paragon fonts scss import edx brand paragon variables scss import edx paragon scss core core scss import edx brand paragon overrides scss note that including fonts will affect performance in some applications may choose not to load the custom font to keep it highly performant paragon cli the paragon cli command line interface is a tool that provides various utility commands to automate actions within the open edx environment available commands paragon install theme theme installs the specific edx brand package use paragon help to see more information getting help please reach out to the paragon working group pwg open edx slack request an invite https openedx org slack wg paragon https openedx slack com archives c02nr285kv4 github issues https github com openedx paragon issues new template blank issue md weekly pwg meeting https calendar google com calendar embed src c v86shrnegshsqgp4fj2k94u7bc 40group calendar google com ctz america 2fnew york internationalization paragon supports internationalization for its components out of the box with the support of react intl https formatjs io docs react intl you may view translated strings for each component on the documentation website by switching languages in the settings due to paragon s dependence on react intl that means that your whole app needs to be wrapped in its provider e g javascript import intlprovider from react intl import messages as paragonmessages from edx paragon reactdom render intlprovider locale userslocale messages paragonmessages userslocale app intlprovider document getelementbyid root note that if you are using edx frontend platform s appprovider component you don t need a separate context as intlprovider is already included you would only need to add paragon s i18n messages like this javascript import app ready subscribe initialize from edx frontend platform import appprovider from edx frontend platform react import messages as paragonmessages from edx paragon import app from app this is your app s i18n messages import appmessages from i18n subscribe app ready reactdom render appprovider app appprovider document getelementbyid root initialize this will add your app s messages as well as paragon s messages to your app messages appmessages paragonmessages here you will typically provide other configurations for you app contributing please refer to the how to contribute https openedx org r how to contribute documentation and code of conduct https openedx org code of conduct from open edx the paragon working group accepts bug fixes new features documentation and security patches you may find open issues here https github com openedx paragon issues or by visiting the paragon working group project board https github com orgs openedx projects 43 views 15 if you d like to contribute a new component or update an existing component please consider reaching out to the paragon working group via the channels described above getting help to propose your changes the paragon working group looks forward to your contributions development developing locally against a micro frontend mfe if you want to test the changes with local mfe setup you need to create a module config js file in your mfe s directory containing local module overrides after that the webpack build for your application will automatically pick your local version of paragon and use it the example of module config js file looks like this for more details about module config js refer to the frontend build documentation https github com openedx frontend build local module configuration for webpack javascript module exports modules you want to use from local source code adding a module here means that when your mfe runs its build it ll resolve the source from peer directories of the app modulename the name you use to import code from the module dir the relative path to the module s source code dist the sub directory of the source code where it puts its build artifact often dist localmodules modulename edx paragon scss core dir src paragon dist scss core modulename edx paragon icons dir src paragon dist icons note that using dist dist will require you to run npm build in paragon to add local changes to the dist directory so that they can be picked up by the mfe to avoid doing that you can use dist src to get any local changes hot reloaded on save in the mfe modulename edx paragon dir src paragon dist dist then when importing paragon s core scss in your mfe the import needs to begin with a tilde so that path to your local paragon repository gets resolved correctly import edx paragon scss core internationalization when developing a new component you should generally follow three rules 1 the component should not have any hardcoded strings as it would be impossible for consumers to translate it 2 internationalize all default values of props that expect strings i e for places where you need to display a string and it s okay if it is a react element use formattedmessage e g see alert src alert index jsx component for a full example javascript import formattedmessage from react intl formattedmessage id pgn alert closelabel defaultmessage dismiss description label of a close button on alert component for places where the display string has to be a plain javascript string use formatmessage this would require access to intl object from react intl e g for class components use injectintl hoc javascript import injectintl from react intl class myclasscomponent extends react component render const alttext intl this props const intlalttext alttext intl formatmessage id pgn mycomponent alttext defaultmessage close description close label for toast component return iconbutton alt intlcloselabel onclick variant primary export default injectintl myclasscomponent for functional components use useintl hook javascript import useintl from react intl const myfunctioncomponent alttext const intls useintl const intlalttext alttext intl formatmessage id pgn mycomponent alttext defaultmessage close description close label for toast component return iconbutton alt intlcloselabel onclick variant primary export default myfunctioncomponent notes on the format above id is required and must be a dot separated string of the format pgn componentname subcomponentname propname the defaultmessage is required and should be the english display string the description is optional but highly recommended this text gives context to translators about the string 3 if your component expects a string as a prop allow the prop to also be an element since consumers may want to also pass instance of their own translated string for example you might define a string prop like this javascript mycomponent proptypes myprop proptypes oneoftype proptypes string proptypes element adding a new component 1 start the documentation site development server the paragon documentation site serves both as documentation and as a workbench to create your component within to see your component in action you need to run the documentation site locally npm install npm start 2 create new component to create new component run npm run generate component mycomponent where mycomponent is your new component s name this will create a directory in src that will contain templates for all necessary files to start developing the component mycomponent index jsx readme md mycomponent scss variables scss mycomponent test jsx the script will also automatically export your component from paragon 3 start developing src mycomponent index jsx is where your component lives the file is created with the following template edit it to implement your own component jsx import react from react import proptypes from prop types import classnames from classnames const mycomponent react forwardref classname children ref div ref ref classname classnames png mycomponent classname children div mycomponent defaultprops classname undefined mycomponent proptypes a class name to append to the base element classname proptypes string specifies contents of the component children proptypes node isrequired export default mycomponent 4 optional add styles to your component if your component requires additional styling which most likely is the case edit created scss style sheet in your component s directory src mycomponent mycomponent scss which by default contains an empty class for your component if you wish to use sass variables which is the preferred way of styling the components since values can be easily overridden and customized by the consumers of paragon add them in src mycomponent variables scss this file should contain all variables specific to your component this way the variables will also get automatically picked up by documentation site and displayed on your component s page please note that you need to follow paragon s css styling conventions docs decisions 0012 css styling conventions 5 document your component the documentation for your component lives in src mycomponent readme md the documentation site scans this directory for markdown or mdx files to create pages by default the file is created with following content md title mycomponent type component components mycomponent status new designstatus done devstatus done notes something special about this component describe your component here and give usage examples basic usage jsx live mycomponent hello mycomponent some notes on the format above the top part of the markdown file is known as frontmatter this metadata with consumed by the documentation site to control the title of the page and the doc site navigation title controls the page title of the generated page components is a list of react components by displayname this usually matches the name you define the component as in code in our example so far it is mycomponent components added to this list will be scanned by react docgen for code comments and a props api table will be rendered at the bottom of the page categories is a list of categories where this page will appear the documentation site menu status designstatus devstatus and notes appear in the http localhost 8000 status page jsx code blocks in the markdown file can be made interactive with the live attribute all paragon components and icons are in scope note the scope of this code block is controlled by www components codeblock jsx 6 navigate to your component on the doc site and start building visit the documentation at http localhost 8000 http localhost 8000 and navigate to see your readme md powered page and workbench changes to the readme md file will auto refresh the page eslint paragon runs eslint as a pre commit hook if your code fails linting you will not be able to commit to avoid hitting a giant wall of linter failures when you try to commit we recommend configuring your editor to run eslint http eslint org docs user guide integrations to run eslint in the console at any time run the following npm run lint paragon s eslint config is based off eslint config edx https github com openedx eslint config edx tree master packages eslint config edx which itself is based off eslint config airbnb https www npmjs com package eslint config airbnb paragon uses eslint 3 and will upgrade to v4 as soon as eslint config airbnb releases a supported version which itself comes with a number of built in rules this configuration is highly opinionated and may contain some rules with which you aren t yet familiar like comma dangle http eslint org docs rules comma dangle but rest assured you re writing modern best practice js one of the most powerful features of this eslint config is its inclusion of eslint plugin jsx a11y https github com evcohen eslint plugin jsx a11y this plugin actually enforces accessibility best practices at the linter level it will catch things reviewers might not notice like event handlers bound to noninteractive elements https github com evcohen eslint plugin jsx a11y blob master docs rules no noninteractive element interactions md of course it won t catch all accessibility violations but it s a pretty good low pass filter testing paragon uses jest https facebook github io jest with enzyme https github com airbnb enzyme for tests and coverage both libraries are full featured and very well supported unit testing jest https facebook github io jest is an all in one test runner and assertion library created for use with react components jest s api https facebook github io jest docs en api html is similar to jasmine s and comes with functionality for mocking and spying as well check out the docs https facebook github io jest docs en getting started html for more details they are very comprehensive paragon also uses airbnb s enzyme http airbnb io enzyme library to help render our components within unit tests enzyme comes with a number of utilities for shallow rendering mounting components querying the dom simulating dom events and querying react components themselves read the docs http airbnb io enzyme docs api index html for more details to run the unit tests run npm run test to add unit tests for a component create a file in your component s directory named componentname test js jest will automatically pick up this file and run the tests as part of the suite take a look at dropdown test jsx https github com openedx paragon blob master src dropdown deprecated dropdown test jsx or checkbox test jsx https github com openedx paragon blob master src checkbox checkbox test jsx for examples of good component unit tests run unit tests in chrome devtools inspector to run the unit tests in the chrome devtools inspector run npm run debug test then open chrome inspect in your chrome browser and select the node modules bin jest target to open the chrome devtools you can set breakpoints in chrome devtools or insert a debugger statement into the code to pause execution at that point screenshot of chrome on the chrome inspect page docs inspect chrome jest png snapshot testing jest has built in snapshot testing http facebook github io jest docs en snapshot testing html snapshot testing with jest functionality which serves as a good means of smoketesting components to ensure they render in a predictable way when you modify components or stories or add new components or stories make sure to update the snapshots or else the snapshot tests will fail it s easy to do just run npm run snapshot if the snapshot tests fail it s generally pretty easy to tell whether it s happening because of a bug or because the snapshots need to be updated don t be afraid to inspect the test output for clues coverage paragon measures code coverage using jest s built in coverage flag and report it via codecov https codecov io gh edx paragon shoot for 100 test coverage on your prs but use your best judgment if you re really struggling to cover those last few lines at the very least don t reduce total coverage codecov will fail your build if your pr reduces coverage example app paragon components can have different behavior in the mfe environment example app in the project root allows you to test new changes inside the mfe environment steps to install the example app 1 npm install to install dependencies 2 launch any devstack it is required for mfe to login into the system and set up configs 3 npm run start example mfe to start the app 4 go to the example src mycomponent jsx and use paragon components inside the mfe environment semantic release paragon uses the semantic release package https github com semantic release semantic release to automate its release process creating git tags creating github releases and publishing to npm preview next release version from pull requests as a convenience the node js ci build push check on pull requests includes a step to analyze the commit s and outputs a preview of what version semantic release will publish if a pr gets merged this is done using the dry run option for the semantic release cli which will skip the publish release steps look for a message in this ci step along the lines of the next release version is next release version commit messages semantic release analyzes commit messages to determine whether to create a major minor or patch release https github com semantic release semantic release default commit message format or to skip a release paragon currently uses the default conventional angular changelog rules https github com conventional changelog conventional changelog tree master packages conventional changelog angular which means that there are 3 commit types that will trigger a release 1 feat minor release 2 fix patch release 3 perf patch release there are other commit types https github com conventional changelog commitlint blob master 40commitlint config angular type enum index js l1 l12 that will not trigger a release that you can use at your own discretion suggested prefixes are docs chore style refactor and test for non changelog related tasks breaking changes any of the previous 3 commit types combined with breaking change in the commit message body will trigger a major version release example breaking change commit message perf pencil remove graphitewidth option breaking change the graphitewidth option has been removed the default graphite width of 10mm is always used for performance reason treeshaking paragon is distributed on npm as es6 modules this means that webpack can use treeshaking on any paragon components that a consuming app is not using resulting in greatly reduced bundle sizes to get treeshaking to work your app may require some updates most notably babel 7 see this pr for an example of the changes necessary to update an app to take advantage of treeshaking with paragon https github com openedx frontend app payment pull 48 people the assigned maintainers for this component and other project details may be found in backstage https backstage openedx org catalog default component paragon backstage pulls this data from the catalog info yml file in this repository reporting security issues please do not report security issues in public please email security edx org we tend to prioritize security issues which impact the published edx paragon npm library more so than the documentation website https paragon openedx netlify app or example react application | component-library react-components accessibility design-system react | os |
Company-Name-Standardization | company name standardization using natural language processing to standardize company names introduction problem statement is to standardize a list of similar company names jpmc jpmorgan j p morgan chase to their standard name jpmorgan chase co we could have used traditional string matching approaches like levenshtein distance for string matching but they are really slow for large datasets installation 1 clone or download this repository 2 run pip install r requirements txt inside this repo consider doing this inside of a python virtual environment 3 data xlsx contains the standard list of company names to which you want your current name s to be mapped to i have downloaded the forbes 2000 list and taken the name column for that purpose note the name of the column in data xlsx should be name check out that data xlsx file in the repo for reference 4 use streamlit run app py to start the application image https user images githubusercontent com 21261866 128198978 3677bbdb 9945 446b 8279 bdba792fc331 png the user has the option to enter the name in the search bar like i did jpmc and you could see the result below that it correctly maps jpmc to jpmorgan chase co if you have multiple items then you can upload a txt csv file in with the column name as name i will add a test txt in the repo for reference image https user images githubusercontent com 21261866 128200110 c1aa0357 d831 4623 a219 3f4cff356182 png as you can see it correctly maps jpmc and deloittee wrong spelling to the correct standard name but fails to do in case of datsum it was a random name this is where the human in loop would come and the correct standard name for datsum has to be manually fed into the data xlsx file to improve the model algorithm this is how the model will be optimized as you keep feeding more standard names into the list note if the standard name would be equal to the name that you have mentioned here then it would give no result or blank why is this because we are excluding 100 matches because in that case a wrong spelling name was matching to itself and showing up as a match so if you see a blank empty space then that means that it is correct and a full match i ll attach screenshot for more clarity image https user images githubusercontent com 21261866 128202551 1b6b5e19 1c87 4c8e a0a7 8a6698b9fddd png in this approach you can see that jpmc is matched to the correct name for deloitte touchetohmatsu we are using the standard name so it doesn t return anything and for datsum it s a wrong entry because we don t have it s standard name so that has to be feeded back into the data xlsx file references 1 super fast string matching https bergvca github io 2017 10 14 super fast string matching html 2 sparse dot topn https github com ing bank sparse dot topn 3 boosting the selection of the most similar entities in large scale datasets https medium com wbaa https medium com ingwbaa boosting selection of the most similar entities in large scale datasets 450b3242e618 reach out for any questions gmail https img shields io badge gmail c14438 style flat logo gmail logocolor white mailto sachin93 gmail com | ai |
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Machine-Learning-for-Beginners | machine learning intro traditional artificial intelligence ai algorithms are designed to solve specific problems deep blue was purpose built for playing chess game enemy ai are built to attack the player most traditional methods of ai approximate a solution for a specific problem machine learning tries to learn more general concepts and work in changing dynamic contexts types of learning there are various ways for learning to happen supervised learning the algorithm is given inputs as well as the expected output in a training set the goal is to learn general rules that map inputs to the correct outputs for future inputs unsupervised learning the algorithm is given inputs without expected outputs the goal is to discover hidden patterns that can be used for future learning reinforcement learning the algorithm works with a given input without the expected outputs and a specific goal that it should aim to achieve the algorithm should determine if it is closer to it s goal or not categories based on outputs classification inputs are categorised into two or more groups classification generally uses supervised learning regression similar to classification where the outputs are not discrete there may be categories that were previously unknown clustering inputs are categorised into groups however the groups are not known clustering generally uses unsupervised learning data collecting and preparing data is one of the most important part of machine learning the attributes that describe an object are called features the classification of that object is called a label remove redundant features e g temperature in fahrenheit and temperature in celsius is redundant remove features that don t add value to classifying the object ensure no single feature automatically determine the label getting your hands dirty getting setup with python ensure that you have the below software installed software you require python 2 7 https www python org anaconda recommended https www continuum io getting setup with r software you require r 3 3 0 or r 3 2 5 https www r project org r studio recommended https www rstudio com products rstudio download hello world in this python example we re providing a list of fruit and try to classify them as apples or oranges for simplicity we re using the weight of the fruit and the roughness of it s skin if it s rough we re almost sure it s an orange if it s smooth we re almost sure it s an apple from sklearn import tree the features of fruit weight rough 1 or smooth 0 features 140 1 130 1 150 0 170 0 the labels of the fruit in the feature list orange 1 or apple 0 labels 0 0 1 1 the classifier that we re using a simple decision tree clf tree decisiontreeclassifier training the classifier with the fruit and their labels clf clf fit features labels predicting a new fruit print clf predict 160 0 iris flower classification this is an expansion of the hello world example with the iris flower dataset here we re trying to predict the type of iris flower based on it s petals and sepals this example also demonstrates how to seperate a dataset for training and testing data from sklearn datasets import load iris from sklearn import tree import numpy as np iris load iris test idx 0 50 100 training data training target np delete iris target test idx training data np delete iris data test idx axis 0 testing data testing target iris target test idx testing data iris data test idx clf tree decisiontreeclassifier clf fit training data training target print testing target print clf predict testing data vis code from sklearn externals six import stringio import pydot dot data stringio tree export graphviz clf out file dot data feature names iris feature names class names iris target names filled true rounded true special characters true graph pydot graph from dot data dot data getvalue graph write svg iris svg print testing data 1 testing target 1 print iris feature names iris target names references google developers youtube https www youtube com playlist list plou2xlyxmsiiuibfyad6rfyqu jl2ryal | ai |
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esp-iot-bridge | readme cn md esp iot bridge solution the esp iot bridge solution framework is a complete project directory structure framework based on the iot bridge components which can be used by developers for reference esp iot bridge solution is mainly for the connection and communication between various network interfaces realized by iot application scenarios such as interconnection between spi sdio usb wi fi ethernet and other network interfaces and can be combined with other solutions to form more complex application scenarios such as wi fi mesh lite rainmaker etc this solution is widely used in wireless hotspots wired network cards 4g internet access and other fields for more information see user guide md components iot bridge user guide md in the examples examples directory demos of some common application scenarios are implemented for users to quickly integrate into their own application projects examples wifi router examples wifi router the device based on the esp iot bridge solution connects to the router through wi fi or ethernet and the smart device such as the phone can access the internet by connecting to the softap hotspot provided by the esp iot bridge device examples wireless nic examples wireless nic esp iot bridge device can be connected to the pc or mcu through multiple network interfaces usb eth spi sdio once connected the pc or mcu will have an additional network card these devices can access the internet after configuring the network examples wired nic examples wired nic esp iot bridge device can connect to the network by plugging the ethernet cable into the lan port of router pc or mcu can connect with the esp iot bridge device through multiple interfaces usb spi sdio to gain internet access examples 4g hotspot examples 4g hotspot esp iot bridge device can be equipped with a mobile network module with a sim card and then convert the cellular network into a wi fi signal the surrounding smart devices can connect to the hotspot from the esp iot bridge device to gain internet access examples 4g nic examples 4g nic esp iot bridge device can be equipped with a mobile network module with a sim card after the network module is connected to the internet the pc or mcu can be connected to it through the network interface eth spi sdio to gain internet access | server |
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ng-clarity | clarity angular logo png clarity angular getting started clarity angular is published as two npm packages npm version ui https img shields io npm v clr ui latest label 40clr 2fui style flat square https www npmjs com package clr ui contains the static styles for building html components npm version clr angular https img shields io npm v clr angular latest label 40clr 2fangular style flat square https www npmjs com package clr angular contains the angular components this package depends on clr ui for styles installing clarity install css only docs installation md installing clarity ui install angular components docs installation md installing clarity angular documentation for documentation on the clarity angular including a list of components and example usage see our website https angular clarity design contributing the clarity project team welcomes contributions from the community for more detailed information see contributing md docs contributing md licenses the clarity design system is licensed under the mit license the font is licensed under the open font license ofl feedback if you find a bug or want to request a new feature please open a github issue https github com vmware clarity ng clarity issues include a link to the reproduction scenario you created by forking one of the clarity stackblitz templates for the version you are using at clarity stackblitz templates https stackblitz com clr team | angular design-system a11y ui-components | os |
geph2 | archived this repository is now obsolete geph has been rewritten in rust at https github com geph official geph4 | os |
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WebDevelopmentCodeHelp | webdevelopmentcodehelp full stack web development codes and notes source code help by love babbar dot batch mern stack web development join my whatsapp community https chat whatsapp com c3m0m72nazp2isdydhzjaq br linked in profile https www linkedin com in turwashchakraborty project name https user images githubusercontent com 121122397 213859728 04b9c039 a685 425c 8469 3fb287a09aec gif | front_end |
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linbox_server | linbox server linbox is a distributed im system designed for www medtree cn download medtree app for iphone at here https itunes apple com cn app yi shu id933709180 mt 8 download medtree app for android at here https medtree cn release android 4 1 1 medtree apk tech netty and tcp protocol kafka as center mq dubbo as rpc framework zookeeper as config center usage build the project gradle zip distribution package is under build distribution linbox server 1 0 zip unzip it and start the server start the tcp client sh tcpserver sh start the router sh consumer sh start the rpc client sh rpcserver sh high availablity and scaliability linbox is distributed by design all of the components of linbox tcp client rpc client and router are all distributed steps to prompt availability use zookeeper cluster use kafka cluster use tcp client cluster im properties src main config im properties need tobe configured for server hosts and ports use rpc client cluster rpc xml src main config spring rpc xml need to be configured for pointed rpc port or you can use dynamic ports generated by system use router client cluster dependencies java version jdk 1 8 is required to build and run it security update for jre be carefual as we use aes 256 encryption you need to download packages to update your local jre environment download package at here http www oracle com technetwork java javase downloads jce8 download 2133166 html unzip and copy the jars into java home jre lib security click this http stackoverflow com questions 6481627 java security illegal key size or default parameters for detail information for this problem mysql mysql database is used as persistence storage for im messages mysql versions there is no special requirement for mysql version but if you want to use emojis you need to use mysql version 5 6 and config the character set to utf8mb4 predefined tables you can find all mysql operations in package com linbox im server storage there are 4 tables predefined in programs sql script could be find in mysql sql src main docs mysql sql configs config mysql connection in im properties src main config im properties redis config redis connection in im properties src main config im properties zookeeper zookeeper is used as config center of the project it is also a dependency of dubbo and kafka config zookeeper in rpc xml src main config spring rpc xml kafka kafka is used as center mq kafka is configd in im properties src main config im properties architecture architecture src main docs im architecture jpg communication protocol linbox use tcp protocal and json structure in communication data package structure is designed as below version type length content 2 byte 2 byte 4 byte json string name description version a flag to show the protocol version of the package type request response command type length the length of the content part content encrypted data content structured in json main communications get history messages im pulloldmessage src main docs im pullmessage jpg get unread count and messages im syncunread src main docs im syncunread jpg send messages im communication src main docs im communication jpg | os |
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autogen | pypi version https badge fury io py pyautogen svg https badge fury io py pyautogen build https github com microsoft autogen actions workflows python package yml badge svg https github com microsoft autogen actions workflows python package yml python version https img shields io badge 3 8 20 7c 203 9 20 7c 203 10 20 7c 203 11 blue downloads https static pepy tech badge pyautogen week https pepy tech project pyautogen https img shields io discord 1153072414184452236 logo discord style flat https discord gg pabnfjrkgz this project is a spinoff from flaml https github com microsoft flaml autogen p align center img src https github com microsoft autogen blob main website static img flaml svg width 200 br p fire heads up pyautogen v0 2 will switch to using openai v1 fire flaml is highlighted in openai s cookbook https github com openai openai cookbook related resources from around the web fire autogen https microsoft github io autogen is released with support for chatgpt and gpt 4 based on cost effective hyperparameter optimization for large language model generation inference https arxiv org abs 2303 04673 fire flaml supports code first automl tuning private preview in microsoft fabric data science https learn microsoft com en us fabric data science what is autogen autogen is a framework that enables the development of llm applications using multiple agents that can converse with each other to solve tasks autogen agents are customizable conversable and seamlessly allow human participation they can operate in various modes that employ combinations of llms human inputs and tools autogen overview https github com microsoft autogen blob main website static img autogen agentchat png autogen enables building next gen llm applications based on multi agent conversations with minimal effort it simplifies the orchestration automation and optimization of a complex llm workflow it maximizes the performance of llm models and overcomes their weaknesses it supports diverse conversation patterns for complex workflows with customizable and conversable agents developers can use autogen to build a wide range of conversation patterns concerning conversation autonomy the number of agents and agent conversation topology it provides a collection of working systems with different complexities these systems span a wide range of applications from various domains and complexities this demonstrates how autogen can easily support diverse conversation patterns autogen provides enhanced llm inference it offers easy performance tuning plus utilities like api unification and caching and advanced usage patterns such as error handling multi config inference context programming etc autogen is powered by collaborative research studies https microsoft github io autogen docs research from microsoft penn state university and the university of washington quickstart the easiest way to start playing is 1 click below to use the github codespace open in github codespaces https github com codespaces badge svg https codespaces new microsoft autogen quickstart 1 2 copy oai config list sample to notebook folder name to oai config list and set the correct config 3 start playing with the notebooks installation autogen requires python version 3 8 it can be installed from pip bash pip install pyautogen minimal dependencies are installed without extra options you can install extra options based on the feature you need for example use the following to install the dependencies needed by the blendsearch https microsoft github io flaml docs use cases tune user defined function blendsearch economical hyperparameter optimization with blended search strategy option bash pip install pyautogen blendsearch find more options in installation https microsoft github io autogen docs installation each of the notebook examples https github com microsoft autogen tree main notebook may require a specific option to be installed for code execution https microsoft github io autogen docs faq code execution we strongly recommend installing the python docker package and using docker for llm inference configurations check the faqs https microsoft github io autogen docs faq set your api endpoints multi agent conversation framework autogen enables the next gen llm applications with a generic multi agent conversation framework it offers customizable and conversable agents that integrate llms tools and humans by automating chat among multiple capable agents one can easily make them collectively perform tasks autonomously or with human feedback including tasks that require using tools via code features of this use case include multi agent conversations autogen agents can communicate with each other to solve tasks this allows for more complex and sophisticated applications than would be possible with a single llm customization autogen agents can be customized to meet the specific needs of an application this includes the ability to choose the llms to use the types of human input to allow and the tools to employ human participation autogen seamlessly allows human participation this means that humans can provide input and feedback to the agents as needed for example https github com microsoft autogen blob main test twoagent py python from autogen import assistantagent userproxyagent config list from json load llm inference endpoints from an env variable or a file see https microsoft github io autogen docs faq set your api endpoints and oai config list sample config list config list from json env or file oai config list you can also set config list directly as a list for example config list model gpt 4 api key your openai api key here assistant assistantagent assistant llm config config list config list user proxy userproxyagent user proxy code execution config work dir coding user proxy initiate chat assistant message plot a chart of nvda and tesla stock price change ytd this initiates an automated chat between the two agents to solve the task this example can be run with python python test twoagent py after the repo is cloned the figure below shows an example conversation flow with autogen agent chat example https github com microsoft autogen blob main website static img chat example png please find more code examples https microsoft github io autogen docs examples autogen agentchat for this feature enhanced llm inferences autogen also helps maximize the utility out of the expensive llms such as chatgpt and gpt 4 it offers enhanced llm inference with powerful functionalities like tuning caching error handling and templating for example you can optimize generations by llm with your own tuning data success metrics and budgets python perform tuning config analysis autogen completion tune data tune data metric success mode max eval func eval func inference budget 0 05 optimization budget 3 num samples 1 perform inference for a test instance response autogen completion create context test instance config please find more code examples https microsoft github io autogen docs examples autogen inference for this feature documentation you can find detailed documentation about autogen here https microsoft github io autogen in addition you can find research https microsoft github io autogen docs research blogposts https microsoft github io autogen blog around autogen and transparency faqs https github com microsoft autogen blob main transparency faqs md discord https discord gg pabnfjrkgz contributing guide https microsoft github io autogen docs contribute roadmap https github com orgs microsoft projects 989 views 3 citation autogen https arxiv org abs 2308 08155 inproceedings wu2023autogen title autogen enabling next gen llm applications via multi agent conversation framework author qingyun wu and gagan bansal and jieyu zhang and yiran wu and shaokun zhang and erkang zhu and beibin li and li jiang and xiaoyun zhang and chi wang year 2023 eprint 2308 08155 archiveprefix arxiv primaryclass cs ai ecooptigen https arxiv org abs 2303 04673 inproceedings wang2023ecooptigen title cost effective hyperparameter optimization for large language model generation inference author chi wang and susan xueqing liu and ahmed h awadallah year 2023 booktitle automl 23 mathchat https arxiv org abs 2306 01337 inproceedings wu2023empirical title an empirical study on challenging math problem solving with gpt 4 author yiran wu and feiran jia and shaokun zhang and hangyu li and erkang zhu and yue wang and yin tat lee and richard peng and qingyun wu and chi wang year 2023 booktitle arxiv preprint arxiv 2306 01337 contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla opensource microsoft com if you are new to github here https help github com categories collaborating with issues and pull requests is a detailed help source on getting involved with development on github when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g status check comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments contributers wall a href https github com microsoft autogen graphs contributors img src https contrib rocks image repo microsoft autogen a legal notices microsoft and any contributors grant you a license to the microsoft documentation and other content in this repository under the creative commons attribution 4 0 international public license https creativecommons org licenses by 4 0 legalcode see the license license file and grant you a license to any code in the repository under the mit license https opensource org licenses mit see the license code license code file microsoft windows microsoft azure and or other microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of microsoft in the united states and or other countries the licenses for this project do not grant you rights to use any microsoft names logos or trademarks microsoft s general trademark guidelines can be found at http go microsoft com fwlink linkid 254653 privacy information can be found at https privacy microsoft com en us microsoft and any contributors reserve all other rights whether under their respective copyrights patents or trademarks whether by implication estoppel or otherwise | agent-based-framework agent-oriented-programming chat chat-application chatbot chatgpt gpt gpt-35-turbo gpt-4 llm-agent llm-framework llm-inference llmops | ai |
LLM-for-search | llm for search large language models like gpt 4 gpt x etc can be used to create a semantic search system this system intends to improve search relevance and improve user experience this repo covers keyword search embedding search dense retrieval reranking | ai |
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solaris | p align center img src https github com cosmiq solaris raw master static sol logo png width 350 alt solaris p h2 align center an open source ml pipeline for overhead imagery by a href http www cosmiqworks org cosmiq works a h2 p align center img align center src https img shields io pypi pyversions solaris svg alt pypi python version href https pypi org project solaris img align center src https img shields io pypi v solaris svg alt pypi href https pypi org project solaris img align center src https img shields io conda vn conda forge cw eval svg alt conda forge img align center src https travis ci com cosmiq solaris svg branch master alt build img align center src https readthedocs org projects solaris badge alt docs img align center src https img shields io github license cosmiq solaris svg alt license img align center src https img shields io docker build cosmiqworks cw eval svg alt docker a href https codecov io gh cosmiq solaris img align center src https codecov io gh cosmiq solaris branch master graph badge svg a p this is a beta version of solaris which may continue to develop please report any bugs through issues documentation documentation installation instructions installation instructions dependencies dependencies license license this repository provides the source code for the cosmiq works solaris project which provides software tools for tiling large format overhead images and vector labels converting between geospatial raster and vector formats and machine learning compatible formats performing semantic and instance segmentation object detection and related tasks using deep learning models designed specifically for overhead image analysis evaluating performance of deep learning model predictions documentation the full documentation for solaris can be found at https solaris readthedocs io and includes a summary of solaris installation instructions api documentation tutorials for common uses the documentation is still being improved so if a tutorial you need isn t there yet check back soon or post an issue installation instructions coming soon one command installation from conda forge we recommend creating a conda environment with the dependencies defined in environment yml environment yml before installing solaris after cloning the repository cd solaris if you re installing on a system with gpu access conda env create n solaris f environment gpu yml otherwise conda env create n solaris f environment yml finally regardless of your installation environment conda activate solaris pip install pip the package also exists on pypi https pypi org but note that some of the dependencies specifically rtree https github com toblerity rtree and gdal https www gdal org are challenging to install without anaconda we therefore recommend installing at least those dependencies using conda before installing from pypi conda install c conda forge rtree gdal 2 4 1 pip install solaris if you don t want to use conda you can install libspatialindex https libspatialindex org then pip install rtree installing gdal without conda can be very difficult and approaches vary dramatically depending upon the build environment and version but the rasterio install documentation https rasterio readthedocs io en stable installation html provides os specific install instructions simply follow their install instructions replacing pip install rasterio with pip install solaris at the end docker you may also use our docker container docker pull cosmiqworks solaris api documentation see the readthedocs https cw eval readthedocs io page dependencies all dependencies can be found in the requirements file requirements txt requirements txt or environment yml environment yml license see license license txt traffic github https img shields io github downloads cosmiq cw eval total svg pypi https img shields io pypi dm cw eval svg conda https img shields io conda dn conda forge cw eval svg | geospatial machinelearning deeplearning computervision geo gis python | ai |
design-system | asyncapi design system assets github repobanner atom png https www asyncapi com deciduous tree environment setup joystick how to setup storybook locally install all essential prerequisites before launching the storybook environment locally to install the dependencies run the command below cmd npm install now that you ve installed all of the node modules in your project you can launch the storybook environment to get started type the command below cmd npm run storybook by default this will launch your local storybook environment on port 6006 | design-system nodejs storybook | os |
primitives | radix primitives logo primitives png https radix ui com primitives radix primitives an open source ui component library for building high quality accessible design systems and web apps radix primitives is a low level ui component library with a focus on accessibility customization and developer experience you can use these components either as the base layer of your design system or adopt them incrementally documentation for full documentation visit radix ui com docs primitives https radix ui com docs primitives releases for changelog visit radix ui com docs primitives overview releases https radix ui com docs primitives overview releases contributing please follow our contributing guidelines github contributing md authors benoit grelard benoitgrelard https twitter com benoitgrelard workos https workos com jenna smith jjenzz https twitter com jjenzz andy hook andy hook https twitter com andy hook workos https workos com pedro duarte peduarte https twitter com peduarte chance strickland chancethedev https twitter com chancethedev contributors ar nazeh nazeh https github com nazeh fabio capucci cappuc https github com cappuc community pedro duarte peduarte https twitter com peduarte colm tuite colmtuite https twitter com colmtuite workos https workos com discord https discord com invite 7xb99ug to get involved with the radix community ask questions and share tips twitter https twitter com radix ui to receive updates announcements blog posts and general radix tips thanks a href https www chromatic com img src https user images githubusercontent com 321738 84662277 e3db4f80 af1b 11ea 88f5 91d67a5e59f6 png width 153 height 30 alt chromatic a thanks to chromatic https www chromatic com for providing the visual testing platform that helps us review ui changes and catch visual regressions license licensed under the mit license copyright 2022 present workos https workos com see license license for more information | ui ui-components ui-kit design-systems accessibility react component-library radix-ui colors | os |
selenium-cucumber-ruby | welcome to selenium cucumber selenium cucumber automation tesing using ruby selenium cucumber is a behavior driven development bdd approach to write automation test script to test web and android apps it enables you to write and execute automated acceptance unit tests it is cross platform open source and free automate your test cases with minimal coding more details http seleniumcucumber info this repository has been moved to https github com selenium cucumber selenium cucumber ruby | front_end |
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core | ark core p align center img src https raw githubusercontent com arkecosystem core master banner png p license mit https badgen now sh badge license mit green https opensource org licenses mit note please checkout mainsail https github com arkecosystem mainsail for the next generation of the ark core blockchain protocol featuring a new dpos consensus engine that is more reliable and efficient currently in development introduction this repository contains all plugins that make up the ark core check our dedicated documentation site https ark dev docs core for information about all available plugins and how to write a core plugin https ark dev docs core development plugins intro if you want to get started with developing your own plugins documentation development https ark dev docs core installation intro docker https ark dev docs core development docker api documentation api v2 https ark dev docs api security if you discover a security vulnerability within this package please send an e mail to security ark io all security vulnerabilities will be promptly addressed credits this project exists thanks to all the people who contribute contributors license mit license ark ecosystem https ark io | blockchain ark nodejs crypto dpos smartbridge typescript modular-architecture | blockchain |
symphony-2 | symphony 2 0 https symphony iohk io symphony https symphony iohk io static cc3e46f246ec28d72b2311a62590c527 775d9 day view jpg symphony 2 0 is a real time 3d immersive blockchain explorer symphony 2 0 aims to answer the question what does a blockchain look and sound like it is an educational initiative with the aim of inspiring a wide audience about blockchain technology visual structures in symphony 2 0 all of the 3d structures represent real components of a blockchain transactions transactions are represented as hexagonal crystals the height of each crystal is based on the transaction value the brightness of the crystal surface is based on the ratio of unspent to spent transaction outputs see bitcoin utxo https learnmeabitcoin com glossary utxo fully spent transactions appear darker in color than unspent transactions blocks the arrangement of crystals on the block surface and the crystal radii is controlled by a 2d simplex noise function https en wikipedia org wiki simplex noise the noise amplitude is controlled by the ratio of total block transaction fees to total block value we use this metric as a type of network health if the transaction fees are very high compared to the transaction amounts we consider this an unhealthy state an unhealthy state is visually reflected by transactions being more random in radius and spacing merkle trees symphony https symphony iohk io static 6d0dd183b4c83ef4e918b3a3b65ecc00 775d9 block angle jpg merkle trees https en wikipedia org wiki merkle tree are a fundamental component of blockchains and decentralized applications they allow efficient verification of large data sets in bitcoin and other cryptocurrencies merkle trees connect one block to the next they are constructed from hashes of the block transactions in symphony 2 0 we show how the branches of the trees connect to the transactions on the top of the block mempool in symphony 2 0 the mempool https 99bitcoins com bitcoin mempool mempool is visualised as a swirling cloud of particles at the center of the blockchain spiral the total count of particles is controlled by the actual mempool size spent transactions we show transaction outputs being spent via a trail of energy leaving the transaction crystal and flying toward the mempool blockchain the macro blockchain structure is shown as an archimedean spiral https en wikipedia org wiki archimedean spiral this structure is an efficient use of space for showing the entire blockchain on screen sound synthesis symphony 2 0 generates a unique soundscape for each block based on the block data using additive synthesis https en wikipedia org wiki additive synthesis it loops through each of the transactions in a block and converts the transaction values to pitch larger transaction value lower pitch each transaction has a fundamental pitch as well as 7 harmonics https en wikipedia org wiki harmonic it utilises gpu js https github com gpujs gpu js to generate the sound buffers quickly we use the ratio of the transaction fee to value as an indicator of transaction health if a transaction has a very high fee to value ratio we add randomness to the harmonics creating a more sonically inharmonic sound the sound in symphony 2 0 serves a functional purpose you can listen to the sound of a block and ascertain properties about the number of transactions the level of fees in the block and the value being moved around installation bash yarn install yarn start license symphony 2 0 is licensed under the apache 2 0 license md licence | blockchain |
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awesome-iot | awesome iot awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg a curated list of iot everyone can contribute here simliar projects awesome azure iot https github com formulahendry awesome azure iot a curated list of awesome azure internet of things projects and resources awesome android things https github com amitshekhariitbhu awesome android things a curated list of awesome android things tutorials libraries and much more at one place awesome openiot https github com agile iot awesome open iot a curated list of awesome open source iot frameworks libraries and software awesome iot https github com hqarroum awesome iot a curated list of awesome internet of things projects and resources toc framework framework library library sdk sdk arduino arduino low level low level app app storage storage security security os os android things android things voice controller voice controller platform platform iot clouds iot clouds iiot clouds iiot clouds apis apis middleware middleware toolkits include non os toolkits include non os data visualization data visualization hardware hardware home automation home automation ide ide robotics robotics others others language language others others protocol library protocol library mqtt mqtt coap coap spark spark wemo wemo smcp smcp lora lora openthread openthread others others fork fork hardware com hardware com nfc nfc serial serial others others software software tools tools voice voice ai ai resources websites projects resources websites projects course course website website blog blog group group github org github org free book free book related resources projects related resources projects tutorial tutorial edge edge analytics analytics others others framework net core iot 1193 292 https github com dotnet iot a set of libraries to interact with sensors displays and input devices from net core framework this libraries allows to work with the gpio port for various boards like raspberry pi and hummingboard and contains a growing set of community maintained device bindings for iot components areg sdk 15 0 https github com aregtech areg sdk areg sdk is a developer friendly an interface centric real time asynchronous communication engine to enable distributed and mist computing https csrc nist gov publications detail sp 500 325 final where connected things interact and provide services as if they act like thin distributed servers cylon 2339 0 https github com hybridgroup cylon javascript framework for robotics physical computing and the internet of things devify server 53 1 https github com devifyplatform devify server s extremely light weight and is very easy to use it aims to help developers to create iot application servers faster epoc js 27 0 https github com charliegerard epoc js this framework provides an interface to access data from the emotiv epoc brain sensor using node js esp idf 2542 0 https github com espressif esp idf the official framework from espressif to build wi fi ble and bt apps with esp32 foglight 10 100 https github com oci pronghorn foglight is a lightweight runtime that enables makers of all ages and skill levels to create highly performant apps for embedded devices like raspberry pis framboos 75 3 https github com jkransen framboos is a small java wrapper around the default gpio driver on linux boards like raspberry pi and beagleboard freedomotic 208 4 https github com freedomotic freedomotic is an open source flexible secure internet of things iot application framework useful to build and manage modern smart spaces gobot 2062 1 https github com hybridgroup gobot golang framework for robotics physical computing and the internet of things grow iot 7 7 https github com commongarden grow iot is a full javascript based iot stack with a simple api and basic user interface guh 50 29 https github com guh guh is an open source iot internet of things server which allows to control a lot of different devices from many different manufacturers heimcontrol js 1306 4 https github com ni c heimcontrol js home automation with node js and raspberry pi iot 433 mhz 121 11 https github com roccomuso iot 433mhz iot system to control 433 mhz rc power sockets pir door sensors and much more iot edge 264 0 https github com azure iot edge the azure iot gateway sdk was our first step to enabling edge analytics in iot solutions iot sol 38 0 https github com 01org intel iot services orchestration layer the total solution that provides visual graphical programming for developing iot applications iotcloud 2 14 50 https github com iotcloud iotcloud2 an open source framework for iot and sensor centric applications johnny five 6024 0 https github com rwaldron johnny five javascript robotics and iot programming framework developed at bocoup firmata protocol kura 85 1 https github com eclipse kura an open source framework for development of iot applications lelylan 647 1 https github com lelylan lelylan development openssl source lightweight microservices architecture for the internet of things for developers lightweight mqtt machine network 21 1 http lwmqn github io lwmqn is a machine network framework with mqtt see also ipso alliance technical archive http www ipso alliance org ipso community resources technical archive liota 162 2 https github com vmware liota is an open source offering for iot solution developers and resides primarily on iot gateways opendevice 23 8 https github com opendevice opendevice open iot internet of things platform and framework pando cloud 75 2 https github com pandocloud pando cloud is the cloud part of pando iot solution it s made of a bunch of tools protocols and frameworks below pando cloud pando embedded framework pando protocol as so on pingo 211 0 https github com pingo io pingo py generic api for controlling boards with programmable io pins polymcu 84 2 https github com labapart polymcu has been designed from the beginning to be as flexible as possible host os independent support any toolchain any rtos any micro controller vendor sdk rpi gpio js 221 5 https github com jamesbarwell rpi gpio js control raspberry pi gpio pins with node js sensorbee 54 33 https github com sensorbee sensorbee lightweight stream processing engine for iot serverless 7951 0 https github com serverless serverless serverless is the application framework for building web mobile and iot applications exclusively on amazon web services lambda and api gateway simgrid 46 53 https github com simgrid simgrid is a scientific instrument to study the behavior of large scale distributed systems such as grids clouds hpc or p2p systems sming 1800 0 https github com sminghub sming sming is an asynchronous c c framework with superb performance and multiple network features sming is open source and is tailored towards embedded devices thingsboard iot gateway 463 246 https github com thingsboard thingsboard gateway open source iot gateway integrates devices connected to legacy and third party systems with thingsboard iot platform using opc ua and mqtt protocols library sdk armbian build sdk 630 https github com armbian build for creating customized kernel and debian based userspace for popular development boards aws iot arduino y n sdk 63 4 https github com aws aws iot device sdk arduino yun sdk for connecting to aws iot from an arduino y n azure iot gateway sdk 26 2 https github com azure azure iot gateway sdk contains the infrastructure and modules to create iot gateway solutions cylon js for intel iot 29 40 https github com hybridgroup cylon intel iot is a javascript framework for robotics physical computing and the internet of things iot electron 41 16 https github com spark electron the electron is a tiny cellular development kit based around u blox s sara u series 3g or g series 2g cellular modem module and a stm32f205 arm cortex m3 microcontroller esp8266 arduino core 2588 0 https github com esp8266 arduino arduino core for esp8266 wifi chip ez connect lite sdk 67 16 https github com marvell iot aws starter sdk marvell s starter sdk for aws iot service microsoft azure iot sdk 203 1 https github com azure azure iot sdks sdks for a variety of languages and platforms that help connect devices to microsoft azure iot services arduino arduinojson 873 0 https github com bblanchon arduinojson an elegant and efficient json library for embedded systems ino 874 1 https github com amperka ino ino is a command line toolkit for working with arduino hardware pjon 427 4 https github com gioblu pjon digital communication framework for arduino and iot windows remote arduino 98 13 https github com ms iot remote wiring remote arduino wiring interface for windows 8 1 windows phone 8 1 and windows 10 used to control an arduino from a universal windows platform application wiringpi 455 11 https github com wiringpi wiringpi gordon s arduino wiring like wiringpi library for the raspberry pi low level amazon echo bridge 452 1 https github com armzilla amazon echo ha bridge amazon echo bridge allows you to quickly emulate a phillips hue bridge bringing the ability to seamlessly integrate an amazon echo into various home automation systems awot 69 8 https github com lasselukkari awot web server library for arduino teensy esp8266 and esp32 btstack 151 1 https github com bluekitchen btstack dual mode bluetooth stack with small memory footprint cocoamqtt 210 0 https github com emqtt cocoamqtt mqtt for ios and os x written with swift devices 134 0 https github com goiot devices suite of libraries for iot devices written in go fauxmoesp https bitbucket org xoseperez fauxmoesp belkin wemo emulator library for esp8266 inih 312 3 https github com benhoyt inih is a simple ini file parser written in c iot helpers 37 8 https github com dotnettoscana iothelpers a library that allows to easily interact with windows 10 iot core features like gpio i2c and spi devices iotit flashing tool 18 2 https github com xshellinc iotit is an open source command line utility for flashing initializing iot devices krypton 7 35 https github com cesanta krypton embedded tls dtls library source and binary compatible openssl subset ladon 114 2 https github com ory am ladon is a library written in go for access control policies similar to role based access control or access control lists libtuv 19 17 https github com samsung libtuv asynchronous i o for iot js and embedded system libui 8021 1 https github com andlabs libui simple and portable but not inflexible gui library in c that uses the native gui technologies of each platform it supports lk 312 0 https github com littlekernel lk the lk embedded kernel an smp aware kernel designed for small systems magenta 286 0 https github com fuchsia mirror magenta magenta is a new kernel that powers the fuchsia os matrix os 29 12 https github com matrix io matrix os is a platform for running applications on the matrix creator matrixssl 36 0 https github com matrixssl matrixssl is an embedded ssl and tls implementation designed for small footprint iot devices requiring low overhead per connection mcuboot 43 3 https github com runtimeco mcuboot is a secure bootloader for 32 bit mcus nexmon 885 2 https github com seemoo lab nexmon is our c based firmware patching framework for broadcom cypress wifi chips pelion device management client 19 41 https github com armmbed mbed cloud client a library that connects devices to pelion device management service and to mbed enabled cloud services from our partners pingo py 223 15 https github com pingo io pingo py provides a uniform api to program devices like the raspberry pi beaglebone black pcduino etc just like the python dbapi provides an uniform api for database programming in python polymcu 57 3 https github com labapart polymcu an open framework for micro controller software secure device grid 4 20 https github com trifork secure device grid secure device to device communication solution for iot simbody 540 0 https github com simbody simbody high performance c multibody dynamics physics library for simulating articulated biomechanical and mechanical systems like vehicles robots and the human skeleton smartobject 8 2 https github com petereb smartobject a smart object class that helps you with creating ipso smart objects in your js apps see also ipso alliance technical archive http www ipso alliance org ipso community resources technical archive soletta 96 2 https github com solettaproject soletta soletta project is a framework for making iot devices with soletta project s libraries developers can easily write software for devices that control actuators sensors and communicate using standard technologies spiffs 174 0 https github com pellepl spiffs wear leveled spi flash file system for embedded devices susi 13 150 https github com webvariants susi is an application framework to build interfaces for arbitrary systems swiftygpio 407 1 https github com uraimo swiftygpio a swift library to interact with linux gpio spi on arm uip 246 3 https github com adamdunkels uip uip is a very small implementation of the tcp ip stack wifidog 291 1 https github com wifidog wifidog gateway a complete and embeddable captive portal solution for wireless community groups or individuals windows 10 iot core iot helpers 25 0 https github com dot and net iothelpers his library allows to easily interact with gpio i2c and spi devices in windows 10 iot core xfrp 14 2 https github com kuntengrom xfrp frp client for openwrt lede frp is a fast reverse proxy to help you expose a local server behind a nat or firewall to the internet xipki 34 10 https github com xipki xipki extensible simple public key infrastructure consists of ca and ocsp responder xkcptun 104 25 https github com liudf0716 xkcptun xkcptun is kcp tunnel for openwrt lede implemented in c language app cordova ble 149 3 https github com evothings cordova ble bluetooth low energy plugin for cordova cordova mqtt plugin 24 11 https github com arcoirislabs cordova plugin mqtt mqtt cordova plugin for apache cordova iot espressif android 46 1 https github com espressifapp iot espressif android is used to control esp8266 device by android pad or phone mqttx 12 0 https github com emqx mqttx mqttx is a cross platform mqtt desktop client open sourced by emq which supports macos linux and windows it allows users to quickly and easily test mqtt mqtts connections publish and subscribe to mqtt messages phonegap nfc 312 2 https github com chariotsolutions phonegap nfc phonegap nfc plugin pwaify 269 8 https github com vladikoff pwaify experimental project to convert your pwa progressive web app into a cross platform electron app brings pwas to your desktop summon 7 52 https github com lab11 summon a platform for mobile devices that provides a convenient and scalable mechanism for iot device interactivity enabled by web based interfaces and driven by the devices themselves storage hypergolix 72 11 https github com muterra py hypergolix is programmable cloud sync like dropbox but you integrate it into your applications instead of using it from the filesystem iotdl 9 8 https github com dpjanes iotdb iotql an sql like language for the iot node iotdb 19 61 https github com dpjanes node iotdb easily control the internet of things using semantics hstreamdb 172 https github com hstreamdb hstream the streaming database built for iot data storage and real time processing security iotseeker https github com rapid7 iotseeker this scanner will scan a network for specific types of iot devices to detect if they are using the default factory set credentials nshield 66 35 https github com fnzv nshield an easy and simple anti ddos solution for vps dedicated servers and iot devices based on iptables scanners box 424 0 https github com we5ter scanners box the toolbox of open source scanners trezor crypto 94 1 https github com trezor trezor crypto heavily optimized cryptography algorithms for embedded devices os mynewt https github com apache mynewt core is an open source operating system for tiny embedded devices its goal is to make it easy to develop applications for microcontroller environments where power and cost are driving factors amazon freertos 842 0 https github com aws amazon freertos is an operating system for microcontrollers that makes small low power edge devices easy to program deploy secure connect and manage arm mbed 629 0 https github com mbedmicro mbed the arm mbed iot device platform provides the operating system cloud services tools and developer ecosystem to make the creation and deployment of commercial standards based iot solutions possible at scale armbian https www armbian com debian based docker enabled lightweight linux for popular development boards optimised for embedded usage brillo https developers google com brillo brillo extends the android platform to all your connected devices contiki 1813 0 https github com contiki os contiki the open source os for the internet of things f9 kernel 316 4 https github com f9micro f9 kernel an efficient and secure microkernel built for arm cortex m cores inspired by l4 flingos 176 0 https github com flingos flingos an educational operating system written in c a great stepping stone from high to low level development huawei liteos 341 3 https github com liteos liteos kernel huawei liteos kernel hypriot 253 62 https github com hypriot image builder rpi hypriotos for the raspberry pi is a debian based container os optimized for docker janos 138 0 https github com jan os janos janos is an operating system designed to run on the chipset of mobile phones linino 83 13 https github com arduino linino linino is a gnu linux distribution based on openwrt and maintained by doghunter lua rtos esp32 131 2 https github com whitecatboard lua rtos esp32 is a real time operating system designed to run on embedded systems with minimal requirements of flash and ram memory macchina io 144 0 https github com macchina io macchina io an open source toolkit for building embedded iot applications that connect sensors devices and cloud services nodeos 3605 0 https github com nodeos nodeos lightweight operating system using node js as userspace nuttx http nuttx org is a real time operating system rtos with an emphasis on standards compliance and small footprint scalable from 8 bit to 32 bit microcontroller environments the primary governing standards in nuttx are posix and ansi standards openwrt 230 173 https github com openwrt openwrt openwrt is described as a linux distribution for embedded devices pikort 164 5 https github com piko rt pikort a tiny linux like real time kernel optimized for arm cortex m chips raspbian http raspbian org raspbian is a free operating system based on debian optimized for the raspberry pi hardware riot 748 1 https github com riot os riot the friendly operating system for the internet of things rmp 19 8 https github com edi systems m5p1 muprokaron a single file rapid development rtos for iot with integrated graphics rt thread 493 0 https github com rt thread rt thread rt thread is an open source real time operating system for embedded devices from china sel4 microkernel 1344 1 https github com sel4 sel4 the world s first operating system kernel with an end to end proof of implementation correctness and security enforcement is available as open source silk 74 2 https github com silklabs silk is a free as in free beer firmware for a number of smartphones based on the open source android operating system with a nodejs layer on top of it that makes it possible to write programs and get access to hardware aspects using only simple javascript snappy ubuntu core http developer ubuntu com en snappy canonical a new transactionally updated ubuntu for clouds and devices tachyos 7 82 https github com fritzprix tachyos is the rtos based on microkernel architecture which includes only minimal components like thread synchronization memory management inter thread communication while supporting execution context address space isolation protection and extensible modular interface tinyara 35 1 https github com samsung tinyara is a lightweight rtos based platform to support low end iot devices tinyos 543 0 https github com tinyos tinyos main designed for low power wireless devices such as those used in sensor networks ubiquitous computing personal area networks smart buildings and smart meters tock os 243 1 https github com helena project tock is an operating system designed for running multiple concurrent mutually distrustful applications on cortex m based embedded platforms trochili 75 6 https github com liuxuming trochili a small rtos optimized for the embedded iot devices support cortex m3 zephyr 352 5 https github com zephyrproject rtos zephyr is a small scalable real time operating system for use on resource constrained systems supporting multiple architectures android things android things user space drivers 140 2 https github com androidthings contrib drivers sample peripheral drivers for android things voice controller alexa rubykit 109 6 https github com damianfc alexa rubykit implements a quick back end service for deploying applications for amazon s echo alexa alexapi 17 1 https github com sammachin alexapi turn a raspberry pi into an alexa client flask ask 946 0 https github com johnwheeler flask ask is a flask extension that makes building alexa skills for the amazon echo easier and much more fun platform thing it node 20 3 https github com marcgille thing it node a device independent iot platform including support of complex event processing storyboards and a mobile app actorcloud 32 0 https github com actorcloud actorcloud actorcloud is an iot platform that provides one stop platform services for enterprises with low power iot networks it provides multiple protocol access message flow management data parsing and data processing capabilities for devices on a secure and reliable basis astarte 19 0 https github com astarte platform astarte astarte is an open source iot platform written in elixir it is a turnkey solution which packs in everything you need for connecting a device fleet to a set of remote applications it performs data modeling automated data reduction real time events and provides you with any feature you might expect in a modern iot platform right now linux and esp32 devices are supported out of the box using the provided sdks blynk 716 0 https github com blynkkk blynk server is a platform with ios and android apps to control arduino esp8266 raspberry pi and the likes over the internet clavin 212 2 https github com ericssonresearch calvin base calvin is an application environment that lets things talk to things it comprises of both a development framework for application developers and a runtime environment that handles the running application devicehive https github com devicehive iot data platform wide range of connectivity options device management security and data processing embarc open software platform osp 23 9 https github com foss for synopsys dwc arc processors embarc osp is a software distribution aimed at facilitating the development of embedded systems based on arcv2 processors flowchain app 22 50 https github com flowchain flowchain app a flowchain plugin that provides the flow based programming fbp engine grovepi 330 0 https github com dexterind grovepi is an open source platform for connecting grove sensors to the raspberry pi hivemq 329 0 https github com hivemq hivemq community edition is an open source mqtt platform and mqtt broker hologram https hologram io open source full stack platform with standalone devices and usb plug in offers a free developer tier iot js 921 0 https github com samsung iotjs platform for internet of things with javascript iotgo 173 0 https github com itead iotgo is an open source iot platform like wordpress zencart and all other open source software you can deploy your own iotgo cloud service jasper http jasperproject github io jasper is an open source platform for developing always on voice controlled applications kerberos io web 176 16 https github com kerberos io web a gui to configure the machinery and to view events that were detected by the machinery kitnic 124 0 https github com monostable kitnic a registry for ready to build open hardware electronics projects lan 105 0 https github com phodal lan internet of things server layer with coap websocket mqtt http f mainflux 33 3 https github com mainflux mainflux mainflux is an open source and patent free iot cloud platform based on microservices mobius 46 2 https github com iotketi mobius is the open source iot server platform based on the onem2m standard mongoose iot 487 0 https github com cesanta iot is a full stack iot platform including firmware and cloud components available for esp8266 nebula http nebula readthedocs io a docker orchestrator designed to manage iot devices pagenodes 99 0 https github com monteslu pagenodes completely browser based iot platform a chrome progressive web app particle spark http spark github io particle formally spark is a complete open source full stack solution for cloud connected devices pharothings 37 29 https github com pharo iot pharothings is a live programming platform for iot projects based on pharo platformio 980 0 https github com platformio platformio platformio is a cross platform code builder and the missing library manager siemens mindsphere https www siemens com global en home products software mindsphere html open cloud based iot operating system uses opc ua as communication standard from siemens which is extensible with services thingengine 3 0 https github com stanford mobisocial iot lab thingengine core an open source platform for iot rules that you can execute anywhere you want thingsboard 5102 1700 https github com thingsboard thingsboard open source iot platform device management data collection processing and visualization toit 961 67 https toit io the toit platform combines the functionality of serving your devices in a robust resilient way and letting you have control over your devices and your data as well as ready to use over the air firmware and application updates on your network connected embedded devices united manufacturing hub 9 0 https github com united manufacturing hub united manufacturing hub the open source manufacturing app platform combines various open source solutions and packages them in a helm chart for example nodered vernemq and timescaledb iot clouds agile iot platform https www aylanetworks com products iot platform ayla networks iot platform with cloud services alibabacloud https intl aliyun com solutions iot a cloud computing solution arm pelion https www pelion com arm pelion iot platform including connectivity device and data management service artik cloud https artik cloud samsung cloud for the iot aws iot https aws amazon com iot amazon cloud for the iot azure iot hub https azure microsoft com en us services iot hub microsoft cloud for the iot bosch iot cloud https www bosch si com products bosch iot suite iot cloud bosch iot cloud 2 html highly scalable cloud infrastructure based on cloud foundry cloudplugs iot https cloudplugs com an end to end fog computing platform for iot exosite murano https exosite com platform iot platform by exosite google cloud iot https cloud google com solutions iot google cloud platform iot solutions huawei cloud iotda https www huaweicloud com product iothub html huawei cloud for the iot ibm watson http www ibm com watson ibm cloud for the iot oracle iot cloud https cloud oracle com iot oracle cloud for the internet of things rightech iot cloud https app rightech io iot platform salesforce iot cloud http www salesforce com iot cloud salesforce cloud for the internet of things sap hana https www sap com products iot platform cloud html sap cloud for the internet of things siemens mindsphere http www siemens com global en home company topic areas digitalization mindsphere html open iot ecosystem as paas xively iot cloud https www xively com iot platform yaler https yaler net relay infrastructure for secure access to embedded systems zatar http www zatar com zatar is the first armmbed standards based iot cloud service emqx cloud https www emqx com en cloud fully managed mqtt service for iot connect your iot devices to any cloud without the burden of maintaining infrastructure iotsharp https github com iotsharp iotsharp iotsharp is an open source iot platform iiot clouds dataxchange https www scytec com cloud manufacturing devicewise for factory http www telit com solutions industries smart manufacturing telit iiot cloud predix https www predix com industrial iot cloud by general electric space time insight iiot http www spacetimeinsight com solutions internet of things industrial iot cloud formerly go factory com thingworx https www thingworx com platforms industrial connectivity industrial iot cloud voice of the machine http www parker com portal site parker menuitem 17c8315d31f057bc86a6c3544256d1ca vgnextoid 244744e25684b510vgnvcm100000e6651dacrcrd vgnextchannel 9f45216358d55510vgnvcm100000e6651dacrcrd vgnextfmt industrial iot cloud by parker hannifin based on exosite apis ogc sensorthings api 21 15 https github com opengeospatial sensorthings the ogc sensorthings api is an ogc standard specification for providing an open and unified way to interconnect iot devices data and applications over the web qeo tinq 6 392 https github com brunodebus tinq core tinq is completely based on the qeo publish subscribe framework produced by technicolor as explained in the license section middleware kaa 234 0 https github com kaaproject kaa kaa open source middleware platform for building managing and integrating connected products with the internet of everything kuzzle 502 0 https github com kuzzleio kuzzle an open source backend with advanced features like real time pub sub or geofencing and a multiprotocol interface that supports mqtt lorawan and more website https kuzzle io solutions technologies iot backend meact 6 43 https github com bkupidura meact task is to get metric from external stuff write it to and perform various action openiot 205 0 https github com openiotorg openiot the openiot middleware infrastructure will support flexible configuration and deployment of algorithms for collection sitewhere 61 0 https github com sitewhere sitewhere sitewhere open source iot platform for device connectivity management data persistence processing integration and analytics both in cloud and on premise t6 21 4 https github com mathcoll t6 data first iot platform to connect physical objects with time series db and perform data analysis thingspeak 743 0 https github com iobridge thingspeak thingspeak is an open source internet of things application and api to store and retrieve data from things using http over the internet or via a local area network shifu 312 0 https github com edgenesis shifu shifu is a kubernetes native iot development framework that supports multi protocol device access toolkits include non os layered architecture of jtag interface and tap support iot toolkit 39 41 https github com connectiot iottoolkit reference implementation of the smart object api iot adk addonkit 8 1 https github com ms iot iot adk addonkit contains command line scripts for package creation and image creation process and samples for iot products based on rpi2 mbm kinomajs 293 0 https github com kinoma kinomajs a javascript runtime optimized for the applications that power iot devices macchina io 144 0 https github com macchina io macchina io an open source toolkit for building embedded iot applications that connect sensors devices and cloud services openocd 10 34 https github com arduino openocd openocd provides on chip programming and debugging support with a layered architecture of jtag interface and tap support pyocd 112 0 https github com mbedmicro pyocd open source python library for programming and debugging arm cortex m microcontrollers using cmsis dap renode 81 0 https github com renode renode a virtual development tool for multinode embedded networks data visualization arbela 12 2 https github com walkingtree arbela rich extensible customizable and configurable dashboard crouton 75 0 https github com edfungus crouton is a dashboard that lets you visualize and control your iot devices with minimal setup d3 js 49188 0 https github com mbostock d3 a javascript visualization library for html and svg dashing 10067 0 https github com shopify dashing dashing is a sinatra based framework that lets you build beautiful dashboards devicepilot https www devicepilot com operational analytics for connected devices includes free forever tier echarts 11457 0 https github com ecomfe echarts echarts is a commercial charting solution originally intended to address the report need of the company s various business systems freeboard 3034 0 https github com freeboard freeboard a damn sexy open source real time dashboard builder for iot and other web mashups a free open source alternative to geckoboard highcharts 4949 0 https github com highslide software highcharts com highcharts js the javascript charting framework iotdashboard 7 14 https github com electrocoder iotdashboard fast django server for iot devices shelloid 20 1 https github com shelloid shelloid is an open source iot ready real time big data web application platform built using node js and clojure hardware apixel 8 31 https github com aprilbrother apixel apixel is a combination of a esp8266 dev board with a ws2812b addressable rgb led all in one arduino http www arduino cc open source electronics platform based on easy to use hardware and software arduino zero https www arduino cc en main arduinoboardzero this board aims to provide a platform for innovative projects in smart iot devices wearable technology high tech automation crazy robotics and much more beaglebone http beagleboard org getting started beaglebone black is a low cost community supported development platform for developers and hobbyists bitsy bits 3 36 https github com bitsybits bitsybits core is an iot composite project this means it has all parts to implement the full user experience carloop 6 0 https github com carloop carloop library make apps for your car using signals from obd ii can and gps publish data online using the particle platform cheapduino http www dfrobot com wiki index php cheapduino sku dfr0236 cheapduino is the most cheapest arduino compatible processor in the world esp8266 smartwatch 39 0 https github com jeija esp8266 smartwatch esp8266 diy wifi smartwatch with mpu 9250 rtc oled ft232 intel galileo http www arduino cc en arduinocertified intelgalileo galileo is a microcontroller board based on the intel quark soc x1000 application processor a 32 bit intel pentium class system on a chip microduino https www microduino cc microduino is about the size of a quarter and less than half the size of the original arduino board nodemcu http www nodemcu com a firmware based on esp8266 wifi soc powerduino 53 102 https github com dekunukem powerduino a fully programmable power strip with energy monitoring and wireless connectivity pulpino 201 0 https github com pulp platform pulpino pulpino is an open source microcontroller system based on a small 32 bit risc v core developed at eth zurich raspberry pi https www raspberrypi org a tiny and affordable computer that you can use to learn programming through fun practical projects squarewear http rayshobby net sqrwear an open source arduino based wearable microcontroller tessel https tessel io tessel is a completely open source and community driven iot and robotics development platform wemos http www wemos cc very cheap firmware based on esp8266 wifi soc widora 15 21 https github com widora openwrt widora widora is open source wifi development hardware prototype with sound card based on mt7688a running openwrt https github com openwrt openwrt home automation ck homeautomation 15 9 https github com chkr1011 ck homeautomation the first open source home automation sdk for windows 10 iot core eclipse smarthome http eclipse org smarthome smart home adoption will only gain momentum if the different devices can be connected into over arching use cases but currently the market for smart home systems and iot gadgets is heavily fragmented floorplan for home assistant 949 0 https github com pkozul ha floorplan the home assistant front end provides a great way of viewing and interacting with your entities heimcontrol js 1306 4 https github com ni c heimcontrol js home automation with node js and raspberry pi home assistant 3237 0 https github com balloob home assistant open source home automation platform running on python 3 home pi 145 1 https github com denschu home pi home automation with angularjs and mqtt on a raspberry pi homebridge 3030 0 https github com nfarina homebridge homebridge is a lightweight nodejs server you can run on your home network that emulates the ios homekit api lumos 70 1 https github com jonathanrjpereira lumos aims to change that by pairing with wifi and uses machine learning to adjust the light to match your sleep schedule magic mirror 503 0 https github com microsoftedge magic mirror demo a magic mirror powered by a uwp hosted web app mozilla smart home 4 8 https github com mozilla smarthome iot offers a middle ground between in a box solutions like apple homekit and diy solutions like raspberry pi mycontroller 110 0 https github com mycontroller org mycontroller is automation controller for home office or any place ninja blocks https ninjablocks com smart home controller a computer for the coffee table openhab 2536 0 https github com openhab openhab distro a vendor and technology agnostic open source automation software for your home pimatic 362 0 https github com pimatic pimatic a home automation server and framework for the raspberry pi running on node js privateeyepi http projects privateeyepi com home automation and monitoring projects for raspberry pi razberry http razberry z wave me razberry brings z wave to the raspberry pi platform smart mirror 1181 0 https github com evancohen smart mirror the fairest of them all a diy voice controlled smart mirror with iot integration sonoff homeassistant 336 1 https github com kmanoz sonoff homeassistant is alternative firmware for the brilliant cheap not quality range of sonoff range of esp 8266 based wifi controlled switches v r 31 2 https github com futurice vor is open source software and hardware for turning your open office into an open real time map for finding people open work places and current events node red https github com node red node red node red is a programming tool for wiring together hardware devices apis and online services in new and interesting ways hkontroller https github com hkontrol hkontroller apple homekit controller implemented in go programming language hkmobile https github com hkontrol hkmobile apple homekit controller for android ide angular 2 iot 10 4 https github com urish angular2 iot is an experimental technology that allows you to program physical hardware buttons leds etc using angular 2 deviot 70 1 https github com gepd deviot sublime text plugin for iot development platformio atom ide 108 2 https github com platformio platformio atom ide the next generation integrated development environment for iot stino 1280 1 https github com robot will stino is a sublime text plugin that provides an arduino like environment for editing compiling and uploading sketches wyliodrinstudio 25 2 https github com wyliodrin wyliodrinstudio wyliodrin studio is a chrome based ide for software and hardware development for iot and embedded linux systems robotics airsim 2606 1 https github com microsoft airsim is a simulator for drones and soon other vehicles built on unreal engine artoo 1269 0 https github com hybridgroup artoo ruby framework for robotics and the internet of things hubot 10481 0 https github com github hubot a customizable life embetterment robot others for embedded systems iot in mind aws iot button logger to git 4 2 https github com kachkaev aws iot button logger to git a beginner friendly aws lambda function that logs events from iot devices into a git repository of your choice written in typescript tested with jest compiled with parcel uses azure pipelines for ci cd corto 15 4 https github com cortoproject corto corto is a tested proven architecture for normalizing data from different technologies into one view regardless of location format or datamodel emul8 50 71 https github com emul8 emul8 is an emulator of various embedded systems with emul8 you can develop embedded software entirely in a virtual environment that runs within your pc esp8266 deauther 3806 0 https github com spacehuhn esp8266 deauther allows you to perform a deauth attack with an esp8266 against selected networks fluent bit 90 4 https github com fluent fluent bit is a data collector for linux embedded linux osx and bsd family operating systems kamanja 21 1 https github com ligadata kamanja is an open source continuous decisioning engine that is hardened for enterprise reliability requirements scalable to iot level data volumes and enables low latency use cases node red 2513 0 https github com node red node red a visual tool for wiring the internet of things parlay 8 160 https github com promenadesoftware parlay is software that brings visibility and accessibility to embedded devices redzilla 13 37 https github com muka redzilla is a service which allow to create easily instances of node red remotedebug 17 11 https github com joaolopesf remotedebug a library to remote debug over telnet connection rio 68 0 https github com solidstategroup rio an open source library allowing you to create an internet connected led wall sonoff tasmota 4869 0 https github com arendst sonoff tasmota provide esp8266 based itead sonoff with web mqtt and ota firmware using arduino ide tinyvp 12 48 https github com lyegoshin tinyvp is a very small and lean hypervisor using mips r5 hardware vz option vorto 32 3 https github com eclipse vorto is a toolset that lets you describe devices using a simple language and share these descriptions so called information models in a centralized vorto repository language atomvm 390 0 https github com bettio atomvm atomvm is a tiny portable virtual machine that allows erlang and elixir code to run on microcontrollers with less than 500kb of ram such as the esp32 eliot 76 48 https github com c3d elfe extensible language for everyday and the internet of things elua 393 1 https github com elua elua quickly prototype and develop embedded software applications with the power of lua and run them on a wide range of microcontroller architectures esp basic 144 0 https github com esp8266 basic basic interpreter for the esp8266 jerryscript 1244 0 https github com samsung jerryscript a javascript engine for internet of things luvit 2237 0 https github com luvit luvit node js for the lua inventor micropython 3070 0 https github com micropython micropython micropython is a lean and fast implementation of the python 3 programming language that is optimised to run on a microcontroller szl 100 0 https github com dimkr szl is a tiny embeddable scripting engine inspired by tcl and shell terra 1248 0 https github com zdevito terra is a low level system programming language that is embedded in and meta programmed by the lua programming language toitlang 961 0 https toitlang org is a high level language that s made to have a syntax very close to python as it s built from first principles for microcontrollers it s at least 20x faster than micropython they ve also built a slick ide integration v7 576 0 https github com cesanta v7 v7 is a javascript engine written in c it makes it possible to program internet of things iot devices in javascript pikascript 660 36 https github com pikastech pikascript pikascript is a extremely lightweight python engine that can run with less than 4kb of ram such as stm32g030c8 and stm32f103c8 it is zero dependency zero configuration easy to deploy and expand others esp8266 wifi relay 31 19 https github com jangoe esp8266 wifi relay esp8266 esp12e wifi doppel relay iot unterputz montage m glich schaltaktor k3po 22 9 https github com k3po k3po is a network driver and language agnostic testing tool littled 545 3 https github com graemedouglas littled a relational database for embedded devices and sensors nodes mbed tls 601 0 https github com armmbed mbedtls an open source portable easy to use readable and flexible ssl library mongoose flashing tool 36 7 https github com cesanta mongoose flashing tool mongoose flashing tool also called mft is the mongoose iot platform flashing tool unik 593 0 https github com emc advanced dev unik is a tool for compiling application sources into unikernels lightweight bootable disk images rather than binaries protocol library mqtt aphid 58 4 https github com ibm swift aphid a lightweight mqtt 3 1 1 client written in pure swift 3 arduino mqtt 95 6 https github com 256dpi arduino mqtt mqtt library for arduino based on the eclipse paho projects eclipse paho javascript client 510 1 https github com eclipse paho mqtt javascript the paho javascript client is an mqtt browser based client library written in javascript that uses websockets to connect to an mqtt broker eclipse paho mqtt c client 142 3 https github com eclipse paho mqtt c this code builds libraries which enable applications to connect to an mqtt broker to publish messages and to subscribe to topics and receive published messages emqx 10300 1800 https github com emqx emqx an ultra scalable open source mqtt broker connect 100m iot devices in one single cluster move and process real time iot data with 1m msg s throughput at 1ms latency esp8266 mqtt 440 0 https github com tuanpmt esp mqtt mqtt client library for esp8266 soc espruna https bitbucket org xoseperez espurna firmware for esp8266 based smart switches includes web gui mqtt and aot software updates gleam 50 108 https github com mikespook gleam a operation cluster based on mqtt hivemq https github com hivemq a mqtt broker and mqtt client in java homie for esp8266 115 1 https github com marvinroger homie esp8266 an arduino for esp8266 implementation of homie an mqtt convention for the iot homie server 45 3 https github com marvinroger homie server a web server for homie an mqtt convention for the iot java mqtt client 405 2 https github com fusesource mqtt client a java mqtt client lightmqtt 32 11 https github com pasisalenius lightmqtt is a lightweight mqtt client written in swift m2mqtt 69 11 https github com ppatierno m2mqtt mqtt client library for net and winrt micrott 673 1 https github com unetworking utt is a lightweight and efficient mqtt broker designed to raise the bar for pub sub performance moquette 309 2 https github com andsel moquette java mqtt lightweight broker mosca 1097 0 https github com mcollina mosca mosca is a node js mqtt broker mosquitto 158 0 https github com eclipse mosquitto an open source mqtt v3 1 v3 1 1 broker mqtt explorer https mqtt explorer com tool to visualize your mqtt topics in a topic hierarchy a mqtt swiss army knife mqtt kafka bridge 28 35 https github com jacklund mqttkafkabridge bridge which consumes mqtt messages and republishes them on kafka on the same topic mqtt c 52 2 https github com liambindle mqtt c a portable mqtt c client for embedded systems and pcs alike mqtt js 1359 0 https github com mqttjs mqtt js the mqtt client for node js and the browser neurite 4 5 https github com linkgo neurite a serial to mqtt bridge an easier way to build iot product with esp8266 arduino paho mqtt wxapp 196 0 https github com tennessine paho mqtt wxapp paho mqtt javascript mqtt broker pubsub client 684 0 https github com knolleary pubsubclient a client library for the arduino ethernet shield that provides support for mqtt strong pubsub 97 1 https github com strongloop strong pubsub pubsub for node js browser mobile and iot surgemq 776 1 https github com surgemq surgemq is a high performance mqtt broker and client library that aims to be fully compliant with mqtt 3 1 and 3 1 1 specs vernemq 561 1 https github com erlio vernemq a distributed mqtt message broker wolfssl mqtt 155 14 https github com wolfssl wolfmqtt a c mqtt library that works with wolfssl waterstream https waterstream io mqtt broker leveraging apache kafka as its own storage and distribution engine nanomq https github com nanomq nanomq a light weight and blazing fast mqtt broker for iot edge platform coap californium 36 0 https github com eclipse californium californium is a java implementation of coap for the iot backend and less constrained iot devices coap net 47 4 https github com smeshlink coap net a c implementation of the coap protocol copper 46 14 https github com mkovatsc copper a firefox add on to browse the internet of things go coap 110 8 https github com dustin go coap implementation of coap in go h5 coap 36 26 https github com morkai h5 coap implementation of the constrained application protocol coap client for node js icoap 28 21 https github com stuffrabbit icoap objective c client implementation of coap java coap 3 0 https github com open coap java coap complete coap implementation in java it is a fork with lots of improvements lobaro coap 74 4 https github com lobaro lobaro coap complete coap implementation in c mbed coap 23 11 https github com armmbed java coap makes it easy to integrate a java se enabled device with coap based services like mbed cloud microcoap 259 10 https github com 1248 microcoap a small coap implementation for microcontrollers mqtt client framework 312 1 https github com ckrey mqtt client framework ios osx tvos native objectivec mqtt client framework node coap 176 11 https github com mcollina node coap node coap is a client and server library for coap modeled after the http module python coap 36 5 https github com openwsn berkeley coap a coap python library swiftcoap 22 12 https github com stuffrabbit swiftcoap swift server client implementation of coap txthings 48 3 https github com siskin txthings coap library for twisted framework spark spark protocol 81 14 https github com spark spark protocol node js module for hosting direct encrypted coap socket connections spark server 371 13 https github com spark spark server an api compatible open source server for interacting with devices speaking the spark protocol wemo arduino esp8266 alexa multiple wemo switch 213 0 https github com kakopappa arduino esp8266 alexa multiple wemo switch arduino esp8266 alexa multiple belkin wemo switch emulator arduino esp8266 alexa wemo switch 213 5 https github com kakopappa arduino esp8266 alexa wemo switch amazon alexa wemos switch made with arduino d1 mini fauxmo 430 0 https github com makermusings fauxmo emulated belkin wemo devices that work with the amazon echo homebridge platform wemo 106 24 https github com rudders homebridge platform wemo belkin wemo platform plugin for the awesome homebridge project ouimeaux 319 0 https github com iancmcc ouimeaux open source control for belkin wemo devices wemo js 19 288 https github com thatguydan wemo js this library aims to provide a simple interface to a belkin wemo power sockets wemore 26 10 https github com dhleong wemore a more awesome library for belkin wemo interactions smcp smcp 55 0 https github com darconeous smcp is an experimental coap based machine to machine m2m protocol that is in the early stages of development lora lora gateway bridge 78 0 https github com brocaar lora gateway bridge is a service which abstracts the packet forwarder udp protocol running on most lora gateways into json over mqtt lora server 237 0 https github com brocaar loraserver lora server is an open source lorawan network server lorapi 28 31 https github com hallard loraspi raspberry pi lora gateway node for rfm92 95 96 98 69hcw modules lowcostloragw 161 4 https github com congducpham lowcostloragw low cost lora iot gateway with sx1272 76 raspberry and arduino osgp osgp platform 35 7 https github com osgp platform is an open generic scalable and independent internet of things platform which enables various connected smart objects in the public space to be easily controlled and monitored openthread openthread 1139 2 https github com openthread openthread openthread is an open source implementation of the thread networking protocol openthread border router 64 0 https github com openthread borderrouter an open source border router built to work with openthread others anjay 16 23 https github com avsystem anjay is a c library that aims to be the reference implementation of the oma lightweight machine to machine lwm2m device management protocol libimobiledevice 2294 0 https github com libimobiledevice libimobiledevice a library to communicate with services of apple ios devices using native protocols meq 920 1 https github com teamsaas meq is a real time communication service for connecting online devices oss 7 44 37 https github com mosaic lopow dash7 ap open source stack is an open source implementation of the dash7 alliance protocol for ultra low power wireless sensor communication fork aws iot button 5 4 https github com ianmas aws iot button emulator emulate the aws iot button on a raspberry pi with a simple push button using this c sample hardware com bluetooth bluetoothlinux https github com pureswift bluetoothlinux is a pure swift linux bluetooth stack bluetoothserial 863 0 https github com don bluetoothserial cordova phonegap plugin for serial communication over bluetooth react native bluetooth serial 299 2 https github com rusel1989 react native bluetooth serial react native version of bluetoothserial plugin for both android and ios nfc adafruit nfcshield i2c 110 13 https github com adafruit adafruit nfcshield i2c i2c driver for adafruit s pn532 based nfc shield chrome app nfc library 117 4 https github com googlechrome chrome nfc with this simple library you can build a chrome app that communicates over usb with nfc readers liblogicalaccess 53 17 https github com islog liblogicalaccess c rfid library for windows linux mac for pc sc nfc iso compliant and proprietary hardware libnfc 119 4 https github com nfc tools libnfc platform independent near field communication library nfc tools for java 183 26 https github com grundid nfctools nfctools is a collection of libraries and tools for nfc in java node nfc 41 38 https github com camme node nfc a first try at binding libnfc to node rfidiot 314 6 https github com adamlaurie rfidiot python rfid nfc library tools serial rxtx 67 4 https github com rxtx rxtx a java cross platform wrapper library for the serial port others balena 329 3 https github com resin os balena is a new container engine purpose built for embedded and iot use cases and compatible with docker containers drake 500 0 https github com robotlocomotion drake is a toolbox maintained by the robot locomotion group at the mit computer science and artificial intelligence lab csail ibm messaging https github com ibm messaging community around ibm messaging products iotweb 4 9 https github com sensaura public iotweb a embedded http and websocket server for uwp net 4 5 mender deployment service 8 14 https github com mendersoftware deployments microservice for managing software deployments for iiot devices within mender ecosystem meshblu 738 0 https github com octoblu meshblu machine to machine instant messaging platform for the internet of things python enocean 13 45 https github com kipe enocean a python library for reading and controlling enocean devices react native esp8266 smartconfig 75 5 https github com tuanpmt react native smartconfig a react native module for esp8266 esptouch smart config servo 7821 0 https github com servo servo is a prototype web browser engine written in the rust language shellhub 702 70 https github com shellhub io shellhub centralized ssh for the the edge and cloud computing the things network 67 4 https github com thethingsnetwork ttn the things network is a global open crowdsourced internet of things data network the things network arduino library 82 9 https github com thethingsnetwork arduino device lib is an arduino library for arduino devices like the things uno and node to communicate via the things network wamp protocol 228 1 https github com wamp proto wamp proto the web application messaging protocol the web application messaging protocol software copper 46 14 https github com mkovatsc copper a firefox add on to browse the internet of things processing 2644 0 https github com processing processing processing is a flexible software sketchbook and a language for learning how to code within the context of the visual arts tools paho http www eclipse org paho the paho project provides open source client implementations of mqtt and mqtt sn messaging protocols aimed at new existing and emerging applications for machine to machine m 2 m and internet of things iot smart js 487 0 https github com cesanta smart js embedded javascript engine for c c with networking file database and device interfaces toit 961 67 https toit io the toit platform combines the functionality of serving your devices in a robust resilient way and letting you have control over your devices and your data as well as ready to use over the air firmware and application updates on your network connected embedded devices thingson mqtt bench https github com volkanalkilic thingson mqtt bench thingson mqtt bench is a simple cross platform net core benchmark tool for mqtt brokers it measures the maximum number of messages that can be sent to the broker in a specified amount of time mqtt file uploader https github com volkanalkilic mqtt file uploader mqtt file uploader is a simple cross platform net core application that watches local directories for changes and uploads new or modified files to an mqtt broker voice chelexa 2 25 https github com chelexa chelexa natural voice recognition iot cloud chess solution via the amazon echo platform mycroft https mycroft ai mycroft is the world s first open source voice assistant resources websites projects course advanced penetration testing https www cybrary it course advanced penetration testing free an introduction to programming the internet of things iot specialization https www coursera org specializations iot landing page of 6 courses introduction to the internet of things and embedded systems the arduino platform and c programming interfacing with the arduino the raspberry pi platform and python programming for the raspberry pi interfacing with the raspberry pi programming for the internet of things capstone architecting smart iot devices https www coursera org learn iot architecture free build an intelligent system from embedded to cloud not free https www pluralsight com courses building intelligent system embedded to cloud none cryptography https www cybrary it course cryptography free cyber security graduate certificate http online stanford edu course cyber security graduate certificate courses operating systems and systems programming introduction to computer networking computer and network security bitcoin and crypto currencies introduction to cryptography technology and national security paid introduction to architecting smart iot devices https www coursera org learn iot devices free low level software security attacks and countermeassures https www youtube com watch v zlzkf8fvcsu none penetration testing and ethical hacking https www cybrary it course ethical hacking free secure coding https www cybrary it course secure coding free serverless reference architecture iot backend 134 3 https github com awslabs lambda refarch iotbackend demonstrates how to use aws lambda in conjunction with amazon kinesis amazon dynamodb amazon simple storage service amazon s3 and amazon cloudwatch to build a serverless system for ingesting and processing sensor data social engineering and manipulation https www cybrary it course social engineering free software architecture for the internet of things https www coursera org learn iot software architecture free stanford advanced computer security certificate http computersecurity stanford edu q certificate overview required courses using cryptography correctly writing secure code exploiting and protecting web applications elective courses software security foundations mobile security network security emerging threats defenses paid web application penetration testing https www cybrary it course web application pen testing free web connectivity and security in embedded systems https www coursera org learn iot connectivity security free iot online courses https skillcombo com topic internet of things free free website eclipse iot https iot eclipse org eclipse foundation iot top level project and working group hackaday https hackaday io projects discover get inspired repeat hack things for the better ibm iot http www ibm com developerworks cn iot ibm developerworks for iot infoq iot weekly http www infoq com cn adf weekly iot news open source project hardware instructables tech http www instructables com tag type id category technology explore the biggest how to and diy community where people make and share inspiring entertaining and useful projects recipes and hacks makezine https makezine com diy projects and ideas for makers explore iot https kandi openweaver com explore internet of things a search engine tool to discover find a curated list of popular new iot libraries across all languages top authors trending project kits discussions tutorials learning resources blog arduino create https create arduino cc none http edi wang http edi wang asp net windows 10 iot ibm developerworks iot http www ibm com developerworks cn iot none ibm iot blog https www ibm com blogs internet of things none industrial iot blog https industrial iot com industrial iot industrie 4 0 viewpoints intel iot blog https software intel com zh cn iot home none microsoft iot blog https blogs microsoft com iot none serversuperio http www cnblogs com lsjwq none bosch connectedworld blog https blog bosch si com iot articles from the world of bosch iot for all https www iotforall com high quality iot content resources and news group guokr diy http www guokr com group 27 a chinese diy group github org intel iot devkit libraries https github com intel iot devkit official github repo for intel iot developer kit libraries samples microsoft iot https github com ms iot microsoft iot team the hybrid group https github com hybridgroup the create of cylon js free book design iot 594 0 https github com phodal designiot a ebook to tech your create iot system step by step edge computing technology and applications https www manning com books edge computing technology and applications a book about how to create an edge computing strategy iot firstep 24 9 https github com nladuo iot firstep a ebook to tech your create iot system ipv6 wsn book http github com marcozennaro ipv6 wsn book an easy guide to wireless sensor networks wsn ipv6 and the internet of things iot using the web to build the iot https www manning com books using the web to build the iot a collection of six hand picked chapters that introduce the key technologies and concepts for building the application layer of the iot related resources projects awesome embedded systems 0 29 https github com fkromer awesome embedded systems the website awesome embedded systems org http awesome embedded systems org lists resources about embedded system software and hardware development awesome mqtt 668 0 https github com hobbyquaker awesome mqtt curated list of mqtt related stuff tutorial micro services tutorial iot 20 13 https github com nearform micro services tutorial iot an instructor led microservices workshop unpacking the internet of things https www udemy com unpacking the internet of things learn v4 overview shows use cases to help to identify possible potential for enterprise specific products arduino raspberrypi and mqtt https github com sofianinho training tree master iot builds an end to end iot application that ties together several aspects of the mqtt protocol edge areg sdk 15 0 https github com aregtech areg sdk areg sdk is a developer friendly an interface centric real time asynchronous communication engine to enable distributed and mist computing https csrc nist gov publications detail sp 500 325 final where connected things interact and provide services as if they act like thin distributed servers eden 25 0 https github com lf edge eden cli for edge virtualization engine eve project flogo 207 0 https github com tibcosoftware flogo is an open source framework for iot edge apps integration ai ell 1859 0 https github com microsoft ell allows you to build and deploy machine learned pipelines onto embedded platforms like raspberry pis arduinos micro bits and other microcontrollers libdeep https github com bashrc libdeep a deep learning library for c c machinery 174 0 https github com kerberos io machinery is a low budget video surveillance solution that uses computer vision algorithms to detect changes and that can trigger other devices tensorflow for raspberry pi 317 0 https github com samjabrahams tensorflow on raspberry pi step by step instructions for installing tensorflow from source using bazel which is also compiled from scratch as well as pre built tensorflow binaries analytics bistro 321 0 https github com asavinov bistro light weight batch and stream analytics engine which radically changes the way data is processed bistro relies on a novel column oriented data model and is intended for iot applications and data processing at the edge netdata 18973 0 https github com firehol netdata is a system for distributed real time performance and health monitoring piwik 5374 0 https github com piwik piwik piwik is the leading free libre open analytics platform samsara 64 1 https github com samsara samsara is a real time analytics platform digital twins eclipse ditto https github com eclipse ditto is the open source project of eclipse iot that provides a ready to use functionality to manage the state of digital twins others connectthedots 307 0 https github com azure connectthedots connect tiny devices to microsoft azure services to build iot solutions django th 275 0 https github com foxmask django th take the control of your data with this opensource clone of ifttt a bridge between your internet services souliss 137 8 https github com souliss souliss arduino based distributed networking framework for smart homes and iot contributing your contributions are always welcome please submit a pull request or create an issue to add a new framework library or software to the list do not submit a project that hasn t been updated in the past 6 months or is not awesome | iot iot-cloud iot-device azure-iot iot-platform iot-application awesome-iot awesome-list | server |
Hillary-Norgay-GPS | introduction build status https travis ci org dannyp11 han gps svg branch master https travis ci org dannyp11 han gps presentation slides https drive google com file d 0b5qknykly6kwuwltwnhmtmnbmtq view usp sharing hillary norgay gps device is a portable device that supports tracking nearest devices and provides functionality that alerts other devices in its range if it figures out that other deivces are too far or user presses panic button the project is inspired from the lack of cellphone signal dependency in smartphone in the wilderness the high price of already existing gps devices current prototype uses atmega328p ucontroller software serial gps i2c 4x20 lcd i2c compass serial xbee module photocell ambiance light sensor buttons etc hardware atmega328p xbee with adapter i2c 4x20 lcd screen i2c magnetometer gy 85 photocell ambient light sensor 4x buttons 8x leds 3 to 8 shift register rs232 debugger software chibios as rtos note provided a custom software serial implementaion that is good for b9600 full duplex and 4800 rx tx only ui thread xbee parser thread intensive calulation thread avr gcc authors dat pham datpham usc edu kevin bastoul bastoul usc edu rongcui dong rongcuid outlook com how to build use a unix like system have gnumake avr gcc and avrdude installed windows might need some love but command line would probably work depending on the board modify board chosen in src makefile cd main make make program flash the device question please email one of the authors for bug fixing improvement request | os |
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Hotel-Management-System | hotel management system information technology project 2018 b by team 404 b br html5 javascript java css sass | server |
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MLI | mli an api for distributed machine learning note as of 2016 this project is no longer under active development many of its ideas have been incorporated into apache spark mllib http spark apache org mllib and keystoneml http keystone ml org this repository remains available for historical purposes | ai |
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Programming_Computer_Vision_with_Python | about pcv pcv is a pure python library for computer vision based on the book programming computer vision with python by jan erik solem more details on the book and a pdf version of the latest draft can be found at programmingcomputervision com http programmingcomputervision com dependencies you need to have python 2 6 and as a minimum numpy http numpy scipy org matplotlib http matplotlib sourceforge net some parts use scipy http scipy org many sections show applications that require smaller specialized python modules see the book or the individual examples for full list of these dependencies structure pcv the code pcv book contains a clean folder with the code exactly as used in the book at time of publication examples contains sample code some examples use data available at programmingcomputervision com http programmingcomputervision com installation open a terminal in the pcv directory and run with sudo if needed on your system python setup py install now you should be able to do import pcv in your python session or script try one of the sample code examples to check that the installation works license all code in this project is provided as open source under the bsd license 2 clause simplified bsd license see license txt jan erik solem | ai |
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SoC-RTOS | soc rtos queenfield queenfield master icon jpg operating system for a system on chip | os |
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sandbox | sandbox this is where i place all my mini projects and exercises that only require a front end todo app https taniarascia github io sandbox todo based on watch and code by gordie zhu dom manipulation https taniarascia github io sandbox dom playing around with dom manipulation and vanilla js color changer https taniarascia github io sandbox colors making a little color theme picker dave https taniarascia github io sandbox dave i don t remember why this is here but it seems important portfolio https taniarascia github io sandbox portfolio web developer resume portfolio example ghibli https taniarascia github io sandbox ghibli studio ghibli web app for learning how to connect to an api tab https taniarascia github io sandbox tab new tab app for learning how to use local storage | javascript projects webapp portfolio app | front_end |
website | manish software best custom mobile app design amp development service company manish software is the best affordable custom mobile app design amp development company providing iphone app development android app development multi platform mobile apps ipad application development custom software development flutter app development and firebase app development service mobile application development https manishsoftware com top website design amp development company manish software is the top mobile responsive website design amp development company providing commercial web design services for corporates and small businesses web design https manishsoftware com web design | front_end |
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Navilos | navilos step by step rtos for study embedded fw programming rtos | rtos arm embedded | os |
drl4nlp.scratchpad | policy gradients https github com ganeshjawahar drl4nlp scratchpad tree master policy grad buck arxiv17 https github com ganeshjawahar drl4nlp scratchpad tree master policy grad buck arxiv17 txt ask the right questions active question reformulation with reinforcement learning arxiv https arxiv org abs 1705 07830 dhingra acl17 https github com ganeshjawahar drl4nlp scratchpad tree master policy grad dhingra acl17 txt towards end to end reinforcement learning of dialogue agents for information access arxiv https arxiv org abs 1609 00777 code https github com miulab kb infobot paulus arxiv17 https github com ganeshjawahar drl4nlp scratchpad tree master policy grad paulus arxiv17 txt a deep reinforced model for abstractive summarization arxiv https arxiv org abs 1705 04304 nogueira arxiv17 https github com ganeshjawahar drl4nlp scratchpad tree master policy grad nogueira arxiv17 txt task oriented query reformulation with reinforcement learning arxiv https arxiv org abs 1704 04572 code https github com nyu dl queryreformulator li iclr17 https github com ganeshjawahar drl4nlp scratchpad tree master policy grad li iclr17 txt dialog learning with human in the loop arxiv https arxiv org abs 1611 09823 code https github com facebook memnn tree master hitl li iclr17 2 https github com ganeshjawahar drl4nlp scratchpad tree master policy grad li iclr17 2 txt learning through dialogue interactions by asking questions arxiv https arxiv org abs 1612 04936 code https github com facebook memnn tree master askingquestions yogatama iclr17 https github com ganeshjawahar drl4nlp scratchpad tree master policy grad yogatama iclr17 txt learning to compose words into sentences with reinforcement learning arxiv https arxiv org abs 1611 09100 dinu nips16w https github com ganeshjawahar drl4nlp scratchpad tree master policy grad dinu nips16w txt reinforcement learning for transition based mention detection arxiv https arxiv org abs 1703 04489 clark emnlp16 https github com ganeshjawahar drl4nlp scratchpad tree master policy grad clark emnlp16 txt deep reinforcement learning for mention ranking coreference models arxiv https arxiv org abs 1609 08667 code https github com clarkkev deep coref value function https github com ganeshjawahar drl4nlp scratchpad tree master value function narasimhan emnlp16 https github com ganeshjawahar drl4nlp scratchpad tree master value function narasimhan emnlp16 txt improving information extraction by acquiring external evidence with reinforcement learning arxiv http arxiv org abs 1603 07954 code https github com karthikncode deeprl informationextraction misc https github com ganeshjawahar drl4nlp scratchpad tree master misc bordes iclr17 https github com ganeshjawahar drl4nlp scratchpad tree master misc bordes iclr17 txt learning end to end goal oriented dialog arxiv https arxiv org abs 1605 07683 weston nips16 https github com ganeshjawahar drl4nlp scratchpad tree master misc weston nips16 txt dialog based language learning arxiv https arxiv org abs 1604 06045 code https github com facebook memnn tree master dbll nogueira nips16 https github com ganeshjawahar drl4nlp scratchpad tree master misc nogueira nips16 txt end to end goal driven web navigation arxiv https arxiv org abs 1602 02261 code https github com facebook memnn tree master dbll | deep-reinforcement-learning natural-language-processing information-retrieval neural-networks | ai |
thumbprint-ios | build status https github com thumbtack thumbprint ios actions workflows ci yml badge svg branch main https github com thumbtack thumbprint ios actions workflows ci yml license https img shields io github license thumbtack thumbprint ios color important https github com thumbtack thumbprint ios blob main license thumbprint ios header github thumbprint header png thumbprint thumbprint is the design system at thumbtack though its primary purpose to support thumbtack projects we have open sourced it for those interested in how we build and manage our documentation and code documentation thumbprint is documented at thumbprint design https thumbprint design playground the thumbprint repo includes a target named playground and it s a sandbox app where those interested can test out the different thumbprint components it can be a great place to get a sense for the components before using them components screenshot screenshots components png components screenshot calendar picker screenshot screenshots calendarpicker png calendar picker screenshot contributing thumbprint accepts issues and pull requests take at look at our contribution guidelines https thumbprint design overview contributing if you d like to contribute we also maintain a contributing md contributing md file that contains developer specific instructions license thumbprint is licensed under the terms of the apache license 2 0 license assets cat photo in thumbprint targets playground assets assets xcassets from unsplash https unsplash com photographed by manja vitolic license https unsplash com license assets sources thumbprint resources assets xcassets from feathericons https feathericons com license https github com feathericons feather blob master license | os |
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MySQL-Database-Systems-Basic | mysql database systems basic university of jaffna faculty of engineering ec5070 database systems lab 1 exercises 1 create the below tables with constraints i employee fname minit lname bdate address sex salary super ssn dno ssn ii department dname dnumber mgr ssn mgr start date iii dept locations dnumber dlocation iv works on essn pno hours v dependent essn dependent name sex bdate relationship vi project pname pnumber plocation dnum 2 insert the values to the department dependent dept locations works on and project tables the values of the employee s first names should be have your friend s names who are sitting near of you at the practical time you should insert exact 4 records 3 check the all insert statements ran successfully 4 generate er diagram for this above scenario 5 write sql queries for the below questions a find the all details for the employee who has a particular ssn number in the employee table b find the first name and address of the employee who receive a salary of more than 30 000 c find the department names and its location details d find the department location for the particular employee e find the managers names of the department f find the relationship of the employees with their dependents names g find the first name and address of the employee who got the salary between 25 000 and 50 000 h find the department name and start date for department in a particular location i find the department name and department number for particular project j find the all details for the female employees and her department details | server |
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example-app-nodejs-backend-react-frontend | example app nodejs backend react frontend this is an example app created for the blog post what is a good directory structure for a monorepo with a node js back end and react front end https simonplend com what is a good directory structure for a monorepo with a node js back end and react front end requirements node js v12 application structure client directory react front end code server directory node js back end code static directory compiled front end assets created by webpack when you run the command npm run build the node js back end serves serves these assets using the express static https expressjs com en starter static files html serving static files in express middleware usage bash install dependencies for front end and back end npm install build front end assets with webpack npm run build run node js back end server npm start load up http localhost 3000 in your browser to view the example website libraries and frameworks used express https expressjs com fast unopinionated minimalist web framework for node js react https reactjs org a javascript library for building user interfaces webpack https www npmjs com package webpack a popular tool for building front end assets e g css and javascript sucrase https www npmjs com package sucrase a simpler and faster alternative to babel https babeljs io which brings support for jsx typescript es modules and more to your client side and server side javascript | front_end |
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iOS-Reverse-Engineering-presentation | ios reverse engineering presentation overview ios reverse engineering presentation introduce the knowledge and tool chain of reverse engineering of ios which covering the basic use of reverse engineering of ios overview https github com aozhimin ios reverse engineering presentation blob master images overview jpeg usage clone the repo and open the index html to show the slides or click github io link resources class dump https github com nygard class dump theos https github com theos theos iosopendev https github com kokoabim iosopendev ida https www hex rays com products ida hopper https www hopperapp com machoview https github com gdbinit machoview dumpdecrypted https github com stefanesser dumpdecrypted clutch https github com kjcracks clutch cycript http www cycript org lldb commands https objccn io issue 19 2 chisel https github com facebook chisel iosre http iosre com workaround when you start ios reverse engineering you should install many tools you may encounter a variety of problems here is a list of possible workarounds the solution to the failure of the iosopendev installation https www ianisme com ios 2319 html the solution to the failure of make package https blog sunnyyoung net post ni xiang 2017 01 11 theosan zhuang yu pei zhi when you execute make package command to package you may encounter a failure situation wrong prompt is dpkg deb warning deprecated compression type lzma use xz instead you should change value of theos platform dpkg deb compression to xz author alex ao aozhimin0811 gmail com license ios reverse engineering presentation is released under the mit license see license for details | ios reverse-engineering presentation introduction low-level | os |
tinycore-editor | how to start with this project please check from here the instructions howto txt raw https raw githubusercontent com robang74 tinycore editor main howto txt howto txt blob https github com robang74 tinycore editor blob main howto txt references author roberto a foglietta roberto foglietta gmail com repository https github com robang74 tinycore editor forum https github com robang74 tinycore editor discussions google group https groups google com g tinycore editor usefull links to visit rufus https github com pbatard rufus releases download v3 14 rufus 3 14 exe tc forum http forum tinycorelinux net index php topic 18682 0 tc corebook http tinycorelinux net corebook pdf tc linux http tinycorelinux net rationale this project is a proof of concept of an embedded system building suite based on tinycore linux distribution it is broken by design to produce a non certifiable system even under the mildest standards its development history is quite amusing and inspired by a real life human case development model first of all it needs to explain the development model of this project because it is particularly educating about how to not deal with it projects and about how to not manage it senior people born by asking for an emergency immediately available single use workaround it successively grew with a long series of small features addition out of any project planning and without any architecture design or redesign in other words in adding a new feature as little redesign as possible has been done we can call this kind of development model as monkey coding design blind features addition which is quite amusing because it explains how evolution works but in this specific case just the bare minimum non competitive natural selection process like has been implemented if it works then does not change it despite all these limitations in the developing model the result is still interesting in many aspects and also some good ones history case in the past i worked with a skillful system architect with a deep understanding of the system on which he was working but with a surprisingly monkey coding attitude quickly i realized that it was the environment and not the man being negative it is irrelevant to say anything else about that experience years later that experience the monkey coding design blind features addition development model gets real moreover a new motto had a birth you get what you ask pay for is the new wysiwyg to the imperishable memory of all my colleagues who decided to live stressless as paid monkey coders to devote themselves to the things that are important for their own life and nothing else teaching tool despite everything above this is a great teaching tool because its redesign implies being able to deal with legacy systems trust me when i say that there are a lot of legacy systems out there in the real world that requires a progressive redesign because the companies which rely on them are not willing to invest in a brand new alternative from this point of view it is an opportunity to learn skills for a real world specific market segment scrum agile development vs scrum agile redesign both are progressive and deliverable based ways of working and continuously bring value the main difference is the starting point finally we learned that things can work despite an ill or none at all design lessons learned for those who are more interested in learning some theory than dirtying their hands deep into the code my scrum in a nutshell https github com robang74 p2c2 management style raw main my scrum in a nutshell pdf for project management and p c management style https github com robang74 p2c2 management style raw main p2c2 management style pdf for team management adequately managing a project is just as crucial as managing the people involved in the project there is no way to separate these two aspects or avoid them undoubtedly it is not a cheap approach and this is the primary reason because numerous attempts to industrialize creative intellectual it work have been made in vain since the burst of the 2001 dot com bubble | linux-distribution poc tinycore | os |
NTOU-CS-The-Design-of-Embedded-Systems | the design of embedded systems github https img shields io github license 5j54d93 ntou cs the design of embedded systems github repo stars https img shields io github stars 5j54d93 ntou cs the design of embedded systems github repo size https img shields io github repo size 5j54d93 ntou cs the design of embedded systems all my course works of the design of embedded systems in ntou cs mcs51 8051 https github com 5j54d93 ntou cs the design of embedded systems tree main mcs51 8051 control electronic scroll with uart cycle electronic scroll keyboard piano simple electronic watch raspberry pi https github com 5j54d93 ntou cs the design of embedded systems tree main raspberry pi pwm colorful led push button to play music license mit this package is mit licensed https github com 5j54d93 ntou cs the design of embedded systems blob main license | ntou cs mcs51-8051 raspberry-pi | os |
data-pipeline-automation | gans e scooter data pipeline project overview gans is a nascent e scooter sharing system aiming to thrive in major cities globally competing against giants like tier and bird gans plans to carve out its niche not just with its sustainable mobility approach but by optimizing scooter placement based on real time user needs while many e scooter firms prioritize the eco friendliness narrative gans believes that its operational success lies in ensuring scooters are strategically placed various factors such as city topography daily commuter patterns and tourist behavior influence scooter placement to systematically address this challenge gans endeavors to set up a comprehensive and automated data pipeline in the cloud this initiative aims to utilize real time data for predictive modeling ensuring users always find a scooter when they need one project phases phase 1 local pipeline the journey begins with data collection and storage on a local environment 1 1 web scraping objective extract data from the web tools mainly python s beautifulsoup library data sources websites with relevant e scooter data 1 2 api data collection objective obtain specific datasets from online providers tools python s requests library for api interactions data sources external platforms offering data via apis 1 3 database model creation objective establish a logical database structure tasks determine necessary tables and their interrelations outcome a blueprint of the database tailored for gans s operational needs 1 4 local mysql data storage objective validate the designed database model tasks initiate a local mysql instance and populate with curated data outcome a database on a local server filled with relevant datasets phase 2 cloud pipeline transitioning to the cloud we aim to leverage its scalability flexibility and automation capabilities 2 1 cloud database setup objective establish the database in a cloud environment tools amazon web services relational database service rds for mysql setup outcome a scalable and flexible cloud hosted database 2 2 script migration to lambda objective execute code effortlessly in the cloud tools aws lambda for migrating data collection scripts from jupyter notebooks outcome cloud based scripts ready for scheduled execution 2 3 pipeline automation objective harness cloud capabilities for seamless automation tools aws cloudwatch events eventbridge for triggering data collection tasks outcome a fully automated data collection process running on predefined schedules dive deeper with medium articles visit individual medium articles for a deeper dive into each section of this journey the pulse of innovation unfolding a seamless data pipeline journey https medium com sergio david the pulse of innovation unfolding a seamless data pipeline journey e40e786720c9 the great data hunt scraping the web and unlocking apis https medium com sergio david the great data hunt scraping the web and unlocking apis 514251a7e97f the process initiates with data gathering from diverse sources including apis and web pages from chaos to clarity the art of data cleaning and transformation https medium com sergio david from chaos to clarity the art of data cleaning and transformation c57920de19cf raw data undergoes rigorous cleaning and transformation to be rendered usable building castles in the cloud a guide to database design and aws rds https medium com sergio david building castles in the cloud a guide to database design and aws rds 1aabc3ad654d the heart of the system the relational data model is implemented in mysql the automation orchestra scheduling harmony with aws lambda https medium com sergio david the automation orchestra scheduling harmony with aws lambda 521d2a55bd5f automation ensures a seamless continuous flow of data navigating the maze reflections and wisdom from a data pipeline odyssey https medium com sergio david navigating the maze reflections and wisdom from a data pipeline odyssey 94dd38ab94c1 insights into the intricacies and learnings from the project license mit license license | cloud |
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CryptoCurrencyTrader | cryptocurrencytrader a machine learning program in python to generate cryptocurrency trading strategies using machine learning the script is inspired by both the pytrader project https github com owocki pytrader and the auto sklearn project https automl github io auto sklearn stable disclaimer the information in this repository is provided for information purposes only the information is not intended to be and does not constitute financial advice or any other advice is general in nature and not specific to you input data minor changes were made to the poloniex api python wrapper which is inluded in the repository https github com s4w3d0ff python poloniex data is retrieved via the poloniex api in ohlc open high low close candlestick format alternatively data can be supplied in the form of csv files by including them in the working directory setting web flag as false and supplying the filenames as filename1 and filename2 filename1 will be the currency pair used for trading technical indicators training inputs a series of technical indicators are calculated and provided as inputs to the machine learning optimisation exponential moving averages and exponential moving volatilities over a series of windows a kalman filter is also provided as an input training targets strategy score an ideal trading strategy is generated based on past data every candlestick is given a score which represent the potential profit or loss before the next price reversal exceeding the combined transaction fee and bid ask spread this minimum price reversal is represented by p in the diagram below alt text strategyscore jpg raw true optional title strategy generation a buy threshold and sell threshold are selected which maximise profit based on the score returned for the training data where a sell or buy signal is generated if the respective threshold is crossed machine learning meta fitting and hyper parameter optimisation the machine learning optimisation is based on a two layer random search as outlined in the diagram below the meta fitting selects a machine learning and preprocessing pair the selected machine learning model is then optimised using a second random grid search to fit the hyperparameters for that particular machine learning model without gpu support the tensorflow fitting may take a long time alt text ml flowchart png raw true optional title example results with none of the different automated machine learning optimisation strategies was i able to get a set of fitting parameters which was consistently profitable at multiple offsets some of the offsets would be profitable an example is included below alt text fitting example png raw true optional title validation in order to estimate the amount of overfitting a series of offset hyperparameter fittings are performed if the trading strategy is not overfit fitting should be approximately consistent across at all offsets in terms of profit fraction and fitting error to do with none of the different automated machine learning optimisation strategies was i able to get a set of fitting parameters which was consistently profitable at multiple offsets add none price data python 2 7 tensorflow miniconda https conda io docs installation html https conda io docs downloads conda cheatsheet pdf osx linux conda create n tensorflow p2 python 2 7 source activate tensorflow p2 conda install numpy pandas matplotlib tensorflow jupyter notebook scipy scikit learn nb conda conda install c auto multiprocessing statsmodels arch pip install arch polyaxon windows conda create n tensorflow p2 python 2 7 activate tensorflow p2 conda install numpy pandas matplotlib tensorflow jupyter notebook scipy scikit learn nb conda conda install c auto multiprocessing statsmodels arch pip install arch polyaxon | ai |
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AIO | aio build status https app bitrise io app b605234f1ad1aac4 status svg token rgpbc9nzrqanan694acybg branch dev https app bitrise io app b605234f1ad1aac4 ktlint https img shields io badge code 20style e2 9d a4 ff4081 svg https ktlint github io license https img shields io badge license apache 202 0 blue svg https opensource org licenses apache 2 0 awesome kotlin badge https kotlin link awesome kotlin svg https github com kotlinby awesome kotlin movie explore featuring modularization and latest trends on android who is it for android developer folks koltin fans want to learn a real usage of a tool interested in the architecture in use want to participate on crafting a great app beginners intermediates advanced developers just here to copy past images of aio aio demo assets aio demo gif aio main 1 assets aio main 1 png aio main 2 assets aio main 2 png aio detail assets aio detail 1 png aio search assets aio search 1 png about aio stand for all in one it s there because i wanted to say you might find everything at one place no surprises aio is kotlin first so everything you see here in this repository will be in kotlin for now i m using tmdb as backend and api source architecture is mvi udf using rxjava using bitrise https www bitrise io for ci cd release generation is all automatic and human less every push to master causes a new release i try to explain every path i take every workaround i make and all and all anything interesting on my blog worldsnas com https worldsnas com setup a side project http bit ly 326rm8a why i chose dagger a dagger story http bit ly 2si0md2 deep dive in dagger setup http bit ly 2jogqu3 setting up a ci server http bit ly 335ayfx make sure to have a look there for a detailed explanation installing running first create a local properties in root folder get a tmdb api key https developers themoviedb org 3 getting started introduction and add to the properties file with api key tag add a empty field for these keys keystorepass aioalias keypass keystoreaddress fabric api key lastly disable googleservices plugin by commenting apply plugin com google gms google services in app build gradle file now you can open the project in androidstudio and build test run it latest release the project has not yet been published on google play but you can grab the latest release in two ways using telegram aio release channel https t me aio release github release page releases https github com worldsnas aio releases features and goals all the detail of project milestones features progress done can be found on github project https github com worldsnas aio projects 1 for this repository i try to keep it updated and as detailed as possible technologies conductor https www bitrise io a small yet full featured framework that allows building view based android applications dagger2 https dagger dev users guide dependency injection framework for both java and android butterknife https github com jakewharton butterknife view binding into fields rxjava https github com reactivex rxjava a library for composing asynchronous and event based programs by using observable sequences epoxy https github com airbnb epoxy epoxy is an android library for building complex screens in a recyclerview dynamicfeatures https developer android com studio projects dynamic delivery deliver code to android ondemand retrofit https github com square retrofit type safe http client for android and java okhttp https github com square okhttp an http client for android kotlin and java fresco https github com facebook fresco an android library for managing images and the memory they use leakcanary https github com square leakcanary leakcanary is a memory leak detection library for android assertj https joel costigliola github io assertj fluent assertions for java mockk https github com mockk mockk mocking library for kotlin mockwebserver https github com square okhttp tree master mockwebserver a scriptable web server for testing http clients robolectric http robolectric org running android tests on jvm androidx test https developer android com training testing helper libraries for android tests contribution there are a ton of stuff that you can contribute to readme reporting issues with the app by creating new issues on this repo new cool feature in mind add it to the project page like to add documentation just send a pr like to see a feature in the app quicker try to implement it yourself and send a pr a typo sure why not bug fix shoot it different approach to some implementation i m all open something bothers in this repo open a discussion issue so we can talk | android kotlin test dagger2 rxjava2 coroutines-android fresco conductor robolectric retrofit okhttp udf mvi | front_end |
yuanming-hu.github.io | a puzzle game chinese about number theory fluid a fluid simulator gear a physically based puzzle game | front_end |
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