{"id":24280,"date":"2021-02-23T11:51:20","date_gmt":"2021-02-23T02:51:20","guid":{"rendered":"https:\/\/www.waca.associates\/en\/?p=24280"},"modified":"2021-03-02T17:53:18","modified_gmt":"2021-03-02T08:53:18","slug":"top-5-artificial-intelligence-repositories-in-github","status":"publish","type":"post","link":"https:\/\/www.waca.or.jp\/en\/growthhacking\/top-5-artificial-intelligence-repositories-in-github\/","title":{"rendered":"[KIT] Top 5 Artificial Intelligence Repositories in GitHub"},"content":{"rendered":"

<\/h2>
\"What<\/figure>
\"Artificial<\/figure>

1\/ AI LEARNING<\/h2>

AiLearning consists of many lessons such as Machine Learning-MachineLearning-ML, Deep Learning-DeepLearning-DL, Natural Language Processing NLP.
AiLearning Repository got 29K <\/strong>Stars and 9.8K <\/strong>Forks.
They are providing 16 Chapter such as:
Chapter 1: Machine Learning Fundamentals<\/a>
Chapter 2: KNN Nearest Neighbor Algorithm<\/a>
Chapter 3: Decision Tree<\/a>
Chapter 4: Naive Bayes<\/a>
Chapter 5: Logistic Regression<\/a>
Chapter 6: SVM Support Vector Machine<\/a>
Chapter 7: Integrated Methods (Random Forest and AdaBoost)<\/a>
Chapter 8: Return<\/a>
Chapter 9: Tree Return<\/a>
Chapter 10: K-Means clustering<\/a>
Chapter 11: Association Analysis Using Apriori Algorithm<\/a>
Chapter 12: FP-growth finds frequent itemsets efficiently<\/a>
Chapter 13: Using PCA to simplify data<\/a>
Chapter 14: Using SVD to simplify data<\/a>
Chapter 15: Big Data and MapReduce<\/a>
Chapter 16: Recommended System (Migrated)<\/a><\/p>

2\/funNLP<\/h2>

Chinese and English sensitive words, language detection, Chinese and foreign mobile phone\/phone attribution\/operator query, name inference gender, mobile phone number extraction, ID card extraction, mailbox extraction, Chinese and Japanese name database, Chinese abbreviation database, disassembly dictionary, vocabulary emotional value, Stop words, counter verb list, violent word list, traditional and simplified conversion, English analog Chinese pronunciation, Wang Feng lyrics generator, professional name thesaurus, thesaurus, antonyms, negative thesaurus, automobile brand thesaurus, auto parts words Database, continuous English cutting, various Chinese word vectors, company name list, ancient poetry lexicon, IT lexicon, financial lexicon, idiom lexicon, idiom lexicon, historical celebrity lexicon, poetry lexicon, medical lexicon, food Thesaurus, legal thesaurus, automobile thesaurus, animal thesaurus, Chinese chat corpus, Chinese rumor data, Baidu Chinese question and answer data set, sentence similarity matching algorithm collection, bert resource, text generation & abstract related tools, cocoNLP information extraction tool , Regular matching of domestic telephone numbers, Tsinghua University XLORE: Chinese and English cross-language knowledge map, Tsinghua University artificial intelligence technology… Continue Read<\/a>
This repository collects a lot of other necessary repositories related to NLP(Natural Language Processing). NLPfun got 28.8K <\/strong>stars and 8.5K <\/strong>forks.
https:\/\/github.com\/fighting41love\/funNLP<\/a><\/p>

3\/Gold Miner<\/h2>

This repository is a project.
Nuggets Translation Project<\/a> is a high-quality translation of technical articles Internet community, Source for the Nuggets<\/a> English Share article on. Contents cover block chain<\/a> , artificial intelligence<\/a> , Android<\/a> , iOS<\/a> , front-end<\/a> , back-end<\/a> , design<\/a> , product<\/a> , algorithms<\/a> and other<\/a> fields, as well as large high-quality official documents and manuals<\/a> , readers love the cutting-edge developer of new technologies.
Gold-miner got 28.3K<\/strong> stars and 4.6<\/strong> forks.
More information: click here<\/a><\/p>

4\/Airflow<\/h2>

This repository is a project that works on data processing.
Airflow works best with workflows that are mostly static and slowly changing. When DAG structure is similar from one run to the next, it allows for clarity around the unit of work and continuity. Other similar projects include Luigi<\/a>, Oozie<\/a>, and Azkaban<\/a>.
Airflow is commonly used to process data, but has the opinion that tasks should ideally be idempotent (i.e. results of the task will be the same, and will not create duplicated data in a destination system), and should not pass large quantities of data from one task to the next (though tasks can pass metadata using Airflow’s Xcom feature<\/a>). continue reading<\/a>
Airflow got 20.5K <\/strong>stars and 8K <\/strong>forks.<\/p>

5\/spaCy<\/h2>

This repository is a library for advanced Natural Language Processing<\/strong> in Python and Cython. It’s built on the very latest research and was designed from day one to be used in real products. Continue Reading<\/a>
spaCy got 19.7K <\/strong>stars and 3.3K <\/strong>forks.<\/p>

<\/p>","protected":false},"excerpt":{"rendered":"1\/ AI LEARNING AiLearning consists of many lessons such as Machine Learning-MachineLearning-ML, Deep Learning-DeepLearning-DL, Natural Language Processing NLP.AiLearning Repository got 29K Stars and 9.8K Forks.They are providing 16 Chapter such as:Chapter 1: Machine Learning FundamentalsChapter 2: KNN Nearest Neighbor AlgorithmChapter 3: Decision TreeChapter 4: Naive BayesChapter 5: Logistic RegressionChapter 6: SVM Support Vector MachineChapter 7: Integrated Methods (Random Forest and AdaBoost)Chapter 8: ReturnChapter 9: Tree ReturnChapter 10: K-Means clusteringChapter 11: Association Analysis Using Apriori AlgorithmChapter 12: FP-growth finds frequent itemsets efficientlyChapter 13: Using PCA to simplify dataChapter 14: Using SVD to simplify dataChapter 15: Big Data and MapReduceChapter 16: Recommended System (Migrated) 2\/funNLP Chinese and English sensitive words, language detection, […]","protected":false},"author":767,"featured_media":25826,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[169],"tags":[],"class_list":["post-24280","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-growthhacking"],"jetpack_featured_media_url":"https:\/\/www.waca.or.jp\/en\/wp-content\/uploads\/2021\/02\/1_WY7ELhXIVxbGlUwmhA1PSw.jpeg","_links":{"self":[{"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/posts\/24280"}],"collection":[{"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/users\/767"}],"replies":[{"embeddable":true,"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/comments?post=24280"}],"version-history":[{"count":3,"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/posts\/24280\/revisions"}],"predecessor-version":[{"id":24323,"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/posts\/24280\/revisions\/24323"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/media\/25826"}],"wp:attachment":[{"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/media?parent=24280"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/categories?post=24280"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.waca.or.jp\/en\/wp-json\/wp\/v2\/tags?post=24280"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}