{"id":13684704,"url":"https://github.com/VidyasagarMSC/Awesome-AI","last_synced_at":"2025-05-01T00:32:40.029Z","repository":{"id":39898173,"uuid":"132483809","full_name":"VidyasagarMSC/Awesome-AI","owner":"VidyasagarMSC","description":"The guide to master Artificial Intelligence (machine learning \u0026 deep learning) from beginner to advance","archived":false,"fork":false,"pushed_at":"2024-03-31T20:18:26.000Z","size":806,"stargazers_count":145,"open_issues_count":0,"forks_count":30,"subscribers_count":16,"default_branch":"master","last_synced_at":"2024-10-27T18:17:04.644Z","etag":null,"topics":["article","artificial-intelligence","cheatsheet","deep-learning","infographics","machine-learning","machine-learning-algorithms","mooc","open-source","tutorial"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/VidyasagarMSC.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2018-05-07T15:54:45.000Z","updated_at":"2024-10-23T07:35:30.000Z","dependencies_parsed_at":"2024-01-14T16:11:39.386Z","dependency_job_id":"598cb247-8055-41fc-bad4-98dc27f9b909","html_url":"https://github.com/VidyasagarMSC/Awesome-AI","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VidyasagarMSC%2FAwesome-AI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VidyasagarMSC%2FAwesome-AI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VidyasagarMSC%2FAwesome-AI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VidyasagarMSC%2FAwesome-AI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/VidyasagarMSC","download_url":"https://codeload.github.com/VidyasagarMSC/Awesome-AI/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224230576,"owners_count":17277372,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["article","artificial-intelligence","cheatsheet","deep-learning","infographics","machine-learning","machine-learning-algorithms","mooc","open-source","tutorial"],"created_at":"2024-08-02T14:00:37.441Z","updated_at":"2024-11-12T06:30:30.567Z","avatar_url":"https://github.com/VidyasagarMSC.png","language":null,"funding_links":[],"categories":["Awesome lists for learning AI","Other Lists"],"sub_categories":["TeX Lists"],"readme":"# Awesome-AI\n\nA curated list of articles, books, courses, infographics and many more covering Artificial Intelligence, Machine Learning and Deep Learning. \n\n\u003e Check the new [**Awesome-DS**](https://github.com/VidyasagarMSC/Awesome-DS) repo for Data Science content\n\n## Beginners - Getting Started\n\n### :pencil: Articles\n\n- [An introduction to Artificial Intelligence](https://hackernoon.com/understanding-understanding-an-intro-to-artificial-intelligence-be76c5ec4d2e)\n- [A beginner's guide to artificial intelligence, machine learning, and cognitive computing](https://developer.ibm.com/articles/cc-beginner-guide-machine-learning-ai-cognitive/)\n- [Which machine learning algorithm should I use?](https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/?utm_source=facebook\u0026utm_medium=cpc\u0026utm_campaign=analytics-global\u0026utm_content=US_interests-conversions)\n- [The Journey of a Machine Learning model from Building to Retraining](https://towardsdatascience.com/the-journey-of-a-machine-learning-model-from-building-to-retraining-fe3a37c32307?gi=38d2b73db825)\n- [An executive’s guide to AI by McKinsey\u0026Company](https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai)\n- [A Comprehensive Guide On How to Monitor Your Models in Production](https://neptune.ai/blog/how-to-monitor-your-models-in-production-guide)\n\n### :page_facing_up:Courses\n- [Introduction to Artificial Intelligence by Microsoft on edx](https://www.edx.org/course/introduction-to-artificial-intelligence-ai)\n- [Introduction to Machine Learning crash course by Google](https://developers.google.com/machine-learning/crash-course/ml-intro)\n- [Foundations of Machine Learning(foml) by Bloomberg](https://www.techatbloomberg.com/foml)\n\n### :books:Books\n\n- [5 books to Master Machine Learning](https://www.kdnuggets.com/5-free-books-to-master-machine-learning)\n- [Machine Learning Yearning by Andrew Ng - Signup for a free draft copy](http://www.mlyearning.org) - *Approx. 100 pages*\n- [The Hundred-Page Machine Learning Book](http://themlbook.com/wiki/doku.php)\n\n### :snowman:Quick guides\n\n- [Introductory Guide to Artificial Intelligence](https://towardsdatascience.com/introductory-guide-to-artificial-intelligence-11fc04cea042)\n- [DZone's Guide to\nArtificial Intelligence: Machine Learning and Predictive Analytics](https://dzone.com/guides/artificial-intelligence-machine-learning-and-predi)\n- [What is Machine Learning?](https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/i/88174_92991v00_machine_learning_section1_ebook.pdf)\n- [Introducing Deep Learning with MATLAB](https://es.mathworks.com/content/dam/mathworks/tag-team/Objects/d/80879v00_Deep_Learning_ebook.pdf)\n- [Introduction to Tensorflow](https://dzone.com/refcardz/introduction-to-tensorflow?chapter=1)\n\n### Cheatsheets\n- [AI cheatsheets](https://www.codecademy.com/resources/cheatsheets/subject/artificial-intelligence)\n- [The machine learning algorithm cheat sheet - sas.com](cheatsheets/machinelearning/machine-learning-cheat-sheet-sas.png)\n- [Machine Learning Cheatsheet](http://ml-cheatsheet.readthedocs.io/en/latest/index.html)\n- Stanford CS 229:\n  - Deep Learning: http://stanford.io/2BsQ91Q  \n  - Supervised Learning: http://stanford.io/2nRlxxp  \n  - Unsupervised Learning: http://stanford.io/2MmP6FN  \n- [Machine Learning for dummies cheat sheet](https://www.dummies.com/programming/big-data/data-science/machine-learning-dummies-cheat-sheet/)\n\n### Tutorials\n- [Machine Learning Tutorial for Beginners – Learn Machine Learning](https://data-flair.training/blogs/machine-learning-tutorial/)\n- [Deep Learning Tutorial for Beginners - Kaggle](https://www.kaggle.com/kanncaa1/deep-learning-tutorial-for-beginners)\n\n### Presentations \n- [Deep Learning - The Past, Present and Future of Artificial Intelligence](https://www.slideshare.net/LuMa921/deep-learning-the-past-present-and-future-of-artificial-intelligence)\n\n## :robot:Advanced AI\n### :page_facing_up:Courses\n- [Deeplearning.ai | Coursera by Andrew Ng](https://www.deeplearning.ai) - *5 courses*\n- [Machine Learning | Coursera by Andrew Ng](https://www.coursera.org/learn/machine-learning)\n- [fast.ai](http://www.fast.ai) courses\n    - Deep Learning Part 1: [Practical Deep Learning for Coders](http://course.fast.ai/lessons/lessons.html)\n    - Deep Learning Part 2: [Cutting Edge Deep Learning for Coders](http://course.fast.ai/part2.html)\n\n### :books: Books\n- [Deep Learning, An MIT Press book](http://www.deeplearningbook.org)\n- [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)\n\n## Infographics\n\n- [AI \"Technology Readiness\" Infographic](infographics/AI-tech-landscape-graphic.png)\n*Source: CallaghanInnovation*\n- [AI Timeline](infographics/AI-Timeline.jpg) *Source: Apttus*\n- [AI detailed Timeline](infographics/Artificial-Intelligence-AI-Timeline-Infographic.jpeg) *Source: Digital Intelligence Today*\n\n## Opensource Libraries and Tools\n\n- [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - Gymnasium(formerly Gym) is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.\n- [TensorFlow](https://www.tensorflow.org) - An open source machine learning framework for everyone\n- [Explore AI Libraries](https://kandi.openweaver.com/explore/artificial-intelligence) - Discover \u0026 find a curated list of AI popular \u0026 new libraries, top authors, trending project kits, discussions, tutorials \u0026 learning resources on kandi.\n\n## Interactive Tutorials\n\n- [Seedbank](http://tools.google.com/seedbank/) - Collection of Interactive Machine Learning Examples \n- [R2D3](http://www.r2d3.us) - A visual introduction to machine learning\n\n## Tips and Tricks\n\n- [Stanford CS 229](http://stanford.io/2MEHwFM)\n\n## Free books collection\n- [Engati blog](https://www.engati.com/blog/best-artificial-intelligence-books)\n\n## Datasets\n\n- [For benchmarking Deep Learning algorithms](http://deeplearning.net/datasets/)\n\n\n\u003e Thank and show your :hearts: to the respective authors\n\n:exclamation: *If you see a broken link, open an [issue](https://github.com/VidyasagarMSC/Awesome-AI/issues/new).*\n\n#### Fork this repo, add more content and a PR to merge\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FVidyasagarMSC%2FAwesome-AI","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FVidyasagarMSC%2FAwesome-AI","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FVidyasagarMSC%2FAwesome-AI/lists"}