A guild for anyone interested in Artificial Intelligence (AI), Machine Learning (ML) or Deep Learning (DL) to meet, discuss, and challenge themselves.

This is a general-purpose guild for chatting and sharing resources about any topic related to Artificial Intelligence (AI), Machine Learning (ML) & Deep Learning (DL).


## Helpful Links
- [Awesome Artificial Intelligence (AI)](https://github.com/owainlewis/awesome-artificial-intelligence) - A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) - A curated list of awesome machine learning frameworks, libraries and software (by language).
- [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning) - A curated list of awesome Deep Learning tutorials, projects and communities.
- [Deep learning repositories](https://github.com/topics/deep-learning) - More than 32 thousand public Deep learning repositories.
- [Speech and Natural Language Processing](https://github.com/edobashira/speech-language-processing) - A curated list of speech and natural language processing resources.
- [Concerning AI](https://concerning.ai/) - "*Is there an existential risk to humanity from AI? If so, what do we do about it?*" **(Podcast)**
- [Two Minute Papers](https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg) - Features several Machine Learning / Deep Learning scientific articles in a simple and very didactic way **(YouTube channel)**
- [Resources To Help Get Started With Deep Learning & TensorFlow](https://becominghuman.ai/58-resources-to-help-get-started-with-deep-learning-in-tf-9f29ba585e0f) - Fifty-eight blogs/videos to enter Deep Learning with TensorFlow.
- [Introduction to Artificial Intelligence for Beginners](https://www.analyticsvidhya.com/blog/2021/09/introduction-to-artificial-intelligence-for-beginners/)
- [AI Expert Roadmap in 2021](https://i.am.ai/roadmap/)
- [Dataset Search](https://datasetsearch.research.google.com/) - a search engine for datasets.
- [Advanced Computer Vision with Python](https://www.youtube.com/watch?v=01sAkU_NvOY) - Full Course: 06:40:40
- [re3data.org](https://www.re3data.org/) - It indexes and provides extensive information about more than 2450 research data repositories.
- [spaCy](https://spacy.io/) - is a library for advanced Natural Language Processing in Python and Cython. Pretty straightforward and their [tutorials](https://course.spacy.io/en/) are great.
- [500+ Artificial Intelligence Project List with code](https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code)
- [Ten mind-blowing AI websites you probably didn’t know existed](https://faun.pub/ten-mind-blowing-ai-websites-you-probably-didnt-know-existed-3eb2ac7a9110)
- [AI Cover Letter Generator](https://tally.work/) - an AI cover letter generator that creates a cover letter from your resume and a job description in seconds.
- [Top 10 AI Tools to Check Out If You're Bored With ChatGPT](https://hackernoon.com/top-10-ai-tools-to-check-out-if-youre-bored-with-chatgpt)
- [There’s an AI for that](https://theresanaiforthat.com/) features over 4000 tools for over 1000 tasks (updated daily).
- [LetsView AI directory](https://letsview.com/ai-tools)


## (Free) **Books**
- [Machine Learning](http://smlbook.org/) - A First Course for Engineers and Scientists
- [Understanding Machine Learning: From Theory to Algorithms](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/copy.html) by Shai Shalev-Shwartz and Shai Ben-David
- [A Course in Machine Learning](http://ciml.info/) by Hal Daumé III
- [Neural Networks and Deep Learning](https://neuralnetworksanddeeplearning.com/) by Michael Nielsen
- [Deep Learning an MIT Press book](https://www.deeplearningbook.org/) by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- [Machine Learning Yearning](https://www.deeplearning.ai/programs/) by Andrew Ng
- [Machine Learning](https://www.intechopen.com/books/machine_learning) edited by Abdelhamid Mellouk
- [Reinforcement Learning: An Introduction](http://incompleteideas.net/sutton/book/the-book-2nd.html) by Richard S. Sutton and Andrew G. Barto
- [Machine Learning for Mortals (Mere and Otherwise)](https://www.manning.com/books/machine-learning-for-mortals-mere-and-otherwise) by Hefin I. Rhys
- [How Machine Learning Works](https://livebook.manning.com/book/how-machine-learning-works/welcome/v-5) by Mostafa Samir
- [MachineLearningWithTensorFlow2ed](https://www.manning.com/books/machine-learning-with-tensorflow-second-edition) by Chris Mattmann
- [Serverless Machine Learning](https://www.manning.com/books/serverless-machine-learning-in-action) by Carl Osipov
- [Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/) by Dan Jurafsky and James H. Martin
- [Natural Language Processing with Python](https://www.nltk.org/book/) by Steven Bird, Ewan Klein, and Edward Loper
- [Introduction to Information Retrieval](https://nlp.stanford.edu/IR-book/html/htmledition/irbook.html) by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze
- [Think Stats - Probability and Statistics for Python programmers](https://greenteapress.com/wp/think-stats-2e/) by Allen B. Downey
- [The Probability and Statistics Cookbook](http://statistics.zone/) by Matthias Vallentin
- [Mathematics for Computer Science](https://courses.csail.mit.edu/6.042/spring17/mcs.pdf) by Eric Lehman, F Thomson Leighton and Albert R Meyer
- [Introduction to Statistical Thought](https://people.math.umass.edu/~lavine/Book/book.pdf) by Michael Lavine
- [Introduction to Bayesian Statistics](https://www.stat.auckland.ac.nz/~brewer/stats331.pdf) by Brendon J. Brewer
- [Machine Learning from Scratch](https://dafriedman97.github.io/mlbook/content/introduction.html)
- [A Comprehensive Guide to Machine Learning](https://www.eecs189.org/static/resources/comprehensive-guide.pdf) by Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang
- [Essential Natural Language Processing](https://www.manning.com/books/essential-natural-language-processing) - Early access
- [Genetic algorithms in search, optimization, and machine learning](http://www2.fiit.stuba.sk/~kvasnicka/Free%20books/Goldberg_Genetic_Algorithms_in_Search.pdf) - Free Download
- [Advances in Evolutionary Algorithms](https://www.intechopen.com/books/advances_in_evolutionary_algorithms) - Free Download
- [Genetic Programming: New Approaches and Successful Applications](https://www.intechopen.com/books/genetic-programming-new-approaches-and-successful-applications) - Free Download
- [Evolutionary Algorithms](https://www.intechopen.com/books/evolutionary-algorithms) - Free Download
- [Advances in Genetic Programming, Vol. 3](https://www.cs.bham.ac.uk/~wbl/aigp3/) - Free Download
- [Global Optimization Algorithms: Theory and Application](http://www.it-weise.de/projects/book.pdf) - Free Download
- [Genetic Algorithms and Evolutionary Computation](http://www.talkorigins.org/faqs/genalg/genalg.html) - Free Download
- [Convex Optimization](https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf) - Convex Optimization book by Stephen Boyd - Free Download
- [Machine Learning Bookcamp](https://mlbookcamp.com/) - Early access
- [Machine Learning with Python](https://www.freecodecamp.org/learn/machine-learning-with-python/)

#### Feel free to share some of your favorite AI/ML/DL resources in chat! They might just show up on this list, too.

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