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@@ -442,7 +442,6 @@ Books that cover a specific programming language can be found in the [BY PROGRAM
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*[Dive into Deep Learning](https://d2l.ai)
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*[Elements of AI](https://www.elementsofai.com) - MinnaLearn, University of Helsinki
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*[Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises](https://web.stanford.edu/group/pdplab/pdphandbook) - James L. McClelland
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*[Practical Deep Learning for Coders](https://course.fast.ai) - fast.ai
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*[Foundations of Machine Learning, Second Edition](https://mitpress.ublish.com/ebook/foundations-of-machine-learning--2-preview/7093/Cover) - Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
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*[Free and Open Machine Learning](https://nocomplexity.com/documents/fossml/) - Maikel Mardjan (HTML)
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*[Gaussian Processes for Machine Learning](https://www.gaussianprocess.org/gpml/) - Carl Edward Rasmussen, Christopher K.I. Williams
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*[Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)
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*[Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) - Christopher M. Bishop (PDF)
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*[Patterns, Predictions, And Actions: A story about machine learning](https://mlstory.org/pdf/patterns.pdf) - Moritz Hardt, Benjamin Recht (PDF)
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*[Practical Deep Learning for Coders](https://course.fast.ai) - fast.ai
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*[Practitioners guide to MLOps](https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf) - Khalid Samala, Jarek Kazmierczak, Donna Schut (PDF)
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*[Probabilistic Machine Learning - An Introduction](https://github.com/probml/pml-book/releases/latest/download/book1.pdf) - Kevin P. Murphy (PDF)
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*[Python Machine Learning Projects](https://www.digitalocean.com/community/books/python-machine-learning-projects-a-digitalocean-ebook) - Lisa Tagliaferri, Brian Boucheron, Michelle Morales, Ellie Birkbeck, Alvin Wan (PDF, EPUB, Kindle)
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