Machine Learning with Python

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Matrix Calculus for Machine Learning and Beyond! — a free ebook from MIT. This is the 2025 MIT textbook by Alan Edelman, Steven G. Johnson, and Paige Bright. The book directly connects matrix calculus to modern machine learning. Inside: *   Derivatives of matrices and vectors *   Jacobian and Hessian *   Matrix decompositions *   Optimization *   Differentiation in reverse mode *   Backpropagation of error *   Automatic differentiation *   Derivatives through ODEs *   Problems focused on machine learning This is a comprehensive mathematical bridge between linear algebra, calculus, optimization, backpropagation, and machine learning. Free ebook: https://geni.us/Matrix-Calculus-Book

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From machine learning and data visualization to time series and financial data. This repository contains 920 open-source Python projects, categorized into 34 groups. It's a great collection if you want to quickly find reliable libraries and tools for machine learning, data analysis, and related tasks, rather than searching everything manually on GitHub. https://github.com/lukasmasuch/best-of-ml-python

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🚨 Cambridge has just released a real bombshell this time. 📚 A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format. If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation. From simple to complex. 1️⃣ Understanding Machine Learning One of the best books for beginners. It covers the basic theoretical algorithms of machine learning. 🔗 https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf 2️⃣ Mathematical Foundations of Machine Learning If you're not very confident in your math skills, I would start here. 🔗 https://mml-book.github.io/book/mml-book.pdf 3️⃣ Mathematical Analysis of Machine Learning Algorithms A more in-depth look at the mathematical principles of machine learning algorithms. 🔗 https://tongzhang-ml.org/lt-book/lt-book.pdf 4️⃣ Theoretical Principles of Deep Learning The theoretical foundations of deep learning and an understanding of why it all works. 🔗 https://arxiv.org/pdf/2106.10165 5️⃣ Neural Networks and Learning Machines A systematic analysis of neural networks and the principles of their training. 🔗 https://arxiv.org/pdf/1901.05639 6️⃣ Graph Deep Learning A good starting point for those who want to understand graph neural networks. 🔗 https://yaoma24.github.io/dlg_book/dlg_book.pdf 7️⃣ Machine Learning: A Probabilistic Perspective It allows you to look at machine learning from a probabilistic and algorithmic perspective. 🔗 https://people.csail.mit.edu/moitra/docs/bookexv2.pdf 8️⃣ Probability Theory: Theory and Examples Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries. 🔗 https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf 9️⃣ Fundamentals of Applied Probability More focus on the practical application of probability theory. 🔗 https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf 🔟 Advanced Data Analysis An advanced level for those who want to seriously improve their data analysis skills. 🔗 https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf #AI #MachineLearning #FreeBooks #DataScience #DeepLearning #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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🔖 A comprehensive resource on LLMs in one repository We found an open-source course covering Transformers, LoRA, RAG, prompts, model editing, and other key topics. After each chapter, you can immediately access the original sources – the authors have compiled papers and collections from arXiv. ⛓Link to GitHub https://github.com/ZJU-LLMs/Foundations-of-LLMs