Top Free Machine Learning Books
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  • About
  • Understanding Deep Learning (Simon J.D. Prince) (2024)
  • The Little Book of Deep Learning (François Fleuret) (2024)
  • The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks (2022)
  • Generative AI in Higher Education: The ChatGPT Effect (Cecilia Ka Yuk Chan, et al.) (2024)
  • The Shallow and the Deep: A Biased Introduction to Neural Networks and Old School Machine Learning (2023)
  • Machine Learning with Python Tutorial (Bernd Klein) (2022)
  • Mathematical Analysis of Machine Learning Algorithms (Tong Zhang) (2023)
  • Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks (2023)
  • Neural Network Learning: Theoretical Foundations
  • Approaching (Almost) Any Machine Learning Problem
  • Foundations of Machine Learning (Mehryar Mohri, et al)
  • Machine Learning Yearning (Andrew Ng)
  • Machine Learning Algorithms (Jason Brownlee)
  • Dive into Deep Learning (Aston Zhang, et al.)
  • Reinforcement Learning: An Introduction, Second Edition
  • Distributional Reinforcement Learning (Marc G. Bellemare, et al)
  • Algorithms for Reinforcement Learning (Csaba Szepesvari)
  • An Introduction to Deep Reinforcement Learning
  • Fairness and Machine Learning: Limitations and Opportunities (2023)
  • Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al) (2023)
  • Understanding Machine Learning: From Theory to Algorithms
  • Machine Learning from Scratch (Danny Friedman)
  • Deep Learning with Python, 2nd Edition (Francois Chollet)
  • Deep Learning for Coders with Fastai and PyTorch
  • Deep Learning with PyTorch (Eli Stevens, et al.)
  • Probabilistic Machine Learning: An Introduction (Kevin Murphy)
  • Introduction to Statistical Learning: with Applications in Python (Gareth James, et al)
  • Statistics and Machine Learning in Python (Edouard Duchesnay, et al.)
  • Deep Learning (Ian Goodfellow, et al)
  • The Amazing Journey of Reason: from DNA to Artificial Intelligence
  • Machine Learning with Neural Networks (Bernhard Mehlig)
  • Efficient Learning Machines: Theories, Concepts, and Applications
  • Boosting: Foundations and Algorithms (Robert E. Schapire, et al)
  • An Introduction to Machine Learning Interpretability
  • Pen and Paper Exercises in Machine Learning (Michael Gutmann)
  • Interpretable Machine Learning: Black Box Models Explainable
  • Pattern Recognition and Machine Learning (Christopher Bishop)
  • Gaussian Processes for Machine Learning (Carl E. Rasmussen)
  • The Hundred-Page Machine Learning Book (Andriy Burkov)
  • An Introduction to Statistical Learning (Gareth James, et al)
  • Machine Learning Engineering (Andriy Burkov)
  • Machine Learning from Scratch (Danny Friedman)
  • An Introduction to Quantum Machine Learning for Engineers
  • Mathematics for Machine Learning (Marc P. Deisenroth, et al.)
  • Financial Machine Learning (Bryan T. Kelly, et al.) (2023)