4.3.2 Books & articles

  1. Data Structures and Algorithms in Python by Michael T. Goodrich or Introduction to Algorithms by Thomas Cormen et al..
  2. A First Course in Probability by Sheldon Ross.
  3. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
  4. Information Theory, Inference, and Learning Algorithms by David MacKay. Free online version here.
  5. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Free online version here.
  6. Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. Essential for anyone interested in Natural Language Processing. Free online version here.
  7. Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto. Essential for reinforcement learning. Free online version here.
  8. OpenAI Spinning up in Deep Reinforcement Learning: A collection of articles that give great intuition for many RL algorithms. Highly recommended for anyone interested in RL.
  9. Convex Optimization by Stephen Boyd and Lieven Vandenberghe. Super helpful but also super hard -- Stephen Boyd is basically a god. Free online version here.
  10. Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. This book is extremely relevant as machine learning is moving towards bigger models that use massive amounts of compute and data. Free online version here.
  11. Deep Learning with Hadoop & Apache Spark

    No book on the subject that I can find, but there are a few helpful articles here.

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