A curated collection of books to help you grow as a Machine Learning Engineer and Scientist.
Originally created to share books that have helped me in some way on my journey as a machine learning engineer (MLE), hopefully some of which will be useful to you too!
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- Bishop: Deep Learning, Foundations and Concepts (free e-reader version).
- Hastie et al: The Elements of Statistical Learning.
- Deisenroth et al: Mathematics for Machine Learning.
- Goodfellow et al: Deep Learning.
- Sutton et al: Reinforcement Learning.
- Foster: Generative Deep Learning
- Jurafsky et al: Speech and Language Processing.
- Tunstall et al: Natural Language Processing with Transformers.
- Jurafsky et al: Speech and Language Processing.
- Tunstall et al: Natural Language Processing with Transformers.
- Ellis: The Essential Guide to Effect Sizes.
- Lee: Baysian Statistics.
- Martin: Regression Models for Categorical and Count Data.
- Leskovec: Mining Massive Datasets.
- Knuth: The Art of Computer Programming V1, Fundamental Algorithms.
- Dijkstra: A Discipline of Programming
- Martin: Clean Code, A Handbook of Agile Software Craftsmanship.
- Shaw: CPython Internals, Your Guide to The Python 3 Interpreter.
- Gorelick et al: High Performance Python.
- Okken: Python Testing with Pytest.
- Larson: An Elegant Puzzle, Systems of Engineering Management.
- Reilly: The Staff Engineer's Path, A Guide for Individual Contributors Navigating Growth & Change.