Literature

This page lists freely available books and books available through VŠE e-resources. VŠE e-resources may require institutional sign-in.

Python and machine learning

• Wes McKinney, Python for Data Analysis, 3rd edition — the book seminar 1 is built on. Free online at wesmckinney.com/book.
• Allen Downey, Think Python, 3rd edition — allendowney.github.io/ThinkPython.
• Allen Downey, Think Stats, 3rd edition — allendowney.github.io/ThinkStats.
• James, Witten, Hastie, Tibshirani, Taylor, An Introduction to Statistical Learning with Applications in Python (ISLP) — the reference for seminar 2. Free PDF and labs at statlearning.com.

Language models and deep learning

• Raschka, S. (2024). Build a Large Language Model (From Scratch). Simon and Schuster. Available through VŠE e-resources.
• Huyen, C. (2025). AI Engineering: Building Applications with Foundation Models. O'Reilly. Available through VŠE e-resources.
• Tunstall, L., Werra, L. von, & Wolf, T. (2022). Natural language processing with transformers, Revised edition. O’Reilly Media, Inc. Available through VŠE e-resources.
• Daniel Jurafsky, James H. Martin, Speech and Language Processing, 3rd edition draft — free at web.stanford.edu/~jurafsky/slp3.
• Zhang, Lipton, Li, Smola, Dive into Deep Learning — free, with PyTorch code, at d2l.ai.
• Simon J. D. Prince, Understanding Deep Learning, MIT Press — free PDF and notebooks at udlbook.github.io/udlbook.

Further reading

• llm-feature-gen — the package used in the seminar-3 extra notebook: documentation.
• Other languages: Hadley Wickham et al., R for Data Science, 2nd edition — r4ds.hadley.nz.
• Podcast (Czech): Dataři — Český rozhlas Plus.