VLaNC Lab
Books
A curated list of book-length references we frequently recommend across our courses and lab reading.
Generative Modeling
Core references for VAEs, flows, diffusion models, and modern deep generative methods.
Natural Language Processing
NLP foundations and modern transformer-centric practice.
Speech and Language Processing (3rd ed. draft)
Daniel Jurafsky, James H. Martin
Draft
FreeDraft
Openhttps://web.stanford.edu/~jurafsky/slp3/
Natural Language Processing with Transformers
Lewis Tunstall, Leandro von Werra, Thomas Wolf
2022 · O'Reilly
Practical
Openhttps://www.oreilly.com/library/view/natural-language-processing/9781098103231/
Neural Network Methods in Natural Language Processing
Yoav Goldberg
2017 · Morgan & Claypool / Springer
Classic
Openhttps://link.springer.com/book/10.1007/978-3-031-02165-4
Vision and Multimodal
Computer vision references that pair well with vision-language and multimodal ML work.
Computer Vision: Algorithms and Applications (2nd ed.)
Richard Szeliski
2022
Free
Openhttps://szeliski.org/Book/
Machine Learning Foundations
Mathy and practical foundations for modern ML, deep learning, and probabilistic modeling.
Deep Learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville
2016 · MIT Press
Free
Openhttps://www.deeplearningbook.org/
Probabilistic Machine Learning: An Introduction
Kevin P. Murphy
2022 · MIT Press
Free
Openhttps://probml.github.io/pml-book/book1.html
Pattern Recognition and Machine Learning
Christopher M. Bishop
2006 · Springer
Classic
Openhttps://link.springer.com/book/10.1007/978-0-387-45528-0
Information Theory, Inference, and Learning Algorithms
David J. C. MacKay
2003
FreeClassic
Openhttps://www.inference.org.uk/mackay/itila/book.html
Understanding Deep Learning
Simon J. D. Prince
2023
Free
Openhttps://udlbook.github.io/udlbook/
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.)
Aurélien Géron
2022 · O'Reilly
Practical
Openhttps://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/