Essential Machine Learning Books Guide | Generated by AI
Question: What are the best books to learn machine learning (ML), deep learning (DL), and GPT/large language models?
Answer:
Here are some of the most highly recommended books for learning machine learning (ML), deep learning (DL), and topics related to GPT/large language models (LLMs) like transformers and generative AI. These recommendations are based on consensus from experts, online communities, and recent 2025 lists. I’ve categorized them by level and focus for a progressive learning path.
For Machine Learning Fundamentals (Beginners to Intermediate)
-
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
The go-to practical book for beginners. It covers ML concepts with code examples in Python, including supervised/unsupervised learning, neural networks, and deployment. Ideal starting point with hands-on projects. -
Pattern Recognition and Machine Learning by Christopher M. Bishop
A classic for theoretical understanding, focusing on probabilistic approaches. Great if you want deeper math. -
Machine Learning Yearning by Andrew Ng (free online)
Short and practical advice on building ML systems from the Coursera ML instructor.
For Deep Learning
-
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (free online at deeplearningbook.org)
The definitive “bible” of deep learning. Comprehensive theory on neural networks, optimization, CNNs, RNNs, and more. Best for intermediate to advanced learners. -
Deep Learning with Python by François Chollet (updated 2nd edition)
Practical guide using Keras/TensorFlow. Covers modern topics like transformers and generative models. Excellent for coders. -
Dive into Deep Learning by Aston Zhang et al. (interactive online book at d2l.ai, free)
Hands-on with code in multiple frameworks (PyTorch, TensorFlow, etc.). Includes generative models and real-world applications.
For Transformers, GPT, and Large Language Models (LLMs/Generative AI)
-
Natural Language Processing with Transformers by Lewis Tunstall, Leandro von der Werra, and Thomas Wolf (Hugging Face team)
Practical focus on building apps with transformers (BERT, GPT, etc.) using the Hugging Face library. Highly recommended for implementation. -
Build a Large Language Model (From Scratch) by Sebastian Raschka
Step-by-step guide to implementing a GPT-like model in code. Great for understanding internals without black-box libraries. -
Transformers for Natural Language Processing and Computer Vision by Denis Rothman (latest edition)
Covers modern models like GPT-4, DALL-E, and multimodal AI with practical examples.
Start with Géron’s book for ML basics, move to Chollet or Goodfellow for DL, then to transformer-specific books for GPT/LLMs. Supplement with free online courses (e.g., Andrew Ng’s on Coursera) and practice on datasets.
References:
-
[15 Best Machine Learning Books to Read in 2025 DataCamp](https://www.datacamp.com/blog/the-15-best-data-machine-learning-books-to-read-in-2022) -
[Unlock Machine Learning: 9 Books for Beginners in 2025 Coursera](https://www.coursera.org/articles/machine-learning-books) - Top 10 Deep Learning Books to Read in 2025
-
[Natural Language Processing with Transformers O’Reilly](https://www.oreilly.com/library/view/natural-language-processing/9781098103231/) -
[Build a Large Language Model (From Scratch) Manning](https://www.manning.com/books/build-a-large-language-model-from-scratch) - Deep Learning Book (free)
- Dive into Deep Learning