10 Must-read books and surveys about AI and Machine Learning

2026-02-15 · Source: Turing Post · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Advanced, quick

Summary

A collection of ten free books and surveys offers comprehensive resources across major fields and techniques in AI and machine learning. Key topics include Machine Learning Systems by Vijay Janapa Reddi, covering data engineering and hardware-aware training; Understanding Deep Learning by Simon J.D. Prince, exploring core concepts and architectures; and Interpretable Machine Learning by Christoph Molnar, detailing transparent models and model-agnostic methods. The collection also features resources on Large Language Models, including "Foundations of Large Language Models" (270 pages) and "A Survey on Post-training of Large Language Models," alongside surveys on multimodal generative models, context engineering, agentic LLMs, and geometric deep learning, including its mathematical foundations.

Key takeaway

For AI Researchers and Machine Learning Engineers seeking to deepen their expertise or fill knowledge gaps, these curated books and surveys offer structured learning paths. You should explore resources on specific areas like LLM post-training, multimodal generative models, or geometric deep learning to enhance your understanding of current techniques and future directions. Consider integrating the practical methods discussed, such as interpretability techniques or hardware-aware training, into your project workflows.

Key insights

This collection provides foundational and advanced resources across diverse AI and machine learning domains.

Principles

• Effective ML solutions require hardware-aware optimization.
• Interpretability is crucial for understanding model decisions.

Method

The resources cover methods like policy optimization (RLHF, DPO), supervised fine-tuning, and model-agnostic interpretability techniques (LIME, Shapley values).

In practice

• Apply LIME for local model explanations.
• Utilize RLHF for LLM alignment.

Topics

• Machine Learning Systems
• Deep Learning
• Interpretable Machine Learning
• Large Language Models
• Geometric Deep Learning

Code references

• christophM/interpretable-ml-book

Best for: AI Researcher, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Turing Post.