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

Mike's Notes

Alyona Vert from Turing Post compiled this fantastic list of free resources on AI and Machine Learning. Turing Post is excellent and worth subscribing to.

Resources

• https://www.turingpost.com/p/aimlbooksandsurveys

References

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• Home > Ajabbi Research > Library > Subscriptions > Turing Post
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Last Updated

11/03/2026

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

By: Alyona Vert

Turing Post: 16/02/2026

Joined Turing Post in April 2024. Studied control systems of aircrafts at BMSTU (Moscow, Russia), where conducted several researchers on helicopter models. Now is more into AI and writing.

Deep Learning, context engineering, LLMs, multimodal models and agents – all the basics together for your convenience

Sharing some free, useful resources for you. In this collection, we’ve gathered books and surveys that can be your perfect guides to the major fields and techniques. Hope this really helps you master AI and machine learning and fill in any gaps in your knowledge!

1. Machine Learning Systems by Vijay Janapa Reddi
• Provides a framework for building effective ML solutions, covering data engineering, optimization, hardware-aware training, inference acceleration, architecture choice, and other key principles.
• https://mlsysbook.ai/book/
• https://mlsysbook.ai/book/assets/downloads/Machine-Learning-Systems.pdf (preview)
2. Understanding Deep Learning by Simon J.D. Prince
• Explores core deep learning concepts: models, training, evaluation, RL, architectures for images, text and graphs, addressing open theoretical questions.
• https://udlbook.github.io/udlbook/
• https://github.com/udlbook/udlbook/releases/download/v5.0.3/UnderstandingDeepLearning_02_09_26_C.pdf
3. Interpretable Machine Learning by Christoph Molnar
• A practical guide to simple, transparent models (e.g. decision trees) and model-agnostic methods like LIME, Shapley values, permutation importance, and accumulated local effects.
• https://github.com/christophM/interpretable-ml-book
4. Foundations of Large Language Models by Tong Xiao and Jingbo Zhu
• Many recommend this 270-page book as a good resource to focus on fundamental concepts, such as pre-training, generative models, prompting, alignment, and inference.
• https://arxiv.org/abs/2501.09223
• https://arxiv.org/pdf/2501.09223
5. A Survey on Post-training of Large Language Models
• Read this to master policy optimization (RLHF, DPO, GRPO), supervised and parameter-efficient fine-tuning, reasoning, integration, and adaptation techniques.
• https://arxiv.org/abs/2503.06072
• https://arxiv.org/pdf/2503.06072
6. A Survey of Generative Categories and Techniques in Multimodal Generative Models
• Covers multimodal models, exploring six generative modalities, key techniques (SSL, RLHF, CoT), architectural trends, and challenges.
• https://arxiv.org/abs/2506.10016
• https://arxiv.org/pdf/2506.10016
7. Context Engineering 2.0: The Context of Context Engineering
• Explores context engineering – how AI understands human situations and goals – and traces its roots from early human–computer interaction to modern agents, and outlines key ideas and future directions.
• https://arxiv.org/abs/2510.26493
• https://arxiv.org/pdf/2510.26493
8. Agentic Large Language Models, a survey
• Explains agentic LLMs across reasoning, tools and multi-agent collaboration, highlighting their synergy. It also explores their promise, risks and applications in medicine, finance, science, etc.
• https://arxiv.org/abs/2503.23037
• https://arxiv.org/pdf/2503.23037
9. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
• Explores unified geometric principles to analyze neural networks' architectures: CNNs, RNNs, GNNs, Transformers, and guide the design of the future ones.
• https://arxiv.org/abs/2104.13478
• https://arxiv.org/pdf/2104.13478
10. Mathematical Foundations of Geometric Deep Learning by Haitz Saez de Ocariz Borde and Michael Bronstein
• Dives into the the key math concepts behind geometric Deep Learning: geometric and analytical structures, vector calculus, differential geometry and others.
• https://arxiv.org/abs/2508.02723
• https://arxiv.org/pdf/2508.02723