6 Must-read books about AI and Machine Learning:
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6 Must-read books about AI and Machine Learning:
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Sharing some free, useful resources for you. In this collection, we’ve gathered the most recent books to give you up-to-date information on key fundamental topics. Hope this helps you master AI and machine learning:
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Sharing some free, useful resources for you. In this collection, we’ve gathered the most recent books to give you up-to-date information on key fundamental topics. Hope this helps you master AI and machine learning:
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1. Machine Learning Systems by Vijay Janapa Reddi → 
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1. Machine Learning Systems by Vijay Janapa Reddi → 
https://www.mlsysbook.ai/
https://www.mlsysbook.ai/
link
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Provides a framework for building effective ML solutions, covering data engineering, optimization, hardware-aware training, inference acceleration, architecture choice, and other key principles
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Provides a framework for building effective ML solutions, covering data engineering, optimization, hardware-aware training, inference acceleration, architecture choice, and other key principles
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2. Generative Diffusion Modeling: A Practical Handbook by Zihan Ding, Chi Jin → 
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2. Generative Diffusion Modeling: A Practical Handbook by Zihan Ding, Chi Jin → 
https://arxiv.org/abs/2412.17162
https://arxiv.org/abs/2412.17162
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Offers a unified view of diffusion models: probabilistic, score-based, consistency, rectified flow, pre/post-training. It aligns notations with code to close the “paper-to-code” gap.
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Offers a unified view of diffusion models: probabilistic, score-based, consistency, rectified flow, pre/post-training. It aligns notations with code to close the “paper-to-code” gap.
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3. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges → 
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3. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges → 
https://arxiv.org/abs/2104.13478
https://arxiv.org/abs/2104.13478
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Explores unified geometric principles to analyze neural networks' architectures: CNNs, RNNs, GNNs, Transformers, and guide the design of the future ones
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Explores unified geometric principles to analyze neural networks' architectures: CNNs, RNNs, GNNs, Transformers, and guide the design of the future ones
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4. Mathematical Foundations of Geometric Deep Learning by Haitz Saez de Ocariz Borde and Michael Bronstein → 
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4. Mathematical Foundations of Geometric Deep Learning by Haitz Saez de Ocariz Borde and Michael Bronstein → 
https://arxiv.org/abs/2508.02723
https://arxiv.org/abs/2508.02723
link
text
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Dives into the the key math concepts behind geometric Deep Learning: geometric and analytical structures, vector calculus, differential geometry, etc.
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Dives into the the key math concepts behind geometric Deep Learning: geometric and analytical structures, vector calculus, differential geometry, etc.
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5. Interpretable Machine Learning by Christoph Molnar → 
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5. Interpretable Machine Learning by Christoph Molnar → 
https://github.com/christophM/interpretable-ml-book
https://github.com/christophM/interpretable-ml-book
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Practical guide to simple, transparent models (e.g., decision trees) and model-agnostic methods like LIME, Shapley values, permutation importance, and accumulated local effects.
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Practical guide to simple, transparent models (e.g., decision trees) and model-agnostic methods like LIME, Shapley values, permutation importance, and accumulated local effects.
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6. Understanding Deep Learning by Simon J.D. Prince → 
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6. Understanding Deep Learning by Simon J.D. Prince → 
https://udlbook.github.io/udlbook/
https://udlbook.github.io/udlbook/
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Explores core deep learning concenpts: models, training, evaluation, RL, architectures for images, text, and graphs, addressing open theoretical questions
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Explores core deep learning concenpts: models, training, evaluation, RL, architectures for images, text, and graphs, addressing open theoretical questions
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Also, subscribe to the Turing Post: 
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Also, subscribe to the Turing Post: 
https://www.turingpost.com/subscribe
https://www.turingpost.com/subscribe
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6 Must-read books about AI and Machine Learning:

Sharing some free, useful resources for you. In this collection, we’ve gathered the most recent books to give you up-to-date information on key fundamental topics. Hope this helps you master AI and machine learning:

1. Machine Learning Systems by Vijay Janapa Reddi → https://www.mlsysbook.ai/
Provides a framework for building effective ML solutions, covering data engineering, optimization, hardware-aware training, inference acceleration, architecture choice, and other key principles

2. Generative Diffusion Modeling: A Practical Handbook by Zihan Ding, Chi Jin → https://arxiv.org/abs/2412.17162
Offers a unified view of diffusion models: probabilistic, score-based, consistency, rectified flow, pre/post-training. It aligns notations with code to close the “paper-to-code” gap.

3. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges → https://arxiv.org/abs/2104.13478
Explores unified geometric principles to analyze neural networks' architectures: CNNs, RNNs, GNNs, Transformers, and guide the design of the future ones

4. Mathematical Foundations of Geometric Deep Learning by Haitz Saez de Ocariz Borde and Michael Bronstein → https://arxiv.org/abs/2508.02723 
Dives into the the key math concepts behind geometric Deep Learning: geometric and analytical structures, vector calculus, differential geometry, etc.

5. Interpretable Machine Learning by Christoph Molnar → https://github.com/christophM/interpretable-ml-book
Practical guide to simple, transparent models (e.g., decision trees) and model-agnostic methods like LIME, Shapley values, permutation importance, and accumulated local effects.

6. Understanding Deep Learning by Simon J.D. Prince → https://udlbook.github.io/udlbook/
Explores core deep learning concenpts: models, training, evaluation, RL, architectures for images, text, and graphs, addressing open theoretical questions

Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
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