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Books to Master AI Engineering in 2026
16
items This is a curated list of the books that shaped my journey toward becoming an AI engineer.
I have personally read all of these books, and some of them are resources I still return to to this day, whenever I need to revisit a concept.
I selected them...
Sections
3
Must-Have Books
Specialized & Nice-to-Have Books
Other Recommendations [Free Books]
Must-Have Books
These are the books I consider essential for anyone thinking about mastering AI engineering.
If you want a solid mathematical foundation for machine learning, and trust me, you need one, then this is an essential book to have.
Many beginners jump directly into algorithms and frameworks without understanding the mathematics behind them, but this book fills that gap by explaining the key concepts of linear algebra, calculus, probability, and optimization in an accessible way...
What I appreciate most is that it focuses on the intuition behind the mathematics rather than treating it as a collection of abstract formulas.
It won't make you a mathematician, but it will give you the tools to understand why machine learning models work the way they do.
Many beginners jump directly into algorithms and frameworks without understanding the mathematics behind them, but this book fills that gap by explaining the key concepts of linear algebra, calculus, probability, and optimization in an accessible way...
What I appreciate most is that it focuses on the intuition behind the mathematics rather than treating it as a collection of abstract formulas.
It won't make you a mathematician, but it will give you the tools to understand why machine learning models work the way they do.
Mathematics for Machine Learning [Free]
This is one of the most practical machine learning books I've worked through.
What I appreciated most is how it bridges the gap between the familiar Machine Learning (ML) library Scikit-Learn and modern Deep Learning (DL) with PyTorch, making the transition feel natural rather than overwhelming.
Few years go, I would've recommended another book that covers (Scikit-Learn + TensorFlow & Keras), but I no longer think that's the best path for learning ML & DL.
PyTorch has become the dominant framework in research and industry, making it the better choice for most aspiring AI engineers.
Whether you're a student, an AI practitioner, or revisiting the fundamentals, this book is an excellent reference to have on your shelf.
Official GitHub repository containing the book's code: github.com/rasbt/machine-learning-book
What I appreciated most is how it bridges the gap between the familiar Machine Learning (ML) library Scikit-Learn and modern Deep Learning (DL) with PyTorch, making the transition feel natural rather than overwhelming.
Few years go, I would've recommended another book that covers (Scikit-Learn + TensorFlow & Keras), but I no longer think that's the best path for learning ML & DL.
PyTorch has become the dominant framework in research and industry, making it the better choice for most aspiring AI engineers.
Whether you're a student, an AI practitioner, or revisiting the fundamentals, this book is an excellent reference to have on your shelf.
Official GitHub repository containing the book's code: github.com/rasbt/machine-learning-book
Machine Learning with PyTorch and Scikit-Learn
If you want to learn deep learning the "old way" by starting with Keras and building a strong practical foundation, this is definitely the book you want to get. After all, what better way to learn Keras than from the creator of Keras himself?
I cannot recommend it enough, as it was my favorite resource when I was learning Deep Learning. I personally have the second edition (released in 2021), and it was one of the books that helped me truly understand the fundamentals of neural networks a...
The third edition is a major update that brings the book into the modern AI era. It introduces Keras 3 with multi-backend support across TensorFlow, PyTorch, and JAX, while adding new chapters on Transformers, building GPT-like language models from s...
Also, the book is available to read for free on the official website: deeplearningwithpython.io
I cannot recommend it enough, as it was my favorite resource when I was learning Deep Learning. I personally have the second edition (released in 2021), and it was one of the books that helped me truly understand the fundamentals of neural networks a...
The third edition is a major update that brings the book into the modern AI era. It introduces Keras 3 with multi-backend support across TensorFlow, PyTorch, and JAX, while adding new chapters on Transformers, building GPT-like language models from s...
Also, the book is available to read for free on the official website: deeplearningwithpython.io
Deep Learning with Python, Third Edition
What is truly amazing about this book is that it provides clear explanations without hiding the difficult parts.
Instead of simplifying concepts to the point where important details are lost, Sebastian Raschka takes the time to explain the real mechanics behind Large Language Models while keeping the learning process approachable.
If you want to truly understand how LLMs work under the hood, this is one of the best books you can get. This book takes you step by step through building a GPT-like model from scratch, covering tokenization, embeddings, attention mechanisms, transfo...
Sebastian Raschka has a great ability to balance theoretical concepts with practical implementation, allowing you to connect the mathematical ideas with actual code.
If I had to recommend only one resource for understanding LLMs, this would be the one I would choose.
More information about this book and additional complementary materials are available on the author's official website: sebastianraschka.com/llms-from-scratch
Instead of simplifying concepts to the point where important details are lost, Sebastian Raschka takes the time to explain the real mechanics behind Large Language Models while keeping the learning process approachable.
If you want to truly understand how LLMs work under the hood, this is one of the best books you can get. This book takes you step by step through building a GPT-like model from scratch, covering tokenization, embeddings, attention mechanisms, transfo...
Sebastian Raschka has a great ability to balance theoretical concepts with practical implementation, allowing you to connect the mathematical ideas with actual code.
If I had to recommend only one resource for understanding LLMs, this would be the one I would choose.
More information about this book and additional complementary materials are available on the author's official website: sebastianraschka.com/llms-from-scratch
Build a Large Language Model (From Scratch)
If you want to become an LLM specialist who goes beyond the basics and theory to building real LLM-powered systems, this is a book you definitely need in your library.
One of the biggest reasons I was excited to read this book, besides it being highly recommended, is Jay Alammar.
I've been a faithful reader of his blog for years and a real fan of his visual, intuitive approach to explaining complex AI concepts. Combined with Maarten Grootendorst's expertise, the result is one of the most approachable and practical books on LL...
This book is a great bridge between understanding LLMs and actually building useful AI systems with them. This is definitely a must-have.
Official GitHub repository containing the book's code:: github.com/handsOnLLM/Hands-On-Large-Language-Models
One of the biggest reasons I was excited to read this book, besides it being highly recommended, is Jay Alammar.
I've been a faithful reader of his blog for years and a real fan of his visual, intuitive approach to explaining complex AI concepts. Combined with Maarten Grootendorst's expertise, the result is one of the most approachable and practical books on LL...
This book is a great bridge between understanding LLMs and actually building useful AI systems with them. This is definitely a must-have.
Official GitHub repository containing the book's code:: github.com/handsOnLLM/Hands-On-Large-Language-Models
Hands-On Large Language Models: Language Understanding
Unlike traditional machine learning & deep learning, where the focus was often on training models from scratch, modern AI engineering is about building reliable, and scalable systems around powerful existing models.
This is exactly what this book is about. I've It is one of the most valuable resources I've found for understanding the challenges, and best practices behind modern AI engineering.
What I appreciate most about Chip Huyen's book, is her focus on the decisions that happen beyond the model itself, like how to evaluate AI systems, handle trade-offs, reduce costs and latency, choose the right architecture, and design applications th...
Keep in mind: This doesn't teach you how to build a demo, but rather how to think about building AI products.
For anyone who wants to move from experimenting with AI models to engineering real-world AI systems, this is one of the most valuable books available today.
Official Github repository: github.com/chiphuyen/aie-book
This is exactly what this book is about. I've It is one of the most valuable resources I've found for understanding the challenges, and best practices behind modern AI engineering.
What I appreciate most about Chip Huyen's book, is her focus on the decisions that happen beyond the model itself, like how to evaluate AI systems, handle trade-offs, reduce costs and latency, choose the right architecture, and design applications th...
Keep in mind: This doesn't teach you how to build a demo, but rather how to think about building AI products.
For anyone who wants to move from experimenting with AI models to engineering real-world AI systems, this is one of the most valuable books available today.
Official Github repository: github.com/chiphuyen/aie-book
AI Engineering: Building Apps with Foundation Models
Specialized & Nice-to-Have Books
These books aren't required reading, but each offers something special.
The field of interpretability has always been one of the most fascinating areas of machine learning to me.
From the moment I started building models, I found myself wondering how they were able to uncover meaningful patterns and make accurate predictions. Naturally, I wanted to understand why they reached those predictions instead of treating them as blac...
This book does an excellent job of answering those questions by introducing a wide range of interpretability techniques, from feature importance and SHAP to feature and saliency maps, and many others, all with clear explanations and practical example...
One thing I particularly appreciate is that the book doesn't just teach you how to use interpretability tools, it also discusses their strengths and limitations, with clear visuals and explanations.
If you're interested in understanding what goes on inside some of the machine learning models rather than simply training them, I highly recommend adding this book to your library.
From the moment I started building models, I found myself wondering how they were able to uncover meaningful patterns and make accurate predictions. Naturally, I wanted to understand why they reached those predictions instead of treating them as blac...
This book does an excellent job of answering those questions by introducing a wide range of interpretability techniques, from feature importance and SHAP to feature and saliency maps, and many others, all with clear explanations and practical example...
One thing I particularly appreciate is that the book doesn't just teach you how to use interpretability tools, it also discusses their strengths and limitations, with clear visuals and explanations.
If you're interested in understanding what goes on inside some of the machine learning models rather than simply training them, I highly recommend adding this book to your library.
Interpretable Machine Learning
If you've ever watched Josh Starmer's StatQuest videos on YouTube, you already know what makes this book special.
He has a remarkable ability to take topics that initially seem intimidating and break them down into clear, intuitive explanations without sacrificing the underlying ideas.
This book follows that same philosophy, combining concise explanations with colorful illustrations that make even complex machine learning concepts much easier to grasp.
What I like most is that the book focuses on building intuition rather than overwhelming you with mathematical notation. It's not intended to be a rigorous textbook or a comprehensive reference, but rather a companion that helps concepts "click" befo...
So, whether you're just getting started or need a refresher on a particular algorithm, I think it's one of the most approachable and enjoyable machine learning books you can add to your library.
He has a remarkable ability to take topics that initially seem intimidating and break them down into clear, intuitive explanations without sacrificing the underlying ideas.
This book follows that same philosophy, combining concise explanations with colorful illustrations that make even complex machine learning concepts much easier to grasp.
What I like most is that the book focuses on building intuition rather than overwhelming you with mathematical notation. It's not intended to be a rigorous textbook or a comprehensive reference, but rather a companion that helps concepts "click" befo...
So, whether you're just getting started or need a refresher on a particular algorithm, I think it's one of the most approachable and enjoyable machine learning books you can add to your library.
StatQuest Illustrated Guide To Machine Learning
Computer vision was the field that first sparked my interest in deep learning. I was fascinated by the algorithms and neural network architectures that enabled computers to understand and interpret visual information, and this book does an excellent ...
Rather than assuming you already have a strong background in deep learning, Mohamed Elgendy gradually builds the necessary foundations before moving into core computer vision topics like transfer learning, object detection, GANs, and visual embedding...
I think that's one of the book's biggest strengths.
Another aspect I really like is its practical focus. The book doesn't stop at explaining the theory behind convolutional neural networks and modern vision models, it also demonstrates how they're applied to solve real problems.
If your goal is to build a solid understanding of deep learning for computer vision while gaining practical skills along the way, I think this book is an excellent addition to your library.
Rather than assuming you already have a strong background in deep learning, Mohamed Elgendy gradually builds the necessary foundations before moving into core computer vision topics like transfer learning, object detection, GANs, and visual embedding...
I think that's one of the book's biggest strengths.
Another aspect I really like is its practical focus. The book doesn't stop at explaining the theory behind convolutional neural networks and modern vision models, it also demonstrates how they're applied to solve real problems.
If your goal is to build a solid understanding of deep learning for computer vision while gaining practical skills along the way, I think this book is an excellent addition to your library.
Deep Learning for Vision Systems
If you're like me, someone who loves learning through visual explanations, this is one of the most enjoyable AI books you can own.
Welch Labs has an incredible talent for turning complex ideas into intuitive concepts, and this book is a clear manifestation of that.
Through beautifully designed illustrations and clear explanations, it covers topics like gradient descent, back-propagation, neural networks, transformers, and many other AI concepts in a way that's both engaging and easy to follow.
This isn't meant to replace a comprehensive textbook or teach you every mathematical detail. Instead, its greatest strength is helping you develop intuition.
I genuinely believe that having a strong mental picture of how these systems work makes learning the technical details much easier later on.
It's an excellent companion to more traditional AI books, and one I would happily recommend to anyone interested in the field.
Welch Labs has an incredible talent for turning complex ideas into intuitive concepts, and this book is a clear manifestation of that.
Through beautifully designed illustrations and clear explanations, it covers topics like gradient descent, back-propagation, neural networks, transformers, and many other AI concepts in a way that's both engaging and easy to follow.
This isn't meant to replace a comprehensive textbook or teach you every mathematical detail. Instead, its greatest strength is helping you develop intuition.
I genuinely believe that having a strong mental picture of how these systems work makes learning the technical details much easier later on.
It's an excellent companion to more traditional AI books, and one I would happily recommend to anyone interested in the field.
The Welch Labs Illustrated Guide to AI
This book takes you back to the fundamentals by showing you how neural networks actually work under the hood, , starting from first principles.
I like the teaching style of Andrew Trask, he has a knack for breaking down complex ideas into small, manageable steps, making concepts that often feel intimidating much more approachable.
While some of the later chapters become more demanding, I think the journey is well worth it.
If you've ever wanted to truly understand why neural networks learn, rather than just how to train them with PyTorch or TensorFlow, this is an excellent book to add to your library.
I like the teaching style of Andrew Trask, he has a knack for breaking down complex ideas into small, manageable steps, making concepts that often feel intimidating much more approachable.
While some of the later chapters become more demanding, I think the journey is well worth it.
If you've ever wanted to truly understand why neural networks learn, rather than just how to train them with PyTorch or TensorFlow, this is an excellent book to add to your library.
Grokking Deep Learning
Other Recommendations [Free Books]
These are some of the best top-tier AI books you can read for free.
If you are looking for a deeper theoretical understanding of deep learning, this is one of the best free resources available.
Understanding Deep Learning goes beyond simply teaching you how to use frameworks and focuses on explaining the fundamental ideas behind modern neural networks, including optimization, architectures, representation learning, and generative models.
Simon J.D. Prince manages to present rigorous concepts while keeping the explanations accessible.
The fact that the entire book is freely available makes it an incredible resource for students and anyone serious about building a strong foundation in deep learning.
Understanding Deep Learning goes beyond simply teaching you how to use frameworks and focuses on explaining the fundamental ideas behind modern neural networks, including optimization, architectures, representation learning, and generative models.
Simon J.D. Prince manages to present rigorous concepts while keeping the explanations accessible.
The fact that the entire book is freely available makes it an incredible resource for students and anyone serious about building a strong foundation in deep learning.
Understanding Deep Learning
This booklet packs an incredible amount of machine learning knowledge into a surprisingly concise format. Andriy Burkov has a talent for distilling complex topics into clear, practical explanations without sacrificing the core ideas.
I wouldn't recommend it as your very first machine learning book, but once you have some basic familiarity with the field, it becomes an outstanding resource for connecting the dots and reinforcing your understanding.
It's also one of those books you'll find yourself revisiting whenever you need a quick refresher on an algorithm or concept.
As a bonus, Andriy Burkov makes the book available under a "read-first, buy-later" model, allowing anyone to read it for free before deciding whether to purchase a copy.
Official book website: themlbook.com
I wouldn't recommend it as your very first machine learning book, but once you have some basic familiarity with the field, it becomes an outstanding resource for connecting the dots and reinforcing your understanding.
It's also one of those books you'll find yourself revisiting whenever you need a quick refresher on an algorithm or concept.
As a bonus, Andriy Burkov makes the book available under a "read-first, buy-later" model, allowing anyone to read it for free before deciding whether to purchase a copy.
Official book website: themlbook.com
The Hundred-Page Machine Learning Book
If I had to recommend just one free deep learning textbook, Dive into Deep Learning (D2L) would be the one without a doubt.
This book offers a rare combination of solid theoretical explanations, hands-on coding, and interactive notebooks, making it an excellent resource for both beginners and experienced practitioners.
The book is continuously updated, freely available online, and supports multiple deep learning frameworks, making it one of the most comprehensive free learning resources in AI.
If you're serious about learning deep learning, there's little reason not to add this to your study plan.
Official website: d2l.ai
This book offers a rare combination of solid theoretical explanations, hands-on coding, and interactive notebooks, making it an excellent resource for both beginners and experienced practitioners.
The book is continuously updated, freely available online, and supports multiple deep learning frameworks, making it one of the most comprehensive free learning resources in AI.
If you're serious about learning deep learning, there's little reason not to add this to your study plan.
Official website: d2l.ai
Dive into Deep Learning
I am not a specialist in RL, I'have just read a few chapters of this book a few years ago, when I wanted to develop a basic understanding of the field. Written by Richard Sutton and Andrew Barto, two of the pioneers of the field, this book has become...
Don't expect a programming book, though. The focus is on the underlying concepts, algorithms, and intuition rather than code. One thing I particularly like is how the authors strategically place examples and exercises throughout the book. They encour...
To get the most out of the book, I highly recommend implementing some of the algorithms yourself, such as the k-armed bandit testbed, Monte Carlo methods, and Temporal-Difference learning. That extra hands-on practice makes the concepts much easier t...
Don't expect a programming book, though. The focus is on the underlying concepts, algorithms, and intuition rather than code. One thing I particularly like is how the authors strategically place examples and exercises throughout the book. They encour...
To get the most out of the book, I highly recommend implementing some of the algorithms yourself, such as the k-armed bandit testbed, Monte Carlo methods, and Temporal-Difference learning. That extra hands-on practice makes the concepts much easier t...
Reinforcement Learning: An Introduction
Although I didn't have this book when I was learning NLP, I would absolutely recommend it to anyone starting today.
For a resource that is freely available, the amount of knowledge it contains is simply remarkable. It is comprehensive, well-organized, and does an excellent job of explaining both the theory and the intuition behind natural language processing, comp...
The only limitation I noticed is that, like most textbooks, it can't fully keep pace with the rapid evolution of AI. While it covers the foundations and transformer-based language models exceptionally well, it doesn't cover some of the latest LLM arc...
That said, if your goal is to build a deep understanding of NLP, this is one of the best free books you can read, and an outstanding reference to keep coming back to.
For a resource that is freely available, the amount of knowledge it contains is simply remarkable. It is comprehensive, well-organized, and does an excellent job of explaining both the theory and the intuition behind natural language processing, comp...
The only limitation I noticed is that, like most textbooks, it can't fully keep pace with the rapid evolution of AI. While it covers the foundations and transformer-based language models exceptionally well, it doesn't cover some of the latest LLM arc...
That said, if your goal is to build a deep understanding of NLP, this is one of the best free books you can read, and an outstanding reference to keep coming back to.
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, with Language Models