When first starting to learn AI, many people often follow a familiar path: copying code from tutorials and then running the model. This method produces results quite quickly, but it has a major problem — you might be using the model without really understanding what's happening behind the scenes.
According to the original article, the most important skill of an AI engineer is not being able to 'run the model,' but understanding why the model works, how to apply it to a real-world problem, and the limitations of each method.
That's also why quality AI books remain crucial in an age of short tutorials and quick learning videos. They help learners build a foundation in thinking, reasoning, and systems thinking in ways that many short courses struggle to do.
Below are five highly recommended free resources for anyone wanting to delve deeper into AI engineering.
1. Neural Networks and Deep Learning
Neural Networks and Deep Learning is one of the most suitable resources for anyone who wants to understand how neural networks actually work, rather than just calling APIs or using built-in libraries.
The book begins with fundamental concepts such as perceptrons and sigmoid neurons, then systematically explains how to build and train neural networks to recognize handwritten digits. One of its most valuable sections is how it explains backpropagation—the core mechanism that allows neural networks to learn from data.
Beyond theory, the book delves into techniques for improving models such as cost functions, regularization, weight initialization, and hyperparameter tuning. Numerous Python examples are included throughout the content so that readers can run them themselves and understand how everything connects.
The book's strength lies in its good balance between intuition and mathematics. If you already have a basic understanding of linear algebra or calculus, this is a very suitable choice to move past the 'using AI as a black box' phase and begin to understand what's going on beneath the model.
2. Deep Learning
Deep learning is often considered one of the "bibles" of modern deep learning.
Unlike many introductory materials, this book delves quite deeply into both the mathematical foundations and modern deep learning architectures. The content begins with linear algebra, probability, information theory, and numerical computation before moving on to practical machine learning and deep learning.
After the foundational section, the book goes on to explain feedforward networks, convolutional networks, recurrent networks, regularization, and optimization—key components found in many AI systems today.
One notable point is that the book doesn't just focus on implementation, but also discusses representation learning, probabilistic modeling, autoencoders, and generative learning extensively. This makes it more suitable for those who want to delve deeper into or work on more advanced AI projects, rather than simply being a beginner tutorial.
Because of the heavy mathematical content, this isn't a 'quick read' book, but it's an extremely valuable reference for AI engineers looking to build a long-term foundation.
3. Practical Deep Learning
While the first two books lean heavily towards theory, Practical Deep Learning focuses strongly on 'learning by doing'.
This free course is designed for those who already have basic programming knowledge and want to quickly build practical machine learning or deep learning models. The content uses modern tools such as Python, PyTorch, and the fastai library to guide you through the entire workflow, from data processing and model training to deployment.
The appeal of Practical Deep Learning lies in the fact that learners don't just read theory; they work directly with notebooks, datasets, and real-world problems. This makes the learning process much closer to a production environment.
The course also covers a wide range of different fields such as computer vision with CNN, NLP, recommendation systems, and tabular modeling. In addition, it includes modern content such as transfer learning, ResNet, attention, transformers, and latent diffusion.
Another valuable aspect is that Practical Deep Learning not only teaches how to train models but also focuses on how to deploy those models into real-world web applications, as well as optimizing the speed, accuracy, and stability of the system.
4. Artificial Intelligence: Foundations of Computational Agents
Artificial Intelligence: Foundations of Computational Agents approaches AI from the perspective of 'computational agents'—that is, systems capable of sensing, reasoning, learning, and acting in a real-world environment.
Interestingly, the book not only discusses modern machine learning but also explains many core principles of AI such as planning, reasoning, search algorithms, and decision-making.
The latest edition of the book has added more modern topics such as deep learning, causality, and social and ethical issues surrounding AI.
A major strength is that each chapter includes Python examples, case studies, and real-world scenarios to help readers understand not only 'what AI does,' but also 'why AI works that way.' The content spans from agent architecture, planning, and probabilistic reasoning to reinforcement learning and transformers.
This is a very suitable option for those who want a comprehensive and systematic perspective on AI instead of just learning individual models.
5. Ethical Artificial Intelligence
Not all AI literature focuses on modeling and coding. Ethical Artificial Intelligence takes a completely different approach: how to build AI that is safe and aligns with human values.
This is an increasingly important topic as AI becomes more powerful and capable of exhibiting unpredictable behavior.
This document analyzes the potential risks that could arise if AI optimizes goals in ways humans wouldn't expect. For example, AI could find a 'shortcut' to achieve rewards that inadvertently harms the system or manipulate its own reward mechanism.
Another interesting topic is self-delusion — situations where AI distorts its own observations or rewards to achieve a falsely optimal outcome.
The document also discusses utility function, how AI learns human values, self-modeling, and the long-term social impact of AI on politics, culture, and the future of humanity.
Although more academic than the other materials, this is still very much worth reading for modern AI engineers, as AI alignment and safety are increasingly becoming central topics in the industry.
Learning AI is not just about learning to code.
The most interesting aspect of this list is that it doesn't just focus on 'how to train models'. Some documents help build a mathematical and deep learning foundation. Some focus on practical implementation. Others expand to AI agents, reasoning, or ethical AI. Combined, they form a fairly comprehensive roadmap for AI engineers, from fundamentals to production and long-term industry issues.
In today's rapidly changing AI landscape, short tutorials can be helpful for getting started quickly, but it's these kinds of foundational documents that truly provide developers with in-depth understanding and long-term growth.
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