Resources
Discover useful tools, tutorials, and helpful resources organized by category
Deep Learning
Deep Learning is the MIT Press textbook by Ian Goodfellow, Yoshua Bengio and Aaron Courville. The authors describe it as a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The complete book is available online for free and the authors state that it will remain so; the site also provides the table of contents, exercises, lectures, errata and the notation used in the book. It is worth a place on the shelf of anyone who wants the mathematical background behind the libraries they use.
Dive into Deep Learning
Dive into Deep Learning is an interactive deep learning book with code, math and discussions, written by Aston Zhang, Zack C. Lipton, Mu Li and Alex J. Smola with additional chapter authors. It is implemented in PyTorch, NumPy/MXNet, JAX and TensorFlow, and each section is an executable Jupyter notebook, so readers can change the code and tune hyperparameters to see the effect at once. Later additions cover reinforcement learning, Gaussian processes, hyperparameter optimization and transformers for vision and large-scale pretraining. The site says the book has been adopted at 500 universities from 70 countries. A fit for developers who want theory and runnable code side by side.
Neural Networks and Deep Learning
Neural Networks and Deep Learning is a practical and approachable introduction to how modern neural networks work, written with a strong focus on intuition rather than heavy theory. The book walks readers through the core ideas behind deep learning — including perceptrons, backpropagation, gradient descent, overfitting, convolutional networks, and recurrent networks — while gradually building real implementations from scratch in Python. Instead of treating AI as a black box, it explains why neural networks learn, how training actually happens, and what design choices improve performance, making it especially valuable for programmers and self-learners who want to understand the foundations behind today’s AI systems without needing an advanced mathematics background.
Language Models Interview Handbook
This book is a practical guide to understanding and preparing for large language model (LLM) engineering roles, focusing not just on theory but on how LLMs work as complete systems in real-world applications. It walks through the full stack—from tokenization, embeddings, and attention to retrieval (RAG), evaluation, deployment, and optimization—while emphasizing how these components interact to deliver reliable, scalable AI products. Rather than memorizing concepts, the book trains readers to think like engineers: framing problems, making trade-offs between cost, latency, and accuracy, and explaining design decisions clearly in interviews. It also covers common patterns like hybrid search, reranking, and context management, along with career guidance such as resume building and communicating impact, ultimately aiming to help readers bridge the gap between LLM theory and production-ready systems.
Understanding Deep Learning
Understanding Deep Learning by Simon J. D. Prince is a comprehensive and accessible introduction to the core ideas behind modern deep learning. The book focuses on explaining why neural networks work rather than just how to implement them, gradually building from basic concepts like supervised learning and simple neural networks to more advanced topics such as deep architectures and generative models. With clear explanations and visual intuition, it aims to help readers develop a solid conceptual understanding of deep learning systems and the principles that power many of today’s AI applications.
Build a Large Language Model (From Scratch)
In Build a Large Language Model (From Scratch), Sebastian Raschka leads readers on a hands-on journey through designing, coding, training, and fine-tuning an entire large language model using publicly available tools. Beginning with planning and preparing your data, the book walks you through implementing the model architecture, loading pretrained weights, and adapting the model for tasks like text classification or instruction-following. Even if you’re working on a laptop, it shows how you can build a GPT-style LLM, while also discussing scaling options. With clear explanations, diagrams, code examples, and practical exercises, the book aims to demystify what goes on “inside” generative AI and help you really understand what makes these models work.
