Books

Master language models through mathematics, illustrations, and code―and build your own from scratch!

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

Build GPT-2, Llama 3, and DeepSeek from scratch in PyTorch. Every chapter has runnable end-to-end code and loads real pretrained weights. Goes well past where most LLM tutorials stop.

Bridge AI and science with this hands-on guide. Whether you're a researcher learning ML or an engineer entering scientific applications, build real systems across chemistry, biology, physics & climate. Master Transformers, Diffusion Models & GNNs for scientific discovery. 500+ pages, 50+ Colab notebooks. Design molecules, predict proteins, accelerate climate models—all hands-on, zero setup required.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

Pedagogical Philosophy of the BookThis book is designed with three guiding principles:1.     Clarity over Formalism While maintaining mathematical accuracy, the book avoids unnecessary formalism that can confuse beginners. Instead, it uses intuitive explanations, diagrams, and real-world analogies.2.     Integration of Computation Every mathematical concept is tied to computational practice. Readers are encouraged to implement simple code snippets (in Python, NumPy, or similar tools) to reinforce their understanding.3.     Balance Between Breadth and Depth The book covers the essential calculus concepts in sufficient depth to support AI applications, without delving into overly abstract branches that have limited relevance to machine learning. Who Should Read This Book?·        Students of Computer Science, Data Science, and AI – who want to strengthen their mathematical foundation for advanced courses and projects.·        Researchers in AI – who need a refresher or structured guide to connect calculus with modern algorithms.·        Industry Professionals and Engineers – who want to move beyond using libraries like TensorFlow or PyTorch blindly and instead gain an understanding of the mathematics behind the models.·        Educators – who seek a resource that connects abstract mathematics with practical AI examples for teaching purposes.Benefits of Studying This Book1.     Builds Mathematical Confidence – Readers who once found calculus intimidating will discover a fresh, accessible perspective tailored for AI.2.     Enables Deeper Understanding of Algorithms – Going beyond “black box” usage of AI tools, readers will understand why models work.3.     Enhances Problem-Solving Skills – By mastering calculus-driven optimization, readers can design new models and improve existing ones.4.     Supports Academic and Career Growth – Mastery of calculus strengthens research capabilities, technical interviews, and advanced study opportunities.5.     Encourages Critical Thinking – Rather than rote memorization, the book fosters curiosity about the connections between mathematics and intelligent systems. The Long-Term VisionArtificial Intelligence is not just a passing trend—it is shaping the future of science, technology, and human society. Calculus, as a timeless branch of mathematics, ensures that learners have the intellectual tools to adapt to new paradigms. As AI expands into quantum computing, neuroscience-inspired architectures, and beyond, the reliance on calculus will remain unshaken.This book provides readers not just with knowledge, but with intellectual independence—the ability to reason about algorithms, derive insights, and innovate confidently.   

I wanted to understand how ChatGPT and other large language models (LLMs) really work, so I read a lot of books, watched YouTube videos, asked hundreds of questions, and wrote it all down. This book is the result. If you want to understand how large language models like ChatGPT actually work, from tokens and vectors to transformers and training, this book will explain it in a clear, approachable way.

A practical guide to fine-tuning Large Language Models (LLMs), offering both a high-level overview and detailed instructions on how to train these models for specific tasks.Get the paperback version here. Get the Kindle version here.

Revised for PyTorch 2.x! In 2019, I published a PyTorch tutorial on Towards Data Science and I was amazed by the reaction from the readers! Their feedback motivated me to write this book to help beginners start their journey into Deep Learning and PyTorch. I hope you enjoy reading this book as much as I enjoy writing it.

This book is a quick foray into the world of deep learning-based computer vision and abnormal equipment sound detection. The readers are introduced to the ease with which powerful equipment and product quality monitoring solutions can be built using sound and visual data.

AIエージェントの構築が、これほど容易だった時代はない。そして、実際に機能するものを作ることが、これほど難しい時代もない。本書は言語モデルの基礎から本番対応マルチエージェントシステムまで、失敗が起こる前に予測し、壊滅的な障害ではなく優雅な劣化を設計し、完全なアーキテクチャの所有権を確立するための深さをもって、あなたを導く。ペーパーバック版はamazonにて好評発売中。

Mastering Deep Learning with PyTorch: From Fundamentals to Real-World Projects This first edition delivers a complete end-to-end learning pathway for mastering modern deep learning using PyTorch. Major Topics Covered • Deep Learning Fundamentals• Artificial Neural Networks• PyTorch Framework and Tensor Operations• Automatic Differentiation (Autograd)• Feedforward Neural Networks• Convolutional Neural Networks (CNNs)• Recurrent Neural Networks (RNNs)• Long Short-Term Memory Networks (LSTMs)• Attention Mechanisms• Transformer Architectures• Hugging Face Ecosystem• Generative Adversarial Networks (GANs)• Computer Vision Applications• Natural Language Processing Applications• Model Evaluation and Optimization• Hyperparameter Tuning• Explainable Artificial Intelligence (XAI)• Ethical AI and Bias Mitigation• Model Deployment and Production Pipelines Practical Implementations Included • Image Classification Systems• Object Detection Models• Image Segmentation Applications• Text Classification Systems• Sentiment Analysis Models• Language Translation Pipelines• Transformer-Based NLP Applications• GAN-Based Image Generation Capstone Projects Project 1: Pneumonia Detection using CNNProject 2: Sentiment Analysis using LSTMProject 3: Image Colorization using GANProject 4: Real-Time Object Detection SystemProject 5: Transformer-Based Intelligent Chatbot Industry Tools and Technologies • PyTorch• TorchVision• Hugging Face Transformers• TensorBoard• Flask• ONNX• Docker Concepts• AWS Deployment Basics• Google Cloud Deployment Concepts Intended Audience • Undergraduate Students• Postgraduate Students• Data Scientists• Machine Learning Engineers• AI Researchers• Software Developers• Academic Professionals• Industry Practitioners Learning Outcomes Upon completion of this book, readers will be able to:• Design and train neural network architectures.• Build computer vision applications using CNNs.• Develop NLP solutions using RNNs, LSTMs, and Transformers.• Implement generative AI systems using GANs.• Evaluate and optimize deep learning models.• Deploy PyTorch models into production environments.• Understand ethical considerations in AI development.• Create portfolio-ready deep learning projects.This release establishes a strong foundation for academic learning, industrial applications, and advanced research in modern deep learning.

Learn Python. Build AI. Create the Future.What if you could write Python programs that generate content, answer questions, create code, summarize documents, and power intelligent applications?Python Simplified with Generative AI takes you on a complete journey from Python basics to advanced AI-powered development.Inside this book, you will learn:✔ Python Programming from Scratch✔ Data Structures and Object-Oriented Programming✔ AI and Machine Learning Foundations✔ Generative AI Concepts and Applications✔ Prompt Engineering Techniques✔ GPT-Powered Text Generation✔ AI Chatbots and Virtual Assistants✔ Image Generation with AI APIs✔ Flask, FastAPI, Streamlit, and Gradio Development✔ Real-World AI Projects for Your PortfolioWhether you are a student, professional developer, freelancer, educator, or entrepreneur, this book will help you transform ideas into intelligent applications and prepare for the next generation of software development.The future belongs to developers who can combine programming with artificial intelligence. Start building that future today.

Artificial Intelligence learns from information.But how do modern AI systems decide what information to keep and what to discard?Why do GANs generate realistic images?How do large language models compress knowledge?What role does entropy play in reinforcement learning?Can information theory explain intelligence itself?Information Theory and Artificial Intelligence: Entropy, Coding, Regularization, and Generative Models (Volume II) explores the advanced information-theoretic foundations behind today's most powerful AI systems.Inside this volume, you will discover:✓ Contrastive Learning and Representation Learning✓ InfoNCE Loss and Mutual Information Estimation✓ Self-Supervised Learning Architectures✓ Generative Adversarial Networks (GANs)✓ Wasserstein GANs and InfoGAN✓ Mode Collapse and GAN Stability✓ Entropy-Driven Reinforcement Learning✓ Soft Actor-Critic Frameworks✓ Fisher Information and Natural Gradients✓ Information Geometry for Neural Networks✓ Information Theory Behind Large Language Models✓ Token Entropy and Perplexity✓ Transformer Information Routing✓ Federated Learning Communication Constraints✓ Explainable AI Through Entropy and Mutual Information✓ Quantum Information Theory and AI✓ Information Bottleneck Theory✓ AI Fairness, Safety, Alignment, and Future Research ChallengesWhether you are a student, researcher, educator, or AI professional, this volume provides the mathematical and conceptual tools required to understand the information-processing principles that drive modern intelligent systems.Learn how information becomes intelligence.

Artificial Intelligence is powered by neural networks.But how do neural networks actually learn?How do systems recognize faces, understand speech, generate text, and create images?Neural Networks and Architectures: A Comprehensive Guide for Students provides a complete learning pathway from fundamental concepts to state-of-the-art deep learning architectures.Inside this book, you will discover:✓ Biological inspiration behind neural networks✓ Mathematical foundations of deep learning✓ Perceptrons and Multi-Layer Perceptrons (MLPs)✓ Forward Propagation and Backpropagation✓ Activation Functions and Regularization Techniques✓ Convolutional Neural Networks (CNNs)✓ Recurrent Neural Networks (RNNs), LSTM, and GRU✓ Autoencoders and Generative Adversarial Networks (GANs)✓ Transformer Architecture, BERT, and GPT✓ Optimization Algorithms and Model Training✓ Practical Projects using TensorFlow, PyTorch, and Keras✓ Ethical Considerations and Future Research DirectionsDesigned for students, educators, researchers, and AI enthusiasts, this book combines theory, mathematics, implementation, and applications into one comprehensive learning resource.Build a strong foundation in neural networks and prepare yourself for the future of Artificial Intelligence.