Books

Master deep learning with PyTorch & Lightning. Core concepts, practical Q&A, and production-ready code with a full companion GitHub repository.

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.

Понятный иллюстрированный путеводитель по ключевым концепциям и практическим применениям больших языковых моделей. Идеально подходит для работы над проектами, подготовки к собеседованиям и самостоятельного обучения.

هذا الكتاب هو دليل مختصر وموضّح بالرسوم لأي شخص يرغب في فهم الآلية الداخلية للنماذج اللغوية الضخمة، سواء في سياق المقابلات أو المشاريع أو بدافع الفضول الشخصي.

A machine can hand you a grouping that is confident, stable, internally consistent, and seven times worse than the one it found a moment earlier. Nothing in the output says so.This book works k-means through completely on twelve bakery customers, small enough that every number is printed and checkable. You will build the method from nothing, see exactly where it succeeds, and see exactly where it fails silently.Fifty-eight figures, every one captured from a running implementation.

Build a world-class technical publishing brand with this complete guide to designing, structuring, and publishing professional Tech Capsules. Learn proven layouts, visual identity, content architecture, publishing workflows, and best practices used to create high-quality technical micro-books that readers lov

Every news feed is full of AI buzzwords: Transformers, tokens, embeddings, context windows, hallucinations, objective functions. Yet most explanations are either dense academic textbooks or empty marketing fluff. ☕ Coffee Break AI is your practical guide to AI. Written in plain English with warm real-world analogies. It breaks down the core mechanisms of AI into 40 bite-sized chapters.

We'll stick to five libraries, not because more would be a problem, but because keeping it simple shows how well we can organise things. When you download MNIST with just the standard library, you finally see what a dataset loader was hiding. If you write attention as four lines of NumPy before you ever call a PyTorch module, it's no longer a magic process but just plain arithmetic.

Deep learning is transforming the world, and this book provides a clear, practical path to mastering it. Through concise explanations and hands-on Python examples, you will learn to build, train, optimize, and deploy neural networks with confidence for real-world applications.

Make your Python AI code up to 100× faster using NumPy vectorization, Numba, parallel execution, and GPU acceleration. Learn through practical benchmarks and real-world optimization examples.

The latest version of Rust (1.85) has some great new features, like async closures, more stable associated function return types, and const generics that are now mature enough to underpin serious numerical libraries. The linfa and smartcore ecosystems have developed into decent classical machine learning stacks. The Burn training framework feels native to Rust, not like it's been ported from it. The Candle makes it so that loading pre-trained transformer models is more of an engineering task than a research exercise. The crates that used to need all sorts of workarounds now just work.

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。

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.