Books

Game producers already have most of what AI collaboration requires. They have been managing context, decomposing work, and evaluating output for years. This book is the map from where they are to where the work is going.

Most developers use AI coding tools like better autocomplete. The 10x Developer's Cursor Playbook shows you how to use Cursor for agent workflows, refactoring, MCP integrations, collaboration and security so you can ship faster, write better code and get more out of AI.

As AI adoption accelerates, attacks such as prompt injection, jailbreaks, data poisoning, and agent exploitation are redefining cybersecurity. This book explains why these attacks work and provides practical strategies for building secure, resilient AI systems.

From LLMs and RAG to Memory, MCP, Agent Frameworks and Enterprise AI Controls—discover how modern AI Agents work internally within today's rapidly evolving AI ecosystem.

Most AI systems can talk, but few can actually do. This book shows you how to build AI agents that reliably use tools, call APIs and automate real workflows. Using DSPy, Pydantic AI, the Claude Agent SDK, the OpenAI Agents SDK and Google ADK, you'll learn practical patterns for building reliable agents that work in production.

There are no magic prompts to test AI. What you can do is give AI real work under realistic conditions and observe what happens - exactly the kind of evaluation now required under the EU AI Act.

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 Machine Learning. Build Real Projects. Launch Your AI Career.Machine Learning is transforming the world—and Python is the language powering that revolution.Mastering Machine Learning with Python: From Beginner to Pro provides a complete roadmap for understanding, implementing, and deploying modern machine learning solutions.Inside this book, you'll discover:✔ Artificial Intelligence and Machine Learning Fundamentals✔ Data Preprocessing and Feature Engineering✔ Python for Machine Learning✔ Regression and Classification Algorithms✔ Clustering and Dimensionality Reduction✔ Model Evaluation and Hyperparameter Tuning✔ Ensemble Learning Techniques✔ Neural Networks and Deep Learning✔ TensorFlow and Keras Development✔ Real-World Machine Learning Projects✔ Flask and Streamlit Deployment✔ Introduction to MLOps and Production AIFrom your first machine learning model to deploying intelligent applications, this book delivers the practical knowledge and hands-on experience needed to become an AI and Machine Learning professional.Whether you're a student, developer, data analyst, researcher, or career changer, this book will help you transform data into intelligent solutions and ideas into impactful applications.

Language is data. Mathematics is the engine that makes machines understand it.Discover the mathematical foundations behind modern Natural Language Processing and Artificial Intelligence.Inside this book, you will learn:✔ Vector Space Models and Text Representation✔ Linear Algebra for Language Processing✔ Probability Theory and Statistical NLP✔ n-Gram Language Models and Smoothing Techniques✔ Word2Vec, GloVe, and FastText Embeddings✔ Matrix Factorization and Latent Semantic Analysis✔ Contextual Representations with ELMo and BERT✔ Hidden Markov Models and Probabilistic Grammars✔ Topic Modeling with Latent Dirichlet Allocation (LDA)✔ Optimization and Neural Language Models✔ Mathematical Foundations of GPT and Large Language Models✔ Ethical Challenges and Bias in NLP SystemsWhether you are a student beginning your NLP journey or a researcher exploring advanced language models, this book provides the mathematical intuition and practical understanding needed to succeed in the rapidly evolving field of Natural Language Processing.Move beyond coding. Understand the mathematics that powers intelligent language systems.

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.

The future of Artificial Intelligence is connected.From social networks and recommendation engines to autonomous vehicles and cybersecurity systems, modern AI increasingly relies on understanding relationships rather than isolated data points.How do Graph Neural Networks learn from complex networks?How do recommendation systems predict user preferences?How can AI detect fraud, misinformation, and cyber threats using graph structures?How will future Graph Foundation Models transform machine intelligence?Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-2) provides a comprehensive guide to the technologies driving the next generation of AI.Explore Graph Neural Networks, graph embeddings, knowledge graphs, explainable AI, distributed graph learning, and cutting-edge research topics that are reshaping artificial intelligence.Whether you are a student, researcher, educator, or AI professional, this book will help you understand how intelligent systems learn from relationships, networks, and connected data.Learn the science behind Graph AI. Build the intelligence behind tomorrow's connected world.

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.

What is intelligence?At its core, intelligence is the ability to process information.But what exactly is information?How do neural networks compress knowledge?Why does cross-entropy dominate machine learning?How do Variational Autoencoders learn latent representations?What role does entropy play in regularization, generalization, and modern AI systems?Information Theory and Artificial Intelligence: Entropy, Coding, Regularization, and Generative Models (Volume I) provides a comprehensive exploration of the mathematical foundations that power modern artificial intelligence.Inside this volume, you will discover:✓ Shannon Entropy and Measures of Information✓ Mutual Information and Feature Learning✓ Cross-Entropy and Machine Learning Loss Functions✓ Kullback–Leibler (KL) Divergence✓ Source Coding and Data Compression✓ Huffman, Arithmetic, and Lempel–Ziv Coding✓ Channel Capacity and Communication Limits✓ Information-Theoretic Learning Principles✓ Information Bottleneck Theory✓ Entropy-Based Regularization✓ Neural Networks as Information Processing Systems✓ Error-Correcting Codes and AI Robustness✓ Neural Communication Systems✓ Variational Inference and Variational Autoencoders (VAEs)Whether you are a student, researcher, educator, or AI professional, this book provides the conceptual and mathematical foundation necessary to understand how information drives learning, intelligence, and modern AI systems.Learn not just how AI works—but why it works.

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.