Books

Security threat modeling is expensive ($5K–$20K per engagement), slow (2–5 days), and requires rare expertise. What if you could automate it? This book shows you how to build AITM — an open-source Python tool that uses Large Language Models to generate comprehensive STRIDE threat analyses from a simple system description. One command. 30 seconds. Professional results. You'll build every component from scratch: - A stateless threat modeling engine with a 3-step AI workflow - Multi-provider LLM integration (Amazon Bedrock, OpenAI, Ollama) - Structured output parsing with Pydantic — no regex, no fragile parsing - Architecture diagram analysis using vision models - A professional CLI with progress indicators and colored output - Markdown and JSON report generation Whether you're a developer automating security reviews, a DevSecOps engineer integrating threat modeling into CI/CD, or a student learning how to build real-world LLM-powered tools — this book gives you the complete blueprint. Includes 5 real-world use cases, full source code on GitHub, and step-by-step instructions that work on macOS and Linux. No security expertise required. Just Python and curiosity.

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。

The first book that addresses engineering management specifically for the AI-native era. Classic EM books like The Manager's Path and An Elegant Puzzle predate agentic coding tools — this one starts where they left off. Every chapter produces a concrete artifact: a hiring rubric, a performance calibration guide, a budget template, a rollout plan. By the end, you have a complete management playbook for leading engineering teams where agents write most of the code.

A practical .NET/C# guide to building Genetic Algorithms from scratch and applying them to real-world optimization problems with 27 complete projects, visual demos, and full source code.

A fundamental architectural manifesto on AI behavioral safety and the transition from "word generation" to "state synchronization." Stop building "smart calculators"—start building reliable environments.

The secret to viral Instagram Reels isn't a complex camera setup or expensive video editors. It is pacing, psychological hooks, and high-retention scripting.

A hands-on, code-first journey from raw EEG signals to deep learning andself-supervised foundation models — with a runnable Colab notebook for everychapter.

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.

A 24-year game design veteran's field manual for putting generative AI to work in real production — with every prompt, every line of code, and every verification step disclosed.

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.