Data Science

A book about how to be a scientist the modern, open-source way.

This book describes the algorithms and procedures used to fit statistical models to data. The material covered is taught in the Advanced Statistical Computing course in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.

This book introduces the topic of Developing Data Products in R. A data product is the ideal output of a Data Science experiment. This book is based on the Coursera Class "Developing Data Products" as part of the Data Science Specialization. Particular emphasis is paid to developing Shiny apps and interactive graphics.

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 all use data every day, often without realizing it. The Data Mind reveals how paying attention to patterns, asking better questions, and thinking more consciously can change the way we make decisions, understand ourselves, and see the world around us.

Turn analysis into action. Learn to design professional charts, interactive dashboards, and executive reports that communicate insight, reveal business performance, and help decision-makers understand what matters most.

Move beyond spreadsheets and think like a BI professional. Learn Power Pivot, star schema design, and DAX to build relational data models, dynamic measures, and scalable analytical solutions that power modern business intelligence.

Most Excel books teach features. This book teaches analytical thinking. Learn how to structure data, design professional workbooks, and build reliable analytical solutions while mastering the MO-210 Excel Associate curriculum. The essential foundation for every aspiring data, business, and financial analyst.

Move beyond the API. Dismantle the AI black box and build generative engines from scratch with pure Python and NumPy. Master the profound geometric principles and applied mathematics driving LLMs and Transformers. Transform from a mere consumer into an elite AI innovator by writing the core mathematical architecture yourself—no shortcuts, no frameworks, just pure engineering excellence.

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。

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.

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.

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.