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.

Turn data into decisions. Data Visualization Using Tableau for Data Scientists shows how interactive visuals, analytics, and storytelling come together to make complex data understandable, actionable, and impactful.

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.

A powerful new book on Conformal Prediction by bestselling author and machine learning expert Valery Manokhin, bridging theory and real-world machine learning. Discover how to quantify uncertainty with statistical guarantees—across deep learning, time series, forecasting, and more. Preorder now before the price goes up.

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.

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.

Learn how to build your own AI application step-by-step. A hands-on guide to AI development with local LLM inference

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.

How should an AI system make decisions when information is incomplete?How can machines quantify uncertainty instead of merely producing predictions?How can intelligent systems continuously update their beliefs as new evidence emerges?The answer lies in Bayesian Mathematics.In Bayesian Mathematics for AI Decision Making, Anshuman Mishra explores the powerful framework that enables modern AI systems to reason probabilistically, model uncertainty, and make rational decisions in complex environments.From Bayesian inference and probabilistic programming to uncertainty-aware deep learning, reinforcement learning, healthcare diagnostics, robotics, and financial forecasting, this book reveals how Bayesian thinking is shaping the next generation of Artificial Intelligence.Learn how uncertainty becomes knowledge—and how probability becomes intelligence.

Artificial Intelligence is not only about neural networks and transformers. Behind many of the world's most influential AI systems lies a powerful probabilistic framework known as the Hidden Markov Model (HMM).From speech recognition and natural language processing to robotics, cybersecurity, finance, and bioinformatics, HMMs remain one of the most important sequence modeling techniques ever developed.This book takes readers on a complete journey through Markov Chains, probabilistic reasoning, Hidden Markov Models, Forward-Backward algorithms, Viterbi decoding, Baum-Welch training, and advanced HMM architectures.Designed for students, researchers, and AI professionals, the book combines rigorous mathematics with practical applications, making complex concepts accessible and immediately useful.If you want to truly understand how intelligent systems model uncertainty, learn from temporal patterns, and reason about hidden information, this book provides the foundation.

Can Hidden Markov Models predict financial market regimes?How are genes discovered from DNA sequences?How do robots determine their location in uncertain environments?Can probabilistic AI still compete with Transformers and Deep Learning?Volume-3 of Hidden Markov Models and AI answers these questions through real-world applications, Python implementations, industrial case studies, advanced projects, and future AI research.Explore how Hidden Markov Models are used in bioinformatics, cybersecurity, finance, robotics, autonomous systems, anomaly detection, and scientific discovery. Learn to build HMM systems from scratch, work with professional AI libraries, and understand the evolving relationship between probabilistic models and deep learning.This volume is designed for readers who want to move beyond theory and develop practical expertise in modern sequential artificial intelligence.

How do voice assistants understand speech?How does a chatbot track conversation context?How can machines identify speakers, translate languages, recognize named entities, and process sequential information?The answer lies in sequence modeling.In Volume-2 of Hidden Markov Models and AI, readers move beyond theory into practical applications of Hidden Markov Models in speech recognition, natural language processing, machine translation, speaker verification, conversational AI, and intelligent decision-making systems.Learn how modern AI systems transform speech signals and language sequences into meaningful intelligence using probabilistic models that continue to influence today's most advanced technologies.Whether you are an AI student, NLP researcher, speech engineer, or machine learning professional, this volume provides the practical knowledge required to master sequential learning systems.

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.

Every deep learning tutorial shows you the code. Almost none show you the idea. This is the book to read first - before the frameworks, before the maths notation, before the course you've already paid for. One example, followed all the way through, from a random guess to a network that works. By the end you'll understand what backpropagation actually does, why gradients matter, and what's really happening while your model trains. No calculus required.