Books

If you want to build real NLP applications instead of just experimenting with notebooks, spaCy is one of the best places to start. This book walks you through the entire journey from the fundamentals to advanced production workflows with practical explanations, real code examples and hands-on projects that show you how to build fast, reliable NLP systems for the real world.

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.

Large language models are changing the world, yet few people understand how they actually work. This book cuts through the hype, explaining the ideas behind modern AI with clarity, precision, and no unnecessary jargon.

PyTorch Deep Dive is a practical guide to mastering modern deep learning with PyTorch. From core concepts to advanced topics like transformers, diffusion models, and production deployment, it combines clear explanations, hands-on examples, and real-world best practices to help you build and scale AI applications with confidence.

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.

Run powerful AI models directly in the browser with no servers and no cloud dependencies. LiteRT.js teaches you how to build fast, private, and production-ready machine learning applications using WebAssembly, WebGPU, and WebNN, with practical examples and performance-focused techniques throughout.

This practical guide shows you how to build production-ready speech recognition applications from the ground up. Learn how to use Voice Activity Detection, choose the right ASR models, build real-time inference pipelines, and deploy scalable systems with hands-on examples and modern open-source tools.

Master DeepSeek V3 — one of the most capable AI models of its time.This practical guide teaches students, researchers, and professionals how to use DeepSeek V3 for learning, research, coding, content creation, automation, and productivity — with strong focus on prompt engineering and responsible AI usage.

Unlock the power of Llama 4 — the next generation of open-weight multimodal AI.This practical guide shows students, researchers, and professionals how to harness advanced AI tools for learning, research, teaching, and productivity. From generating study notes and research ideas to building educational chatbots and optimizing workflows, discover how Llama 4 can transform the way you work and learn

How does code actually run under the hood? Stop guessing and master the hidden physics of execution. Learn how Python and Java truly manage memory, layout data structures, and drive high-performance algorithms.

When you buy this book, you will get access to the Companion Resource Library, which includes:High-resolution versions of all figures featured in this volume.Curated educational videos from leading universities, research organizations, and trusted science communicators that complement the topics covered in this book.Additional companion resources and updates as they become available.

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.

Turn data into intelligent decisions. Discover how machine learning works and learn to build predictive models using Python and Scikit-Learn. From data preprocessing and visualization to classification, regression, clustering, and model evaluation, this practical guide helps you develop real-world machine learning skills.

Artificial Intelligence is reshaping the future. Are you ready to build it? Discover the complete journey from neural networks and deep learning fundamentals to generative AI, large language models, computer vision, and reinforcement learning—all with Python. Learn how to create intelligent systems, develop real-world projects, deploy production-ready models, and explore the technologies driving the next generation of innovation. Deep Learning with Python is your practical guide to mastering modern AI.