"Google JAX Essentials" is a comprehensive guide designed for machine learning and deep learning professionals aiming to leverage the power and capabilities of Google's JAX library in their projects. Over the course of eight chapters, this book takes the reader from understanding the challenges of deep learning and numerical computations in the existing frameworks to the essentials of Google JAX, its functionalities, and how to leverage it in real-world machine learning and deep learning projects.

The book starts by emphasizing the importance of numerical computing in ML and DL, demonstrating the limitations of traditional libraries like NumPy, and introducing the solution offered by JAX. It then guides the reader through the installation of JAX on different computing environments like CPUs, GPUs, and TPUs, and its integration into existing ML and DL projects. Moving further, the book details the advanced numerical operations and unique features of JAX, including JIT compilation, automatic differentiation, batched operations, and custom gradients. It illustrates how these features can be employed to write code that is both simpler and faster.

The book also delves into parallel computation, the effective use of the vmap function, and the use of pmap for distributed computing. Lastly, the reader is walked through the practical application of JAX in training different deep learning models, including RNNs, CNNs, and Bayesian models, with an additional focus on performance-tuning strategies for JAX applications.

Key Learnings

• Mastering the installation and configuration of JAX on various computing environments.
• Understanding the intricacies of JAX's advanced numerical operations.
• Harnessing the power of JIT compilation in JAX for accelerated computations.
• Implementing batched operations using the vmap function for efficient processing.
• Leveraging automatic differentiation and custom gradients in JAX.
• Proficiency in using the pmap function for distributed computing in JAX.
• Training different types of deep learning models using JAX.
• Applying performance tuning strategies to maximize JAX application efficiency.
• Integrating JAX into existing machine learning and deep learning projects.
• Complementing the official JAX documentation with practical, real-world applications.

Table of Content

1. Necessity for Google JAX
2. Unravelling JAX
3. Setting up JAX for Machine Learning and Deep Learning
4. JAX for Numerical Computing
5. Diving Deeper into Auto Differentiation and Gradients
6. Efficient Batch Processing with JAX
7. Power of Parallel Computing with JAX
8. Training Neural Networks with JAX

Audience

This is must read for machine learning and deep learning professionals to be skilled with the most innovative deep learning library. Knowing Python and experience with machine learning is sufficient is desired to begin with this book

Unleash the Speed: Master WebAssembly and Take Your Web Apps to the Next Level

Through practical examples, you’ll learn how WebAssembly can transform C++, Rust, and other languages into lightning-fast web apps. Step-by-step, you’ll master techniques to integrate WebAssembly modules into JavaScript projects for seamless communication.

But we go deeper. You’ll discover strategies to optimize code, leverage multi-threading, and apply cutting-edge methods like lazy loading. We also tackle memory management and dive into profiling, benchmarking, and browser tools to push performance to the limits.

This isn’t just essentials of webassembly - it’s a roadmap to learn intricacies, best practices, and solutions to common pitfalls. The goal is to equip you with the expertise to develop insanely fast applications. The future is native-level performance on the web. Join the revolution and skill up with this guide to mastering WebAssembly. Your web apps will never be the same!

Key Learnings

• Grasp WASMs core concepts, bridging web and native apps, boosting performance seamlessly.
• Dive into tools like Emscripten and Binaryen, facilitating efficient cross language compilations to Wasm.
• Learn techniques to convert C++, Python, and Java apps, unlocking web application potential.
• Seamlessly integrate JavaScript and WebAssembly, ensuring smooth inter-module communications.
• Harness strategies for fine-tuning code, guaranteeing peak application performance.
• Explore multi-threading, leveraging simultaneous operations, accelerating execution times.
• Implement lazy loading and code splitting, optimizing web app responsiveness and load times.
• Dive deep into efficient memory usage, ensuring resourceful application execution.
• Proficiently use browser developer tools, dissecting and enhancing application performance.
• Enhance user experience with effective caching, ensuring rapid web application accessibility.

Prerequisites

This book is for every programmer aspiring to put their all applications onto web browser with the same speed, efficiency and product performance. Knowing fundamentals of any programming language and basics of web app development is sufficient to tear out the book wholly.