Thorough and complete with lots of examples and best practices, "RESTful Java with JAX-RS" demonstrates how to build RESTful Web applications with Java that are elegant, easy to use, and easy to understand.

CD-ROM contains: Source code -- Tools for developing and deploying Web services.

A bestselling forbidden love rockstar romance is now free to read for the very first time! He's JAX. He's a rockstar. He's a screw-up. He broke my heart. He's my new stepbrother. Jaxson Blue is rock royalty: the son of one of the biggest names on the planet. He was my first...everything. Then he broke my heart in the most public way possible. I never want to think about Jax again. Too bad his music seems to follow me wherever I go. Now my hard-living roadie of a father is marrying Jax's rock star mother and the four of us have to co-exist in the same house for two weeks. Jax is still the same sexy, arrogant jerk I fell in love with, and I'm too weak to resist him for long. So I make a deal with myself. It's only until the wedding. It's only a fling. It doesn't mean anything. I'm not doing anything wrong...right? NOTE: All characters are over the age of 18 and are not blood-related.

Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. In Deep Learning with JAX you will learn how to: • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax • Leverage libraries and modules from the JAX ecosystem Deep Learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Google’s JAX offers a fresh vision for deep learning. This powerful library gives you fine control over low level processes like gradient calculations, delivering fast and efficient model training and inference, especially on large datasets. JAX has transformed how research scientists approach deep learning. Now boasting a robust ecosystem of tools and libraries, JAX makes evolutionary computations, federated learning, and other performance-sensitive tasks approachable for all types of applications. About the book Deep Learning with JAX teaches you to build effective neural networks with JAX. In this example-rich book, you’ll discover how JAX’s unique features help you tackle important deep learning performance challenges, like distributing computations across a cluster of TPUs. You’ll put the library into action as you create an image classification tool, an image filter application, and other realistic projects. The nicely-annotated code listings demonstrate how JAX’s functional programming mindset improves composability and parallelization. What's inside • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax About the reader For intermediate Python programmers who are familiar with deep learning. About the author Grigory Sapunov holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning. The technical editor on this book was Nicholas McGreivy. Table of Contents Part 1 1 When and why to use JAX 2 Your first program in JAX Part 2 3 Working with arrays 4 Calculating gradients 5 Compiling your code 6 Vectorizing your code 7 Parallelizing your computations 8 Using tensor sharding 9 Random numbers in JAX 10 Working with pytrees Part 3 11 Higher-level neural network libraries 12 Other members of the JAX ecosystem A Installing JAX B Using Google Colab C Using Google Cloud TPUs D Experimental parallelization

Now fans of JAX can cook more than seventy of their most requested receipts adapted for home cooking.

"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 standard 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. 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 Necessity for Google JAX Unravelling JAX Setting up JAX for Machine Learning and Deep Learning JAX for Numerical Computing Diving Deeper into Auto Differentiation and Gradients Efficient Batch Processing with JAX Power of Parallel Computing with JAX 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.

Jax and his friends are based on real animals that lived in author's neighbors' backyards in the state of Colorado, USA. The author started writing the book about Jax and his friends when he noticed how all the animals seem to help each other during the cold and snowy months when there seemed to be the least of food. The animals would actually drop a piece of food for any animal to pick up.

Jax the Fighter is book 1 of the Fighting Dirty Trilogy. Books 2 and 3, Jax the Killer and Jax the Dom are available everywhere now! I hurt people. I lose myself in the sweat and blood and the sound of bones breaking. Maple’s different. She saved me, made me whole again. Now, someone wants to hurt her. And it’s my turn to save her. Even if it means going back to jail Even if it means killing again.

Jax the Killer is book 2 of the Fighting Dirty Trilogy. Book 3, Jax the Dom is available everywhere now! I hurt people. I lose myself in the sweat and blood and the sound of bones breaking. Maple’s different. She saved me, made me whole again. Now, someone wants to hurt her. And it’s my turn to save her. Even if it means going back to jail Even if it means killing again.

Jax the Dom is book 3 and the finale of the Fighting Dirty Trilogy! I hurt people. I lose myself in the sweat and blood and the sound of bones breaking. Maple’s different. She saved me, made me whole again. Now, someone wants to hurt her. And it’s my turn to save her. Even if it means going back to jail Even if it means killing again.