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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.
Seven is an enigma. A motorcycle club princess. The daughter of a notorious gangster. The best friend of the deputy mayor. A coffee shop owner. The single mom of two young, adopted children. She’s colorful, in every way possible—from her attitude to her piercings and bright pink hair—and she’s a woman on a mission with the power to help broker a clean break between a powerful motorcycle club and a South American drug cartel. But not all players are ready for the game to change, including the ones she can’t see like the CIA. Jax Michaelson has a bad attitude and a good shot. The former Navy SEAL has been on Titan’s problem list for running his mouth since the day he showed up for work, but he does a hell of a job, and they’d never let him go. Call him cocky, that’s fine, because then you’d have to admit he’s the best at anything and everything—except diplomacy. When Titan is forced into the seedy drug world filled with cartel glitz and Harley-riding MCs, Seven and her family become an unexpected bargaining chip right after she and Jax find a way to stand each other—in bed. Will friends become lovers? Or are they too far gone to be opposites that attract? Is Jax nothing but a bad boy who leaves her hoping for a military hero when the burden of living as Mayhem royalty backfires and her children disappear.
ax (Desert Aces MC): An Age Gap, Second Chance at Love MC Romance I wasn’t always the Desert Aces MC President. When I was 20 years old, tragedy struck when my parents were killed by a drunk driver. I was young and stupid and decided to prospect for the Desert Aces MC in Las Vegas. At the time, I didn’t care about its lawlessness. I liked the drinking, the fighting, the fucking. It helped to numb the pain. But as time went on, I began to realize the club was on a dangerous path to destruction. And that if we didn’t clean up our act, our club wouldn’t survive. I couldn’t let that happen. I’ve given everything to my club and it’s time I take a little something for myself. I need to find a woman and settle down. My mind drifts to Diana, all those years ago. I regret letting her walk away. But that all changes when one afternoon, she drives up to the compound gates, looking gorgeous as ever. Instantly, my feelings for her re-surface, igniting inside me. This time, I won’t let her walk away. I’m making her mine.
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.
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
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.
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.
This is the practical, solution-oriented book for every data scientists, machine learning engineers, and AI engineers to utilize the most of Google JAX for efficient and advanced machine learning. It covers essential tasks, troubleshooting scenarios, and optimization techniques to address common challenges encountered while working with JAX across machine learning and numerical computing projects. The book starts with the move from NumPy to JAX. It introduces the best ways to speed up computations, handle data types, generate random numbers, and perform in-place operations. It then shows you how to use profiling techniques to monitor computation time and device memory, helping you to optimize training and performance. The debugging section provides clear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors. The book goes on to show you how to master Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations. If you want to optimize training processes, this book has you covered. It includes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai. You'll learn how to visualize model performance and monitor metrics to assess training progress effectively. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly. Key Learnings Get your calculations done faster by moving from NumPy to JAX's optimized framework. Make your training pipelines more efficient by profiling how long things take and how much memory they use. Use debugging techniques to fix runtime issues like shape mismatches and numerical instability. Get to grips with Pytrees for managing complex, nested data structures across various machine learning tasks. Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost. Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy. Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics. Use advanced visualization techniques, like confusion matrices and learning curves, to make model evaluation more effective. Create your own neural networks and optimizers directly in JAX so you have full control of the architecture. Use serialization techniques to save, load, and transfer models and training checkpoints efficiently. Table of Content Transition NumPy to JAX Profiling Computation and Device Memory Debugging Runtime Values and Errors Mastering Pytrees for Data Structures Exporting and Serialization Type Promotion Semantics and Mixed Precision Integrating Foreign Functions (FFI) Training Neural Networks with JAX
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.
In this book you'll learn the concepts of SOAP based Web Services architecture and get practical advice on building and deploying Web Services in the enterprise. Starting from the basics and the best practices for setting up a development environment, this book enters into the inner details of the JAX-WS in a clear and concise way. You will also learn about the major toolkits available for creating, compiling and testing SOAP Web Services and how to address common issues such as debugging data and securing its content.