Machine Learning For Jax
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Deep Learning With Jax
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Author : Grigory Sapunov
language : en
Publisher: Simon and Schuster
Release Date : 2024-10-29
Deep Learning With Jax written by Grigory Sapunov and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-29 with Computers categories.
"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. 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" --Publisher's description.
Machine Learning For Jax
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Author : GILBERTO. NEAL
language : en
Publisher: Independently Published
Release Date : 2025-02-27
Machine Learning For Jax written by GILBERTO. NEAL and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-27 with Computers categories.
Machine learning is evolving rapidly, and efficiency is more critical than ever. Machine Learning for JAX is your ultimate guide to leveraging JAX for high-performance deep learning, large-scale AI training, and cutting-edge research. Whether you're a researcher, engineer, or AI enthusiast, this book will equip you with the tools to build faster, scalable, and optimized models using JAX's powerful automatic differentiation, JIT compilation, and GPU/TPU acceleration. This book provides comprehensive and hands-on coverage of JAX, from the fundamentals of numerical computing to advanced AI applications, including reinforcement learning, large language models (LLMs), and distributed training. You'll explore real-world industry use cases, optimize AI workflows with pmap and pjit, and learn how to handle massive datasets efficiently. Through detailed explanations, real-world examples, and working code implementations, you'll gain a deep practical understanding of JAX and its role in accelerating machine learning. Each chapter breaks down complex topics in an easy-to-follow manner, ensuring that both beginners and experienced developers can harness the full potential of JAX. What You Will Learn: Fundamentals of JAX and how it differs from NumPy and TensorFlow JIT compilation and vectorization for massive speedups Optimization techniques using SGD, Adam, and RMSprop in JAX Distributed training with multi-GPU and TPU acceleration Building and optimizing large-scale AI models like VAEs, GANs, and LLMs Using JAX in scientific computing and graph neural networks (GNNs) Real-world production use cases and how JAX integrates with Google's AI ecosystem Why This Book? Unlike other deep learning books, Machine Learning for JAX goes beyond the basics and focuses on practical, real-world applications. You won't just learn theory-you'll build, optimize, and scale AI models like a pro. Whether you're working on academic research, AI startups, or enterprise-scale ML systems, this book will elevate your machine learning capabilities. JAX is redefining the future of machine learning and AI research. Don't get left behind. Whether you're an ML researcher, software engineer, or data scientist, this book will empower you with the knowledge and skills to stay ahead in the AI revolution. Get your copy now and unlock the full power of JAX!
Google Jax Essentials
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Author : Mei Wong
language : en
Publisher: GitforGits
Release Date : 2023-05-31
Google Jax Essentials written by Mei Wong and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Computers categories.
"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.
Deep Learning For Biology
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Author : Charles Ravarani
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-07-23
Deep Learning For Biology written by Charles Ravarani and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-23 with Computers categories.
Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems. Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data. Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detection Apply architectures like convolutional neural networks, transformers, graph neural networks, and autoencoders Use Python and interactive notebooks for hands-on learning Build problem-solving intuition that generalizes beyond biology Whether you’re exploring new methods, transitioning into computational biology, or looking to make sense of machine learning in your field, this book offers a clear and approachable path forward.
Advanced Python Programming
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Author : Quan Nguyen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-25
Advanced Python Programming written by Quan Nguyen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-25 with Computers categories.
Write fast, robust, and highly reusable applications using Python's internal optimization, state-of-the-art performance-benchmarking tools, and cutting-edge libraries Key FeaturesBenchmark, profile, and accelerate Python programs using optimization toolsScale applications to multiple processors with concurrent programmingMake applications robust and reusable using effective design patternsBook Description Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases. What you will learnWrite efficient numerical code with NumPy, pandas, and XarrayUse Cython and Numba to achieve native performanceFind bottlenecks in your Python code using profilersOptimize your machine learning models with JAXImplement multithreaded, multiprocessing, and asynchronous programsSolve common problems in concurrent programming, such as deadlocksTackle architecture challenges with design patternsWho this book is for This book is for intermediate to experienced Python programmers who are looking to scale up their applications in a systematic and robust manner. Programmers from a range of backgrounds will find this book useful, including software engineers, scientific programmers, and software architects.
Dive Into Deep Learning
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Author : Joanne Quinn
language : en
Publisher: Corwin Press
Release Date : 2019-07-15
Dive Into Deep Learning written by Joanne Quinn and has been published by Corwin Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-15 with Education categories.
The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.
Machine Learning And Knowledge Discovery In Databases Applied Data Science And Demo Track
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Author : Gianmarco De Francisci Morales
language : en
Publisher: Springer Nature
Release Date : 2023-09-16
Machine Learning And Knowledge Discovery In Databases Applied Data Science And Demo Track written by Gianmarco De Francisci Morales and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-16 with Computers categories.
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: Robustness; Time Series; Transfer and Multitask Learning. Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Google Jax Cookbook
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Author : Zephyr Quent
language : en
Publisher: GitforGits
Release Date : 2024-10-30
Google Jax Cookbook written by Zephyr Quent and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-30 with Computers categories.
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 Essentials
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-24
Jax Essentials written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-24 with Computers categories.
"JAX Essentials" JAX Essentials is a comprehensive guide designed for engineers, researchers, and practitioners aiming to master the fundamentals and advanced capabilities of Google’s JAX library. Beginning with the foundational principles, the book explores JAX's unique approach to array programming, rooted in functional programming and immutability, and covers its architecture, data structures, and the powerful mechanics of automatic differentiation. The reader learns how JAX integrates with the broader numerical computing landscape—tracing its evolution, relationship with tools like NumPy and XLA, and its relevance in modern machine learning workflows. Building on this foundation, the book delves into advanced topics such as efficient array operations, parallelization strategies, just-in-time compilation, differentiation APIs, and functional control flow constructs. Readers are guided through the intricacies of high-performance and distributed computing, memory optimization, cross-framework interoperability, and scientific computing applications, including linear algebra, spectral analysis, differential equations, and statistical modeling. Clear, practical examples and best practices illustrate how to harness JAX’s capabilities for building custom neural networks, scalable training pipelines, and robust production systems. JAX Essentials further addresses the needs of power users with chapters on extensibility, custom primitives, XLA integration, mixed-precision and distributed training, debugging, and performance tuning. The book concludes with an insightful look at JAX’s ecosystem, emerging libraries, research breakthroughs, and future directions, empowering readers to contribute to and thrive in a vibrant, rapidly evolving open-source community. Whether you are migrating legacy code, scaling scientific workloads, or building state-of-the-art machine learning solutions, this book is your definitive companion for unlocking the potential of JAX.
Parallel Python With Dask
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Author : Tim Peters
language : en
Publisher: GitforGits
Release Date : 2023-10-19
Parallel Python With Dask written by Tim Peters and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-19 with Computers categories.
Unlock the Power of Parallel Python with Dask: A Perfect Learning Guide for Aspiring Data Scientists Dask has revolutionized parallel computing for Python, empowering data scientists to accelerate their workflows. This comprehensive guide unravels the intricacies of Dask to help you harness its capabilities for machine learning and data analysis. Across 10 chapters, you'll master Dask's fundamentals, architecture, and integration with Python's scientific computing ecosystem. Step-by-step tutorials demonstrate parallel mapping, task scheduling, and leveraging Dask arrays for NumPy workloads. You'll discover how Dask seamlessly scales Pandas, Scikit-Learn, PyTorch, and other libraries for large datasets. Dedicated chapters explore scaling regression, classification, hyperparameter tuning, feature engineering, and more with clear examples. You'll also learn to tap into the power of GPUs with Dask, RAPIDS, and Google JAX for orders of magnitude speedups. This book places special emphasis on practical use cases related to scalability and distributed computing. You'll learn Dask patterns for cluster computing, managing resources efficiently, and robust data pipelines. The advanced chapters on DaskML and deep learning showcase how to build scalable models with PyTorch and TensorFlow. With this book, you'll gain practical skills to: Accelerate Python workloads with parallel mapping and task scheduling Speed up NumPy, Pandas, Scikit-Learn, PyTorch, and other libraries Build scalable machine learning pipelines for large datasets Leverage GPUs efficiently via Dask, RAPIDS and JAX Manage Dask clusters and workflows for distributed computing Streamline deep learning models with DaskML and DL frameworks Packed with hands-on examples and expert insights, this book provides the complete toolkit to harness Dask's capabilities. It will empower Python programmers, data scientists, and machine learning engineers to achieve faster workflows and operationalize parallel computing. Table of Content Introduction to Dask Dask Fundamentals Batch Data Parallel Processing with Dask Distributed Systems and Dask Advanced Dask: APIs and Building Blocks Dask with Pandas Dask with Scikit-learn Dask and PyTorch Dask with GPUs Scaling Machine Learning Projects with Dask