Deep Learning With 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!
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.
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 With Pytorch
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Author : Luca Pietro Giovanni Antiga
language : en
Publisher: Simon and Schuster
Release Date : 2020-07-01
Deep Learning With Pytorch written by Luca Pietro Giovanni Antiga 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 2020-07-01 with Computers categories.
“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
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.
Optimization And Learning
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Author : Bernabé Dorronsoro
language : en
Publisher: Springer Nature
Release Date : 2025-01-26
Optimization And Learning written by Bernabé Dorronsoro and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-26 with Computers categories.
This book constitutes the refereed proceedings of the 7th International Conference on Optimization and Learning, OLA 2024, held in Dubrovnik, Croatia, during May 13–15, 2024. The 24 full papers presented here were carefully reviewed and selected from 64 submissions. They were organized in the following topical sections: synergies between optimization and machine learning; enhancing optimization and learning techniques; transportation and routing; and applications.
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.
Ensemble Methods For Machine Learning
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Author : Gautam Kunapuli
language : en
Publisher: Simon and Schuster
Release Date : 2023-05-30
Ensemble Methods For Machine Learning written by Gautam Kunapuli 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 2023-05-30 with Computers categories.
Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf ensemble implementations Random forests, boosting, and gradient boosting Feature engineering and ensemble diversity Interpretability and explainability for ensemble methods Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. About the Technology Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets. About the Book Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation! What’s Inside Bagging, boosting, and gradient boosting Methods for classification, regression, and retrieval Interpretability and explainability for ensemble methods Feature engineering and ensemble diversity About the Reader For Python programmers with machine learning experience. About the Author Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. Table of Contents PART 1 - THE BASICS OF ENSEMBLES 1 Ensemble methods: Hype or hallelujah? PART 2 - ESSENTIAL ENSEMBLE METHODS 2 Homogeneous parallel ensembles: Bagging and random forests 3 Heterogeneous parallel ensembles: Combining strong learners 4 Sequential ensembles: Adaptive boosting 5 Sequential ensembles: Gradient boosting 6 Sequential ensembles: Newton boosting PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA 7 Learning with continuous and count labels 8 Learning with categorical features 9 Explaining your ensembles
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