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Author: Mei Wong
Publisher: GitforGits
ISBN: 8196288328
Category : Computers
Languages : en
Pages : 161
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Book Description
"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.
Author: Mei Wong
Publisher: GitforGits
ISBN: 8196288328
Category : Computers
Languages : en
Pages : 161
Get Book Here
Book Description
"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.
Author: Mei Wong
Publisher: GitforGits
ISBN: 8196288328
Category : Computers
Languages : en
Pages : 161
Get Book Here
Book Description
"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.
Author: Zephyr Quent
Publisher: GitforGits
ISBN: 8197950415
Category : Computers
Languages : en
Pages : 250
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Book Description
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
Author: Thomas Nield
Publisher: "O'Reilly Media, Inc."
ISBN: 1098102908
Category : Computers
Languages : en
Pages : 352
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Book Description
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market
Author: Ryoichi Shinkuma
Publisher: Springer Nature
ISBN: 3031293010
Category : Technology & Engineering
Languages : en
Pages : 260
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Book Description
This book presents advances on the state of the art in smart cities systems and applications based on the proof of concept and prototyping for smart cities in an interdisciplinary context of engineering and information sciences. Smart cities have emerged as highly complex technological endeavors that combine knowledge and technology from many disciplines ranging from information sciences to engineering. Due to their complex nature, the modeling, development, and prototyping of applications in smart cities present a myriad of challenges, including technical, economic, and social ones, across application subdomains such as smart transportation, social welfare, tourism, and smart industry. It becomes difficult or sometimes impossible to provide a solution for such potential research issues and challenges from a traditional disciplinary-approach only; to tackle such research issues and to make the paradigm of smart cities a reality, interdisciplinary approaches are deemed necessary. Readers, developers, practitioners, and policy-makers in the field find in the book insights, experiences, findings, and perspectives on smart cities applications with an emphasis on real-life prototyping, beyond the confines of laboratory experiments.
Author: Milutinovi?, Veljko
Publisher: IGI Global
ISBN: 1799871584
Category : Computers
Languages : en
Pages : 393
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Book Description
As computers continue to remain essential tools for the pursuit of physics, medicine, economics, social sciences, and more, supercomputers are proving that they can further extend and greatly enhance as-of-yet undiscovered knowledge and solve the world’s most complex problems. As these instruments continue to lead to groundbreaking discoveries and breakthroughs, it is imperative that research remains up to date with the latest findings and uses. The Handbook of Research on Methodologies and Applications of Supercomputing is a comprehensive and critical reference book that provides research on the latest advances of control flow and dataflow supercomputing and highlights selected emerging big data applications needing high acceleration and/or low power. Consequently, this book advocates the need for hybrid computing, where the control flow part represents the host architecture and dataflow part represents the acceleration architecture. These issues cover the initial eight chapters. The remaining eight chapters cover selected modern applications that are best implemented on a hybrid computer, in which the transactional parts (serial code) are implemented on the control flow part and the loops (parallel code) on the dataflow part. These final eight chapters cover two major application domains: scientific computing and computing for digital economy. This book offers applications in marketing, medicine, energy systems, and library science, among others, and is an essential source for scientists, programmers, engineers, practitioners, researchers, academicians, and students interested in the latest findings and advancements in supercomputing.
Author: Raj Arun R
Publisher: Orange Education Pvt Ltd
ISBN: 8197081824
Category : Computers
Languages : en
Pages : 547
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Book Description
A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index
Author: Bogunuva Mohanram Balachandar
Publisher: Packt Publishing Ltd
ISBN: 1788294963
Category : Computers
Languages : en
Pages : 412
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Book Description
Master core REST concepts and create RESTful web services in Java About This Book Build efficient and secure RESTful web APIs in Java.. Design solutions to produce, consume and visualize RESTful web services using WADL, RAML, and Swagger Familiarize the role of RESTful APIs usage in emerging technology trends like Cloud, IoT, Social Media. Who This Book Is For If you are a web developer with a basic understanding of the REST concepts and envisage to get acquainted with the idea of designing and developing RESTful web services, this is the book for you. As all the code samples for the book are written in Java, proficiency in Java is a must. What You Will Learn Introduce yourself to the RESTful software architectural style and the REST API design principles Make use of the JSR 353 API, JSR 374 API, JSR 367 API and Jackson API for JSON processing Build portable RESTful web APIs, making use of the JAX-RS 2.1 API Simplify API development using the Jersey and RESTEasy extension APIs Secure your RESTful web services with various authentication and authorization mechanisms Get to grips with the various metadata solutions to describe, produce, and consume RESTful web services Understand the design and coding guidelines to build well-performing RESTful APIs See how the role of RESTful web services changes with emerging technologies and trends In Detail Representational State Transfer (REST) is a simple yet powerful software architecture style to create lightweight and scalable web services. The RESTful web services use HTTP as the transport protocol and can use any message formats, including XML, JSON(widely used), CSV, and many more, which makes it easily inter-operable across different languages and platforms. This successful book is currently in its 3rd edition and has been used by thousands of developers. It serves as an excellent guide for developing RESTful web services in Java. This book attempts to familiarize the reader with the concepts of REST. It is a pragmatic guide for designing and developing web services using Java APIs for real-life use cases following best practices and for learning to secure REST APIs using OAuth and JWT. Finally, you will learn the role of RESTful web services for future technological advances, be it cloud, IoT or social media. By the end of this book, you will be able to efficiently build robust, scalable, and secure RESTful web services using Java APIs. Style and approach Step-by-step guide to designing and developing robust RESTful web services. Each topic is explained in a simple and easy-to-understand manner with lots of real-life use-cases and their solutions.
Author: Kumar, Raghvendra
Publisher: IGI Global
ISBN:
Category : Computers
Languages : en
Pages : 360
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Book Description
Integrating generative artificial intelligence (AI) into art, design, and media presents a double-edged sword. While it offers unprecedented creative possibilities, it raises ethical concerns, challenges traditional workflows, and requires careful regulation. As AI becomes more prevalent in these fields, there is a pressing need for a comprehensive resource that explores the technology's potential and navigates the complex landscape of its implications. The Pioneering Applications of Generative AI is a pioneering book that addresses these challenges head-on. It provides a deep dive into the evolution, ethical considerations, core technologies, and creative applications of generative AI, offering readers a thorough understanding of this transformative technology. Researchers, academicians, scientists, and research scholars will find this book invaluable in navigating the complexities of generative AI in art, design, and media. With its focus on ethical and responsible AI and discussions on regulatory frameworks, the book equips readers with the knowledge and tools needed to harness the full potential of generative AI while ensuring its responsible and ethical use.
Author: Pethuru Raj Chelliah
Publisher: John Wiley & Sons
ISBN: 1394219210
Category : Computers
Languages : en
Pages : 436
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Book Description
Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data Overcoming cyberattacks on mission-critical software systems by leveraging federated learning
