Google Jax Essentials

"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.

"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.

THE DEFINITIVE WORD ON STAR WARS FICTION The legendary motion picture Star Wars has spawned two big-screen sequels and three prequels—and decades of bestselling fiction. From the original movie tie-in novel through the monumental Fate of the Jedi series, legions of devoted readers have helped expand science fiction’s most celebrated film saga into a page-turning print sensation. Now, for the first time, a comprehensive overview of these sweeping Star Wars adventures is presented in one beautifully illustrated volume. Star Wars: The Essential Reader’s Companion spans the entire galaxy of published Star Wars fiction—movie novelizations, original stand alone and series novels, short stories, eBook novellas, young adult titles, and comics—and features: • a concise synopsis of each story, including key characters and planets • exclusive behind-the-scenes facts and anecdotes about authors, plot and character development, continuity notes, and significance in the Star Wars Expanded Universe • details on which novels are linked to Star Wars comic books from Dark Horse and Marvel • a chronological listing of titles, spanning the 25,000-year history of the Star Wars universe and placing each story in its proper context • more than one hundred original paintings throughout by some of fans’ favorite artists Whether skimming through fateful eras from the Old Republic to the New Jedi Order; delving deep into the ancient history of the Lost Tribe of the Sith or the tumultuous Clone Wars; crossing paths—and lightsabers—with Dark Lords such as Plagueis or Bane, Sidious or Vader; helming the Millennium Falcon with Han Solo; or mastering the Force with Luke Skywalker, this one-of-a-kind, one-stop reference is a must for fans looking to maximize their knowledge of the sprawling Star Wars Expanded Universe.

This book comprehensively reviews the anatomy, physiology, genetics and pathology of laboratory animals as well as the principles and practices of using laboratory animals for biomedical research.It covers the design of buildings used for laboratory animals, quality control of laboratory animals, and toxicology, and discusses various animal models used for human diseases. It also highlights aspects, such as handling and restraint and administration of drugs, as well as breeding and feeding of laboratory animals, and provides guidelines for developing meaningful experiments using laboratory animals.Further, the book discusses various alternatives to animal experiments for drug and chemical testing, including their advantages over the current approaches. Lastly, it examines the potential effect of harmful pathogens on the physiology of laboratory animals and discusses the state of art in in vivo imaging techniques.The book is a useful resource for research scientists, laboratory animal veterinarians, and students of laboratory animal medicine.

Just the math skills you need to excel in the study or practice of engineering Good math skills are indispensable for all engineers regardless of their specialty, yet only a relatively small portion of the math that engineering students study in college mathematics courses is used on a frequent basis in the study or practice of engineering. That's why Essential Math Skills for Engineers focuses on only these few critically essential math skills that students need in order to advance in their engineering studies and excel in engineering practice. Essential Math Skills for Engineers features concise, easy-to-follow explanations that quickly bring readers up to speed on all the essential core math skills used in the daily study and practice of engineering. These fundamental and essential skills are logically grouped into categories that make them easy to learn while also promoting their long-term retention. Among the key areas covered are: Algebra, geometry, trigonometry, complex arithmetic, and differential and integral calculus Simultaneous, linear, algebraic equations Linear, constant-coefficient, ordinary differential equations Linear, constant-coefficient, difference equations Linear, constant-coefficient, partial differential equations Fourier series and Fourier transform Laplace transform Mathematics of vectors With the thorough understanding of essential math skills gained from this text, readers will have mastered a key component of the knowledge needed to become successful students of engineering. In addition, this text is highly recommended for practicing engineers who want to refresh their math skills in order to tackle problems in engineering with confidence.

ESSENTIAL CONCEPTS IN MRI A concise and complete introductory treatment of NMR and MRI Essential Concepts in MRI delivers the first comprehensive look at magnetic resonance imaging with a practical focus on nuclear magnetic resonance spectroscopy applications. The book includes the essential components of MRI and NMR and is written for anyone new to the field of MRI who seeks to gain a complete understanding of all four essential components of MRI: physics theory, instrumentation, spectroscopy, and imaging. Highly visual and including numerous full color figures that provide crucial graphical descriptions of key concepts discussed in the book, Essential Concepts in MRI includes discussions of quantitative and creative MRI, as well as spatial mapping in MRI and the effects of the field gradient and k-space imaging. The book also covers: A thorough introduction to essential concepts in nuclear magnetic resonance, including classical descriptions of NMR and quantum mechanical descriptions of NMR Comprehensive explorations of essential concepts in NMR instrumentation, including magnets, radio-frequency coils, transmitters, and receivers Practical discussions of essential concepts in NMR spectroscopy, including simple 1D spectroscopy, double resonance, and dipolar interactions in two-spin systems In-depth examinations of essential concepts in MRI, including the design of MRI pulse sequences and the elements of MRI instrumentation, with a special focus on quantitative MRI Essential Concepts in MRI is a must-read reference for upper-level undergraduate and postgraduate students in the physical and medical sciences, especially radiology, MRI, and imaging courses. It is also essential for students and researchers in the biomedical sciences and engineering.

First developed as an accessible abridgement of the successful Handbook of Stem Cells, Essentials of Stem Cell Biology serves the needs of the evolving population of scientists, researchers, practitioners and students that are embracing the latest advances in stem cells. Representing the combined effort of seven editors and more than 200 scholars and scientists whose pioneering work has defined our understanding of stem cells, this book combines the prerequisites for a general understanding of adult and embryonic stem cells with a presentation by the world's experts of the latest research information about specific organ systems. From basic biology/mechanisms, early development, ectoderm, mesoderm, endoderm, methods to application of stem cells to specific human diseases, regulation and ethics, and patient perspectives, no topic in the field of stem cells is left uncovered. Selected for inclusion in Doody's Core Titles 2013, an essential collection development tool for health sciences libraries Contributions by Nobel Laureates and leading international investigators Includes two entirely new chapters devoted exclusively to induced pluripotent stem (iPS) cells written by the scientists who made the breakthrough Edited by a world-renowned author and researcher to present a complete story of stem cells in research, in application, and as the subject of political debate Presented in full color with glossary, highlighted terms, and bibliographic entries replacing references

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