An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning:An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.Covers generation of high dimensional outputs, such as images, text, and gr

In this first volume of the "Mac OS and *OS Internals" trilogy, Jonathan Levin takes on the user mode components of Apple's operating systems. Starting with an introduction as to their layered architecture, touring private frameworks and libraries, and then delving into the internals of applications, process, thread and memory management, Mach messaging, launchd and XPC internals, and wrapping up with advanced debugging and tracing techniques using the most powerful APIs that were hitherto unknown and unused outside Apple's own applications. As with the other books in this series, the approach taken is that of deep reverse engineering, with plenty of hands-on examples, illustrations, pointers to Apple's open sources (when available) and decompilation of code (when not). The book's companion website (NewOSXBook.com) is full of tools, samples and other bonus material for this book. Due to print run issues, NOTE FIRST COPIES WILL SHIP DECEMBER. Read more

In this second volume of the "Mac OS and *OS Internals" trilogy, Jonathan Levin takes on the kernel and hardware aspects of Macs and i-Devices. Starting with an examination of the kernel sources, then going off the beaten path to undocumented portions, especially in the *OS variants. This book explains in detail the various components of XNU - BSD, Mach, platform expert, Kernel Extensions and the IOKit environment. It goes further into memory management (vm_map , pmap and the kernel zone allocator), processes, threads, Mach IPC internals, the Virtual Filesystem Switch, and more. Never before published details about the Apple File System (APFS), Secure Enclave Processor OS (SEPOS), iBoot, Mac EFI and more are explained in meticulous detail and fine illustrations. Numerous experiments allow a hands-on approach, making this invaluable to anyone who wishes to learn more about how the XNU kernel operates - and how to interface with its darkest and most powerful mechanisms. This book is only offered for direct pur

A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics.Some books on algorithms are rigorous but incomplete;others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout.New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learningNew material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays140 new exercises and 22 new problemsReader