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Found insideThis book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision ... Found inside – Page iiiThis is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. About the book Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. Found insideThis AI book collects the opinions of the luminaries of the AI business, such as Stuart Russell (coauthor of the leading AI textbook), Rodney Brooks (a leader in AI robotics), Demis Hassabis (chess prodigy and mind behind AlphaGo), and ... This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems ... Found insideThis two-volume set (LNAI 11055 and LNAI 11056) constitutes the refereed proceedings of the 10th International Conference on Collective Intelligence, ICCCI 2018, held in Bristol, UK, in September 2018 The 98 full papers presented were ... Found insideTraditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. This book introduces machine learning methods in finance. Found insideAn introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. This book provides a conceptual and empirical perspective on learning analytics, its goal being to disseminate the core concepts, research, and outcomes of this emergent field. Found inside – Page 1Its models run anywhere JavaScript runs, pushing ML farther up the application stack. About the book In Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. This book attempts to give the reader a gentle introduction to graphical modeling using R and the main features of some of these packages. 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It also covers the basics of Python and Java frameworks, which we are going to use later in the book. Found insideThis book discusses and assesses the latest trends in the interactive mobile field, and presents the outcomes of the 12th International Conference on Interactive Mobile Communication Technologies and Learning (IMCL2018), which was held in ... This book constitutes the proceedings of the 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, held in Delft, The Netherlands, in September 2019. 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. "Designed to teach people to program even if they have no prior experience. Found insideUsing clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, ... The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference. Found insideWhat you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement ... Description This book is appropriate for any standalone Computers and Society or Computer Ethics course offered by a computer science, business, or philosophy department, as well as special modules in any advanced CS course. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Found insideThis book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. Found inside – Page 1About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. This book presents the fundamentals of nonlinear and mixed-integer optimization, and their applications in the important area of process synthesis in chemical engineering. Accessible, no-nonsense, and programming language-agnostic introduction to algorithms. Found inside – Page 1This book is Part II of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms , the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part II contains Chapters 4 through 6 of the book. Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. This book also walks experienced JavaScript developers through modern module formats, how to namespace code effectively, and other essential topics. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This book introduces techniques and algorithms in the field. Found insideDeep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in ... This volume presents recent research on Methodologies and Intelligent Systems for Technology Enhanced Learning. It contains the contributions of MIS4TEL 2015, which took place in Salamanca, Spain,. On June 3rd to 5th 2015. Found insideEvery chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. Found insideThe book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Information System Design and Intelligent Applications (INDIA 2018), which was held at the Universite des Mascareignes, ... Found insideThis book constitutes the refereed proceedings of the Second International Conference on Innovative Technologies and Learning, ICITL 2019, held in Tromsø, Norway, in December 2019. 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