Download or read book Game Changer written by Matthew Sadler and published by New In Chess,Csi. This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the story behind the self-learning artificial intelligence system with its stunning chess skills

Download or read book Alpha Zero written by Arthur Stone and published by . This book was released on 2020-12-20 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: I should not exist.All children like me are stillborn, or die in infancy. Those who cannot grow stronger, die. No empty child has ever reached a year of age, yet I am now thirteen.It has been a long and miserable thirteen years, where the best I can manage to do is walk with difficulty. Sometimes, I cannot even manage that.My clan has paid dearly for every minute of my life. And money is not so easy to obtain, here at the edge of civilization.Perhaps I might have lived in this state for many years. A cripple, strong in mind but feeble in body. But when some unexpected guests came to our estate, everything changed. I would die at last - or, I would learn to survive on my own.

Download or read book Mutant Year Zero Genlab Alpha Core written by Modiphius and published by Modiphius. This book was released on 2017-07 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: During the great apocalypse, humanity fled to the depths of the underground enclaves. In genetic laboratories, researchers tried to breed a new being, splicing human and animal DNA, creating a beast intelligent yet strong enough to survive in the devastated world. The enclaves have fallen - but the animals fight for freedom has only just begun.

Download or read book Artificial Intelligence with Python written by Prateek Joshi and published by Packt Publishing Ltd. This book was released on 2017-01-27 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.

Download or read book Deep Reinforcement Learning written by Hao Dong and published by Springer Nature. This book was released on 2020-06-29 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.

Download or read book Machine Learning with PyTorch and Scikit Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Download or read book AI 2019 Advances in Artificial Intelligence written by Jixue Liu and published by Springer Nature. This book was released on 2019-11-25 with total page 622 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 32nd Australasian Joint Conference on Artificial Intelligence, AI 2019, held in Adelaide, SA, Australia, in December 2019. The 48 full papers presented in this volume were carefully reviewed and selected from 115 submissions. The paper were organized in topical sections named: game and multiagent systems; knowledge acquisition, representation, reasoning; machine learning and applications; natural language processing and text analytics; optimization and evolutionary computing; and image processing.

Download or read book Trump Sky Alpha written by Mark Doten and published by Graywolf Press. This book was released on 2019-02-19 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: A novel on the political madness of our time and the Internet’s deep workings, by the author of The Infernal One year after the president has plunged the world into nuclear war, a journalist takes refuge in the Twin Cities Metro Containment Zone. On assignment, she documents internet humor at the end of the world, hoping along the way to find the final resting place of her wife and daughter. What she uncovers, hidden amid spiraling memes and twitter jokes in an archive of the internet’s remnants, are references to an enigmatic figure known only as Birdcrash, who may hold the key to an uncertain future.

Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Download or read book The AI Revolution in Chess written by Joshua Doknjas and published by Everyman Chess. This book was released on with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 2018 DeepMind published the shocking results of their chess-playing artificial intelligence software, AlphaZero. Chess players looked in disbelief and immediately wondered how AI would affect the future of chess. Less than a year later, a whole new wave of chess engines emerged that were based on using neural networks to evaluate positions in a completely new way. This book is about the extraordinary impact that AI has had on modern chess. The games of top chess players since the end of 2018 have reflected the use of these new engines in home analysis. They have significantly developed opening theory as well as the general understanding of middlegame concepts. By analysing these games with the help of neural network engines, FIDE Master Joshua Doknjas discusses numerous exciting ideas and examines areas of chess that had previously been overlooked. With thorough explanations, questions, and exercises, this book provides fascinating material for masters and less experienced players alike.

Download or read book The Alpha Plague written by Michael Robertson and published by Michael Robertson. This book was released on 2023-05-06 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you have a plan for the apocalypse? Rhys doesn't. But as he watches chaos spill from the Alpha tower, he knows one thing for sure ... He must get to his son before the virus does. If you like high stakes and edge of your seat action in a post-apocalyptic world, then The Alpha Plague is for you. Get it now to join Rhys at ground zero as he tries to save his loved ones and survive a disaster that will leave the world changed forever.

Download or read book Zero written by Charles Seife and published by Souvenir Press. This book was released on 2019-11-28 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: A NEW YORK TIMES NOTABLE BOOK The Babylonians invented it, the Greeks banned it, the Hindus worshipped it, and the Christian Church used it to fend off heretics. Today it's a timebomb ticking in the heart of astrophysics. For zero, infinity's twin, is not like other numbers. It is both nothing and everything. Zero has pitted East against West and faith against reason, and its intransigence persists in the dark core of a black hole and the brilliant flash of the Big Bang. Today, zero lies at the heart of one of the biggest scientific controversies of all time: the quest for a theory of everything. Within the concept of zero lies a philosophical and scientific history of humanity. Charles Seife's elegant and witty account takes us from Aristotle to superstring theory by way of Egyptian geometry, Kabbalism, Einstein, the Chandrasekhar limit and Stephen Hawking. Covering centuries of thought, it is a concise tour of a world of ideas, bound up in the simple notion of nothing.

Download or read book Capablanca written by Frisco Del Rosario and published by . This book was released on 2010-10-16 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Jose Raul Capablanca is renowned for his exquisite positional play and flawless endgame technique. But The Chess Machine was also a master of that other way to deliver mate: the attack on the enemy king.In this groundbreaking work, award-winning chess coach and author Frisco Del Rosario shines a long-overdue light on this neglected aspect of Capablanca's record. He illustrates how the Cuban genius used positional concepts to build up irresistible king hunts, embodying the principles of good play advocated by the unequaled teacher, C.J.S. Purdy. The author also identifies an overlooked checkmate pattern - Capablanca's Mate - that aspiring attackers can add to the standard catalogue in Renaud and Kahn's The Art of the Checkmate. As Del Rosario shows, Capablanca has inspired not only generations of players, but also many of the classics of chess literature.Easy to read but chock-full of advice for study and practical play, Capablanca: A Primer of Checkmate fills a gaping hole in our understanding of the third World Champion.

Download or read book Finding Alpha written by Eric Falkenstein and published by Wiley. This book was released on 2009-06-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Finding Alpha "Eric Falkenstein is more than one of the smartest and funniest people in finance. He's been a banker, a key model builder at a major rating agency, and a hedge fund trader. In this tour de force, he outlines the successes and failures of financial theory applications in the real world from the perspective of an aggressive early adopter of the best ideas in finance. To this day, I think Eric's private firm default model is one of the best papers ever published in applied finance, and this wonderful book falls into the same category." —Donald R. van Deventer, PhD, founder and Chief Executive Officer, Kamakura Corporation "People dismissed Columbus when he said the world was round. Thank goodness he persisted. Like Columbus, Falkenstein challenges standard thinking, only this time about risk and reward. As the meltdown of the capital markets has shown, the financial industry clearly missed something with regard to risk management. As an industry, we need to consider alternative theories on risk, and clearly Falkenstein is on to something here. Agree with him or not, Finding Alpha is worth a read." —Kevin M. Blakely, President and CEO,The Risk Management Association "Writing through the lens of an experienced practitioner, Falkenstein digests decades of research in capital markets, financial economics, and investment psychology that have shaped modern investment theory. This text is an excellent companion for portfolio managers, investment students, or anyone seeking to better understand the relationship between risk, returns, and financial reward." —Todd Houge, PhD, CFA, The University of Iowa How do we find alpha whenrisk does not correlate with return? Finding Alpha is a practical guide to achieving alpha when conventional measures of risk rarely correlate with higher returns. Author Eric Falkenstein-a PhD who has also been a risk manager and portfolio manager—tells the story of alpha from its beginnings to its current reversal, where risk is now evidenced by return as opposed to vice versa. Falkenstein begins by walking readers through the Capital Asset Pricing Model (CAPM), as well as other well-documented theories about risk and return, and explores how these theories measure up to current empirical evidence being documented by researchers and academics. He also outlines a novel approach to the issues of how benchmark risk and investor overconfidence affects expected asset returns, how to understand the nature of alpha and risk, and how to use practical applications of alpha-seeking strategies that he developed as a successful hedge fund manager. Finding Alpha concludes by outlining some real-life applications of alpha in finance and explains how the search for alpha affects the day-to-day life of all financial professionals.

Download or read book Chess for Life written by Matthew Sadler and published by Gambit Publications. This book was released on 2016-03-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examines how chess style and abilities vary with age. By making a number of case studies and interviewing players who have stayed strong as they have aged, the authors show in detail how players can steer their games towards positions where their experience can shine through.

Download or read book Lessons from AlphaZero for Optimal Model Predictive and Adaptive Control written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 2022-03-19 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to propose and develop a new conceptual framework for approximate Dynamic Programming (DP) and Reinforcement Learning (RL). This framework centers around two algorithms, which are designed largely independently of each other and operate in synergy through the powerful mechanism of Newton's method. We call these the off-line training and the on-line play algorithms; the names are borrowed from some of the major successes of RL involving games. Primary examples are the recent (2017) AlphaZero program (which plays chess), and the similarly structured and earlier (1990s) TD-Gammon program (which plays backgammon). In these game contexts, the off-line training algorithm is the method used to teach the program how to evaluate positions and to generate good moves at any given position, while the on-line play algorithm is the method used to play in real time against human or computer opponents. Both AlphaZero and TD-Gammon were trained off-line extensively using neural networks and an approximate version of the fundamental DP algorithm of policy iteration. Yet the AlphaZero player that was obtained off-line is not used directly during on-line play (it is too inaccurate due to approximation errors that are inherent in off-line neural network training). Instead a separate on-line player is used to select moves, based on multistep lookahead minimization and a terminal position evaluator that was trained using experience with the off-line player. The on-line player performs a form of policy improvement, which is not degraded by neural network approximations. As a result, it greatly improves the performance of the off-line player. Similarly, TD-Gammon performs on-line a policy improvement step using one-step or two-step lookahead minimization, which is not degraded by neural network approximations. To this end it uses an off-line neural network-trained terminal position evaluator, and importantly it also extends its on-line lookahead by rollout (simulation with the one-step lookahead player that is based on the position evaluator). Significantly, the synergy between off-line training and on-line play also underlies Model Predictive Control (MPC), a major control system design methodology that has been extensively developed since the 1980s. This synergy can be understood in terms of abstract models of infinite horizon DP and simple geometrical constructions, and helps to explain the all-important stability issues within the MPC context. An additional benefit of policy improvement by approximation in value space, not observed in the context of games (which have stable rules and environment), is that it works well with changing problem parameters and on-line replanning, similar to indirect adaptive control. Here the Bellman equation is perturbed due to the parameter changes, but approximation in value space still operates as a Newton step. An essential requirement here is that a system model is estimated on-line through some identification method, and is used during the one-step or multistep lookahead minimization process. In this monograph we aim to provide insights (often based on visualization), which explain the beneficial effects of on-line decision making on top of off-line training. In the process, we will bring out the strong connections between the artificial intelligence view of RL, and the control theory views of MPC and adaptive control. Moreover, we will show that in addition to MPC and adaptive control, our conceptual framework can be effectively integrated with other important methodologies such as multiagent systems and decentralized control, discrete and Bayesian optimization, and heuristic algorithms for discrete optimization. One of our principal aims is to show, through the algorithmic ideas of Newton's method and the unifying principles of abstract DP, that the AlphaZero/TD-Gammon methodology of approximation in value space and rollout applies very broadly to deterministic and stochastic optimal control problems. Newton's method here is used for the solution of Bellman's equation, an operator equation that applies universally within DP with both discrete and continuous state and control spaces, as well as finite and infinite horizon.