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Author: Arthur Stone Publisher: ISBN: Category : Languages : en Pages : 526
Book Description
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.
Author: Kevin Ferguson Publisher: Simon and Schuster ISBN: 1638354014 Category : Computers Languages : en Pages : 611
Book Description
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
Author: Modiphius Publisher: Modiphius ISBN: 9781910132647 Category : Languages : en Pages :
Book Description
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.
Author: Hao Dong Publisher: Springer Nature ISBN: 9811540950 Category : Computers Languages : en Pages : 526
Book Description
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.
Author: Sebastian Raschka Publisher: Packt Publishing Ltd ISBN: 1801816387 Category : Computers Languages : en Pages : 775
Book Description
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.
Author: Mark Doten Publisher: Graywolf Press ISBN: 1555978282 Category : Fiction Languages : en Pages : 303
Book Description
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.
Author: Jixue Liu Publisher: Springer Nature ISBN: 3030352889 Category : Computers Languages : en Pages : 622
Book Description
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.
Author: Dominik Klein Publisher: Independently Published ISBN: Category : Languages : en Pages : 268
Book Description
Deep Neural Networks have revolutionized computer engines for Go, Shogi and chess. Finally computers are able to evaluate a game position similiar to the way human experts do it. By that, computers are able to identify long-term strategic advantages and disadvantages. But how do chess engines based on neural networks such as AlphaZero, Leela Chess Zero actually work? This book gives an answer to that question. With lots of practical examples and illustrations, all basic building blocks that are required to understand modern chess are introduced. Based on that, the concepts of both classic and modern chess engines are explained. Finally, a miniature version of AlphaZero to play the game Hexapawn is implemented in Python. Chapters include: Single-Layer and Multilayer Perceptrons, Back-Propagation and Gradient Descent, Classification and Regression, Network Vectorization, Convolutional Layers, Squeeze and Excitation Networks, Fully Connected Layers, Batch Normalization, Rectified Linear Unit (ReLU), Residual Layers, Minimax, Alpha-Beta Search, Monte-Carlo Tree Search, AlphaGo, AlphaGo Zero, AlphaZero, Leela Chess Zero (Lc0), Fat Fritz, Effectively Updateable Neural Networks, Fat Fritz 2, Maia, Supervised Learning Hexapawn, Reinforcement Learning of Hexapawn (Hexapawn Zero)
Author: Matthew Sadler Publisher: Gambit Publications ISBN: 9781910093832 Category : Chess Languages : en Pages : 0
Book Description
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.
Author: Matthew Sadler
Author: Matthew Sadler
Author: Arthur Stone
Author: Kevin Ferguson
Author: Modiphius
Author: Hao Dong
Author: Sebastian Raschka
Author: Mark Doten
Author: Jixue Liu
Author: Dominik Klein
Author: Matthew Sadler