Deep Learning and the Game of Go PDF Download

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Deep Learning and the Game of Go

Deep Learning and the Game of Go PDF Author: Max Pumperla
Publisher: Manning Publications
ISBN: 9781617295324
Category :
Languages : en
Pages : 325
Book Description
It's nearly impossible to build a competent Go-playing machine using conventional programming techniques, let alone have it win. By applying advanced AI techniques, in particular deep learning and reinforcement learning, users can train their Go-bot in the rules and tactics of the game. Deep Learning and the Game of Go opens up the world of deep learning and AI by teaching readers to build their own Go-playing machine. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Deep Learning and the Game of Go

Deep Learning and the Game of Go PDF Author: Max Pumperla
Publisher: Manning Publications
ISBN: 9781617295324
Category :
Languages : en
Pages : 325
Book Description
It's nearly impossible to build a competent Go-playing machine using conventional programming techniques, let alone have it win. By applying advanced AI techniques, in particular deep learning and reinforcement learning, users can train their Go-bot in the rules and tactics of the game. Deep Learning and the Game of Go opens up the world of deep learning and AI by teaching readers to build their own Go-playing machine. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Deep Learning and the Game of Go

Deep Learning and the Game of Go PDF Author: Kevin Ferguson
Publisher: Simon and Schuster
ISBN: 1638354014
Category : Computers
Languages : en
Pages : 384
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

PRICAI 2018: Trends in Artificial Intelligence

PRICAI 2018: Trends in Artificial Intelligence PDF Author: Xin Geng
Publisher: Springer
ISBN: 3319973045
Category : Computers
Languages : en
Pages : 1086
Book Description
This two-volume set, LNAI 11012 and 11013, constitutes the thoroughly refereed proceedings of the 15th Pacific Rim Conference on Artificial Intelligence, PRICAI 2018, held in Nanjing, China, in August 2018. The 82 full papers and 58 short papers presented in these volumes were carefully reviewed and selected from 382 submissions. PRICAI covers a wide range of topics such as AI theories, technologies and their applications in the areas of social and economic importance for countries in the Pacific Rim.

Generatives Deep Learning

Generatives Deep Learning PDF Author: David Foster
Publisher:
ISBN:
Category :
Languages : de
Pages : 310
Book Description
Generative Modelle haben sich zu einem der spannendsten Themenbereiche der Künstlichen Intelligenz entwickelt: Mit generativem Deep Learning ist es inzwischen möglich, einer Maschine das Malen, Schreiben oder auch das Komponieren von Musik beizubringen - kreative Fähigkeiten, die bisher dem Menschen vorbehalten waren. Mit diesem praxisnahen Buch können Data Scientists einige der eindrucksvollsten generativen Deep-Learning-Modelle nachbilden wie z.B. Generative Adversarial Networks (GANs), Variational Autoencoder (VAEs), Encoder-Decoder- sowie World-Modelle. David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt. Die zahlreichen praktischen Beispiele und Tipps helfen dem Leser herauszufinden, wie seine Modelle noch effizienter lernen und noch kreativer werden können.

Deep Learning with C#, .Net and Kelp.Net

Deep Learning with C#, .Net and Kelp.Net PDF Author: Matt R. Cole
Publisher: BPB Publications
ISBN: 9388511018
Category : Computers
Languages : en
Pages : 414
Book Description
Get hands on with Kelp.Net , Microsoft’s latest Deep Learning framework Key Features Deep Learning Basics The ultimate Kelp.Net reference guide Develop state of the art deep learning applications C# Deep Learning code Develop advanced deep learning models with minimal code Develop your own advanced Deep Learning models Loading and Saving Deep Learning Models Comprehensive Kelp.Net reference Sample Deep Learning Models and Tests OpenCL Reference Easily add deep learning to your applications Many sample models and tests Intuitive and user friendly Description Deep Learning with Kelp.Net is the ultimate reference for C# .Net developers who are passionate about deep learning. Readers will learn all the skills necessary to develop powerful, scalable and flexible deep learning models from a fluid and easy to use API. Upon completing the book the reader will have all the tools necessary to add powerful deep learning capabilities to their new or existing applications. What you will learn In-depth knowledge of Kelp.Net How to develop Deep Learning models C# Deep Learning programming Open-Computing Language (OpenCL) Loading and saving Deep Learning models How to develop and use activation functions How to test Deep Learning models Who This Book is For This book targets C# .Net developers who are passionate about deep learning yet want to do so from an easy and intuitive API. Table of Contents Introduction ML/DL Terms and Concepts Deep Instrumentation Kelp.Net Reference Loading and Saving Models Model Testing and Training Sample Deep Learning Tests Creating Your Own Deep Learning Tests Appendix A: Evaluation Metrics Appendix B: OpenCL About the Author Matt R. Cole is a seasoned developer and published author with over 30 years’ experience in Microsoft Windows, C, C++, C# and .Net. He is the owner of Evolved AI Solutions, a premier provider of advanced Machine Learning/Bio-AI technologies. He developed the first enterprise grade MicroService framework written completely in C# and .Net, which is used in production by a major hedge fund in NYC. He also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. He continues to push the limits of Machine Learning, Biological Artificial Intelligence, Deep Learning and MicroServices. In his spare time Matt loves to continue his education and contribute to open source efforts such as Kelp.Net. His Website: www.evolvedaisolutions.com His LinkedIn Profile: www.linkedin.com/in/evolvedai/ His Blog: www.evolvedaisolutions.com/blog.html

The Handbook of Data Science and AI

The Handbook of Data Science and AI PDF Author: Stefan Papp
Publisher: Carl Hanser Verlag GmbH Co KG
ISBN: 1569908877
Category : Computers
Languages : en
Pages : 573
Book Description
Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring: - A comprehensive overview of the various fields of application of data science - Case studies from practice to make the described concepts tangible - Practical examples to help you carry out simple data analysis projects - BONUS in print edition: E-Book inside The book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term. Contains these current issues: - Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms. - Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice - Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies - Computer vision: How can we gain insights from images and videos with data science? - Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios. - ML and AI in production: How to turn experimentation into a working data science product? - Presenting your results: Essential presentation techniques for data scientists

Learning to Play

Learning to Play PDF Author: Aske Plaat
Publisher: Springer Nature
ISBN: 3030592383
Category : Computers
Languages : en
Pages : 330
Book Description
In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI). After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography. The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.

After Digital

After Digital PDF Author: James A. Anderson
Publisher: Oxford University Press
ISBN: 0199357803
Category : Psychology
Languages : en
Pages : 288
Book Description
Current computer technology doubles in in power roughly every two years, an increase called "Moore's Law." This constant increase is predicted to come to an end soon. Digital technology will change. Although digital computers dominate today's world, there are alternative ways to "compute" which might be better and more efficient than digital computation. After Digital looks at where the field of computation began and where it might be headed, and offers predictions about a collaborative future relationship between human cognition and mechanical computation. James A. Anderson, a pioneer of biologically inspired neural nets, presents two different kinds of computation-digital and analog--and gives examples of their history, function, and limitations. A third, the brain, falls somewhere in between these two forms, and is suggested as a computer architecture that is more capable of performing some specific important cognitive tasks-perception, reasoning, and intuition, for example- than a digital computer, even though the digital computer is constructed from far faster and more reliable basic elements. Anderson discusses the essentials of brain hardware, in particular, the cerebral cortex, and how cortical structure can influence the form taken by the computational operations underlying cognition. Topics include association, understanding complex systems through analogy, formation of abstractions, the biology of number and its use in arithmetic and mathematics, and computing across scales of organization. These applications, of great human interest, also form the goals of genuine artificial intelligence. After Digital will appeal to a broad cognitive science community, including computer scientists, philosophers, psychologists, and neuroscientists, as well as the curious science layreader, and will help to understand and shape future developments in computation.

Deep Learning and Practice with MindSpore

Deep Learning and Practice with MindSpore PDF Author: Lei Chen
Publisher: Springer Nature
ISBN: 9811622337
Category : Computers
Languages : en
Pages : 394
Book Description
This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.

AI, Deep Mind and Reinforcement learning | Basic explanation absolutely for beginners | Get 3 amazing projects source code

AI, Deep Mind and Reinforcement learning | Basic explanation absolutely for beginners | Get 3 amazing projects source code PDF Author: Vibhas Mahaldar
Publisher: Vibhas Mahaldar
ISBN:
Category : Computers
Languages : en
Pages :
Book Description
This book is based on Artificial Intelligence and Machine Learning in this book have 2 parts, First part is about full introduction of Artificial Intelligence and second part is about Deep Mind and Reinforcement learning. 1 part : In this part we learn about Artificial Intelligence subsets like · AI explanation · Understanding AI · Categorization of AI · Special Consideration · Applications of AI around us/Details · Understanding AI, ML, DL/Details · Introduction to AI domains/Details · AI Ethics explanation · Why are AI ethics important? · What are the ethical challenges of AI? · Game Time · Python game Stone, Paper and Scissor . SDG Goals/Details 2 part : in this part we learn about Deep Mind and Reinforcement learning and there subset like Learn Machine to walk · Explanation Alpha Go · Explanation · History · defeated Go Players Reinforcement Learning · Explanation · Bipedal Walker (Example 1) · Solving the environment · Training visualization (Slightly uneven terrain) · Training visualization (Hardcore terrain) · DDPG network architecture · Example 2 · Q Values, and Q Learning Deep Q Network · Explanation Neural Network · Biological Neural Networks · Artificial neural networks (ANNs) · Training . Snake game · Explanation · Action · State · Reward Introduction to Google Colab · Explanation · Uploading Files Code · Upload and unzip · Explanation of code . Source code And this book is absolutely for beginners and also get the 3 amazing projects source code.