deep learning and the game of go

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Deep Learning And The Game Of Go

Author : Max Pumperla
ISBN : 1617295329
Genre :
File Size : 40. 66 MB
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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.

Artificial Neural Networks Icann 2001

Author : Georg Dorffner
ISBN : 9783540424864
Genre : Computers
File Size : 80. 99 MB
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This book constitutes the refereed proceedings of the International Conference on Artificial Neural Networks,ICANN 2001, held in Vienna, Austria in August 2001. The 171 revised papers presented together with three invited contributions were carefully reviewed and selected from around 300 submissions. The papers are organized in topical sections on data analysis and pattern recognition, theory, kernel methods, topographic mapping, independent component analysis, signal processing, time series processing, agent-based economic modeling, selforganization and dynamical systems, robotics and control, vision and image processing, computational neuroscience, and connectionist and cognitive science.

Exploratory Learning In The Game Of Go

Author : Barney Pell
ISBN : UCSC:32106010116629
Genre : Curiosity
File Size : 54. 48 MB
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Deep Learning With C Net And Kelp Net

Author : Matt R. Cole
ISBN : 9789388511018
Genre : Computers
File Size : 40. 82 MB
Format : PDF
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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: His LinkedIn Profile: His Blog:

Deep Learning For Dummies

Author : John Paul Mueller
ISBN : 9781119543039
Genre : Computers
File Size : 82. 13 MB
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Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.

Cloud Computing For Machine Learning And Cognitive Applications

Author : Kai Hwang
ISBN : 9780262036412
Genre : Computers
File Size : 55. 61 MB
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The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies.

Deep Learning For Nlp And Speech Recognition

Author : Uday Kamath
ISBN : 9783030145965
Genre :
File Size : 73. 11 MB
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The Deep Learning Ai Playbook

Author : Carlos Perez
ISBN : 9781365879234
Genre :
File Size : 38. 46 MB
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After Digital

Author : James A. Anderson
ISBN : 9780199357789
Genre : Computers
File Size : 67. 42 MB
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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 Essentials

Author : Anurag Bhardwaj
ISBN : 9781785887772
Genre : Computers
File Size : 77. 45 MB
Format : PDF, Kindle
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Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.

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