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Publisher's Summary

Discover the power of reinforcement learning with exemplary case studies and clarified theories.
 

Reinforcement learning has to be one of the most prevalent study techniques in artificial intelligence. It is considered to be a computational approach to learning, which someone makes to attempt to capitalize on the total amount of rewards it obtains when cooperating with a multifaceted, inexact setting.

David Feldspar does an excellent job of explaining all the subtle parts of it, including topics like:
 

  • Teacher stickers and their function in the reinforcement learning process
  • Toddlers and how reinforced positive affirmations work best on them
  • The role of artificial intelligence in reinforcement learning methods
  • Why written group leaders matter for engagement and effective learning techniques 
  • GED study tips and processes
  • How to apply reinforcement learning theories and methods to homeschooling
  • And various other subtopics

Get educated about the positive effects of the long-discovered reinforcement learning methods, and become an expert at applying these approaches to your direct environment, students, children, and others.
 

©2018 David Feldspar (P)2018 David Feldspar

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  • Overall
    4 out of 5 stars
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    5 out of 5 stars

Well written

This excellent book is full of interesting examples, classification schemes, and figures.
Although this book will be more interesting for readers, which have been working in networking, it can be useful also for all researchers and students and any type of listeners interesting in data analysis.

1 of 1 people found this review helpful

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    4 out of 5 stars
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Best guidebook

This book is outstanding introduction for readers unfamiliar with the learning systems. The extended glossary and full-length reference list will help a lot for readers inexperienced in this area.

1 of 1 people found this review helpful

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    5 out of 5 stars
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    4 out of 5 stars

Useful book

The authority with which these topics are presented speaks volumes of the enormous research work undertaken by David Feldspar and his students. The references and extended glossary provided at the end are extremely useful to the reader. Another important aspect of this book is that it is suitable for all levels of readers such as student, researcher and practitioner.

1 of 1 people found this review helpful

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    4 out of 5 stars
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    5 out of 5 stars

Good listen.

It is well received by the students. It must be in the shelves of those who look for the latest research in the area of neural networks. I enjoyed listening this book. Finally, if the phrase "real-time neural networks" is also added in the tag line (sub title) of the book, it could attract more users.

1 of 1 people found this review helpful

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    4 out of 5 stars
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Well written

If you are interest about artificial intelligence and how to know the most prevalent study reinforcement learning, then I would like to say you are on the right place now and this guidebook will be so perfect for you without any hesitation. I heard about this book from a group discussion and after hearing from there, my curiosity grew up. I didn’t do too late to purchase it, even its price was affordable when I got it.

1 of 1 people found this review helpful