Neural Networks: A Practical Guide for Understanding and Programming Neural Networks and Useful Insights for Inspiring Reinvention

By Steven Cooper

☆★The Best Neural Networks Book for Beginners★☆

If you are looking for a complete beginners guide to learn neural networks with examples, in just a few hours, then you need to continue reading.

Have you noticed the increasing prevalence of software that tries to learn from you? More and more, we are interacting with machines and platforms that try to predict what we are looking for. From movie and television show recommendations on Netflix based on your taste to the keyboard on your smartphone trying to predict and recommend the next word you may want to type, it's becoming obvious that machine learning will definitely be part of our future.
If you are interested in learning more about the computer programs of tomorrow then, Understanding Neural Networks – A Practical Guide for Understanding and Programming Neural Networks and Useful Insights for Inspiring Reinvention is the book you have been waiting for. 
 

★★ Grab your copy today and learn ★★

♦ The history of neural networks and the way modern neural networks work
♦ How deep learning works
♦ The different types of neural networks
♦ The ability to explain a neural network to others, while simultaneously being able to build on this knowledge without being COMPLETELY LOST
♦ How to build your own neural network!
♦ An effective technique for hacking into a neural network
♦ Some introductory advice for modifying parameters in the code-based environment
♦ And much more...

You'll be an Einstein in no time! And even if you are already up to speed on the topic, this book has the power to illustrate what a neural network is in a way that is capable of inspiring new approaches and technical improvements. The world can't wait to see what you can do! 
 

Most of all, this book will feed the abstract reasoning region of your mind so that you are able to theorize and invent new types and styles of machine learning. So, what are you waiting for? Scroll up and click the buy now button to learn everything you need to know in no time!

• Language: English
• Publisher: Steven Cooper
• Release date: Nov 6, 2018

Book preview

Table of Contents

Neural Networks

Table of Contents

Preface

Introduction

Origin

Computer Thinking

Components of a Neural Network

Nodes

Layers

Training and Back Propagation

Weights and Biases

Net Types and Best Applications

Building a Neural Network

Neural Networks and Data Analytics

Common Mistakes to Avoid

Conclusion

About the Author

Text Copyright © Steven Cooper

All rights reserved.

No part of this guide may be reproduced in any form without permission in writing from the publisher except in the case of review.

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This declaration is deemed fair and valid by both the American Bar Association and the Committee of Publishers Association and is legally binding throughout the United States.

Furthermore, the transmission, duplication or reproduction of any of the following work including specific information will be considered an illegal act irrespective of if it is done electronically or in print. This extends to creating a secondary or tertiary copy of the work or a recorded copy and is only allowed with an express written consent from the Publisher. All additional right reserved.

The information in the following pages is broadly considered to be a truthful and accurate account of facts, and as such any inattention, use or misuse of the information in question by the reader will render any resulting actions solely under their purview. There are no scenarios in which the publisher or the original author of this work can be in any fashion deemed liable for any hardship or damages that may befall them after undertaking information described herein.

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Preface

The main purpose of this book is to provide the reader with the most elementary knowledge of neural networks fundamentals so that they can understand what these are all about.

Book Objectives

This book will help you:

Know more about the fundamental principles of neural networks and how to understand and program neural networks in more detail.

Have an elementary grasp of neural network concepts and tools that will make this work easier to do.

Have achieved a technical background in neural networks and deep learning and appreciate its power.

Target Users

The book is designed for a variety of target audiences. The most suitable users would include:

Newbies in computer science techniques and deep learning

Professionals in neural networks and deep learning

Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way

Students and academicians, especially those focusing on neural networks and deep learning

Is this book for me?

This book is for those who are interested in neural networks and deep learning. There are a lot of skills that a data scientist needs, such as coding, intellectual mindset, eagerness to make new discoveries, and much more.

It’s important that you are interested in this because you are obsessed with this kind of work. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, then this book is for you.

Introduction

Despite how it's portrayed in books and movies, artificial intelligence is not a synthetic brain floating in a case of blue liquid somewhere. It is an algorithm -- a mathematical equation that tells a computer what functions to perform... In the world of AI, the Holy Grail is to discover the single algorithm that will allow machines to understand the world -- the digital equivalent of the Standard Model that lets physicists explain the operations of the universe. - Jeff Goodell

The multiplication of complex mathematical matrices is an idea that may have your eyelids feeling heavy already. It's quite common to see no practical use for performing such an abstract function. Even with the assistance of a computer, what purpose could be combining these arbitrary data sets serve?

It was discovered that we could define a computer program capable of a superficial, simulated version of learning. The key difference in nature between this new style of programming versus its predecessor is that conventional computer programs contained specific rules and operations to be performed upon execution of the program. Once the program was coded by its engineer(s), the code was locked into place until an update or software patch was released. The advent of computer code possessing the capacity to learn marked a new category of software in which a developer could write an initial function, though, over time, the function would correct, optimize, and rewrite itself. We call these types of software as neural networks, and their special ability is called machine learning.

Have you noticed the increasing prevalence of software that tries to learn from you? More and more, we are interacting with machines and platforms that try to predict what we are looking for. From movie and television show recommendations on Netflix based on your taste to the keyboard on your smartphone trying to predict and recommend the next word you may want to type, it's becoming obvious that machine learning will definitely be part of our future.

You have selected a wonderful time to get involved engaging and improving this amazing technology. Historically, this field has seen its growth in spurts. It is safe to say that, from here, neural networks should continue to experience steady growth. While that is an exciting news, this book will challenge you to accelerate the

Reviews for Neural Networks

[Dimal Patel · Rating: 2 out of 5 stars]

Would of like some matrix math examples of the concepts

[Ivan Miskelic · Rating: 5 out of 5 stars]

Gives a nice quick introduction to concepts related to Neural Networks. Uses story-like descriptions to talk about training techniques and reward functions. Perhaps it's too short, but it's only meant to be an introduction anyway.

[Sathish K Vijayagopalan · Rating: 5 out of 5 stars]

A fabulous book on Neural Networks. A must read for beginners.