Neural Networks for Electronics Hobbyists: A Non-Technical Project-Based Introduction Front Cover

Neural Networks for Electronics Hobbyists: A Non-Technical Project-Based Introduction

  • Length: 139 pages
  • Edition: 1st ed.
  • Publisher:
  • Publication Date: 2018-06-10
  • ISBN-10: 1484235061
  • ISBN-13: 9781484235065
  • Sales Rank: #1821193 (See Top 100 Books)
Description

Learn how to implement and build a neural network with this non-technical, project-based book as your guide. As you work through the chapters, you’ll build an electronics project, providing a hands-on experience in training a network.

There are no prerequisites here and you won’t see a single line of computer code in this book. Instead, it takes a hardware approach using very simple electronic components. You’ll start off with an interesting non-technical introduction to neural networks, and then construct an electronics project. The project isn’t complicated, but it illustrates how back propagation can be used to adjust connection strengths or “weights” and train a network.

By the end of this book, you’ll be able to take what you’ve learned and apply it to your own projects. If you like to tinker around with components and build circuits on a breadboard, Neural Networks for Electronics Hobbyists is the book for you.

What You’ll Learn

  • Gain a practical introduction to neural networks
  • Review techniques for training networks with electrical hardware and supervised learning
  • Understand how parallel processing differs from standard sequential programming

Who This Book Is For

This book anyone interest in neural networks, from electronic hobbyists looking for an interesting project to build, to a layperson with no experience. Programmers familiar with neural networks but have only implemented them using computer code will also benefit from this book.

Table of Contents

Chapter 1: Biological Neural Networks
Chapter 2: Implementing Neural Networks
Chapter 3: Electronic Components
Chapter 4: Building the Network
Chapter 5: Training with Back Propagation
Chapter 6: Training on Other Functions
Chapter 7: Where Do We Go from Here?
Appendix A: Neural Network Software, Simbrain
Appendix B: Resources

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