Neural Networks for Electronics Hobbyists

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
  
  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.

Learn through a hardware-based approach to neural networks versus implementing a network in software No technical background or programming skills are required The in-book project is made from inexpensive, readily available parts

Autorentext
Richard McKeon has spent the last 40 years designing circuits and building communication networks. Currently living in Prescott, Arizona, Rick spends his time pursuing his passion for writing, playing music, and teaching. Some of his interests also include hiking, treasure hunting, recreational mathematics, photography and experimenting with microcontrollers.

Inhalt
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: Nueral Network Software Simbrain.- Appendix B: Resources.