Simulating Neural Networks With Mathematica Download
This is a great starter for neural networks. It is both a tutorial on neural networks and you write the code as you go.I do not think it matters which version of Mathematica you are using. No previous knowledge is required.This book is structured so that the first few chapters introduce the concepts, and the are various applications. Even the classic Traveling salesperson. The last few chapters are some very specific applied theory. Adaptive Resonance Theory was very interesting.Overall this book is both a fun and educational book. Overall, I am quite happy with the book.
It does exactly as it describes.shows the reader how to use mathematica to simulate several types of Neural Networks. The code is clear, fairly short and the example networks fun to work though.
Simulating Neural Networks With Mathematica Download
The flexibilty of Mathematica made it a simple task to view what the networks were doing and thus made the networks easier to understand.My only complaint is that the book is too short. Part of this complaint is that I really enjoyed playing with the example nets and hated to see it end. However, only about 8 networks are mentioned and each is covered in 20-30 pages.program code included. I wish the author had time to double or triple the size of the book to cover the nets more thoroughly and to cover others.The book is also a bit shy on the mathematical treatment of the networks. It does have some math, but the derivations included are not that rigorous. I have supplemented this book with others to cover the theory.Nevertheless, If you're wanting to use Mathematica for Neural Networking and you're having difficulty getting started, this book is worth the price.
This book introduces neural networks, their operation and their application, in the context of Mathematica, a mathematical programming language. Readers will learn how to simulate neural network operations using Mathematica and will learn techniques for employing Mathematics to assess neural network behaviour and performance. It shows how this popular and widely available software con be used to explore neural network technology, experiment with various architectures, debug new training algorithms and design techniques for analyzing network performance.