- Format: Kindle Edition
- File Size: 1129 KB
- Print Length: 119 pages
- Simultaneous Device Usage: Unlimited
- Publisher: Heaton Research, Inc. (3 April 2012)
- Sold by: Amazon Media EU S.à r.l.
- Language: English
- ASIN: B00845UQL6
- Text-to-Speech: Enabled
- Word Wise: Not Enabled
- Average Customer Review: 4 customer reviews
- Amazon Bestsellers Rank: #27,427 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Introduction to the Math of Neural Networks Kindle Edition
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Top Customer Reviews
Fortunately the mathematics of Neural Networks themselves is not complicated, though the training algorithms can be more involved. This book is:
takes you in gently
It seeks to equip you with the maths required as you move through the book. As a result, the book demolishes the barriers to learning what can at first seem an impenetrable subject.
This book will not make you a Neural networks expert, nor will it equip you with the skills required to program one. It will however bring your understanding of the mathematics that sit behind Neural Networks to a level that allows you much easier access to more complex volumes/material. A very good place to start.
It also gives you an insight of the error handling methodology with which to create a Neural Network that helps to readjust the delta weights.
However, if you do not have a basic understanding of differential equations and used to avoid maths classes, when you were at school, you may find difficult to understand what the author is talking about. In this case, before diving into this book, it is better to spend some time on Khan Academy or other equivalent sites. Hence, the reason I gave this book three stars.
One think I like about Jeff Heaton’s work is that he has been trying to educate us about the Neural Networks for a while now.
That would all be good if you are really just looking for an introduction to understand neural networks but don't want to program them. However, if you do want to program a neural network, then you will find that some necessary details are not fully explained or contradictory.
In one chapter it is not clear how to calculate the weights for example so I assume it is done as in the previous chapter. If that were the case then the example given in that chapter is wrong.
Unfortunately (although the necessary math is mostly given), there is no clear algorithm given which clear states how to proceed. As I said, most steps are given in form of equations (and then the equation is explained rather redundantly for example the equation: delta=1 for c>0, delta=-1 for c<0, delta=0 for c=0 would then be explained "if the value of c is greater then 1, delta will take the value of 1, if the value of c is less then 1, delta will take the value of -1 and finally if the value of c is equal to 0 then delta will be 0 as well.) Some people will find it probably helpful to have equations explained in length but my point is that, removing this redundant information (and ignoring the examples for now), the book would probably have an equal amount of information about error and gradient calculation compared to the actual neural network information.Read more ›
Most Helpful Customer Reviews on Amazon.com (beta)
I've had similar issues with other technical books on the Kindle, but this is the worst example so far. If the author could boost the size of the equations 4x and perhaps reduce the size of the the code/program output, and reissue this otherwise very helpful guide, it would be greatly appreciated.
The book requires the reader to have familiarity with basic calculus and derivatives. Otherwise it's a walk in the park. After all, you are going to have to stretch a bit to benefit from this knowledge, so the effort is worth it. The title does say that this is an introduction, so don't be too disappointed that everything isn't laid out for you. There is no effort to categorize the functions introduced or to produce an overall design process of selection appropriate for a particular task. That information comes from further study in this area. Happy hunting.
I would like to praise the author for making the functional application and training of the neural network simple enough to get the reader started. This is a complex and potentially confusing area of applied mathematics. As is advised in these areas, it is best to learn to walk before trying to run. This book will help the reader walk correctly along this path.
Once you understand the proper processes, the world of application will open up for you. This material can be used in pattern recognition, financial forecasting image resolving, flight combat, and vehicle ride control. Buckle up and enjoy the flight.
I would instead suggest studying Richard Golden's and Sandhya Samarasinge's texts as a pair.