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Introduction to the Math of Neural Networks by [Heaton, Jeff]
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Introduction to the Math of Neural Networks Kindle Edition

3.8 out of 5 stars 4 customer reviews

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Length: 119 pages Enhanced Typesetting: Enabled Page Flip: Enabled

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Product details

  • 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
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  • Word Wise: Not Enabled
  • Enhanced Typesetting: Enabled
  • Average Customer Review: 3.8 out of 5 stars 4 customer reviews
  • Amazon Bestsellers Rank: #27,427 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Customer Reviews

3.8 out of 5 stars
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Top Customer Reviews

Format: Kindle Edition Verified Purchase
I have a degree in Mathematics but have not touched the subject for 10 years. I recently started a post graduate course, one of the modules is on Neural Networks. I purchased this book alongside 'An introduction to Neural Networks'.

Fortunately the mathematics of Neural Networks themselves is not complicated, though the training algorithms can be more involved. This book is:
well structured
takes you in gently
unpretentious

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.
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Format: Kindle Edition Verified Purchase
This is concise book that can be used for understanding the basics of Neural Network mathematics and how the propagations are managed. If you were lucky enough to have done maths at high school, it should not take you more than 20 minutes to complete reading the book.

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.
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Format: Kindle Edition Verified Purchase
While I can agree on the fact that the book will give you a gentle introduction to the topic (just what I needed), I find that too much detail has been devoted to the first chapters (what is an error and how to calculate a gradient). It is good to explain what a gradient is for people who have never come across that, but you don't need to spent 34% of the book explaining errors and gradients.

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.
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Format: Kindle Edition Verified Purchase
Jeff explains the basics of Neural Network maths in a style anyone can understand and at a level most college educated student will appreciate.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: HASH(0x891e6b04) out of 5 stars 29 reviews
29 of 29 people found the following review helpful
HASH(0x891fa564) out of 5 stars Excellent, but ...... 5 Aug. 2013
By David M. Stanwick - Published on Amazon.com
Format: Kindle Edition Verified Purchase
I'm not done reading yet but feel compelled to point out one problem to others: the display equations are too small, almost unreadable, on my Kindle Fire HD. The text of the book itself is adjustable in size, as usual, but these other elements are not: Figures, code fragments, console output, and the aforementioned display equations. The figures are about right in size, but the code and output is too large. But, again, the worst problem is that the equations are like a thumbnail of a thumbnail in size -- making subscript and superscript letters nearly invisible.

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.

Many thanks!
12 of 12 people found the following review helpful
HASH(0x891fa7b0) out of 5 stars Practical Math Applied to Neural Networks 18 Feb. 2013
By Robin T. Wernick - Published on Amazon.com
Format: Kindle Edition Verified Purchase
Its freshman calculus and applied math rolled together in a developing brew.that suggests but never leads to a specific process to design neural networks. This introduction is gentle and it will all make sense if you buy another few books on the subject. However, this is not a one stop shop for neural network design. The book is more a basic presentation of various mathematical tools that can be applied to neural networks.

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.
15 of 16 people found the following review helpful
HASH(0x891fa774) out of 5 stars Buy a different book 18 Nov. 2014
By Rick Ber - Published on Amazon.com
Format: Kindle Edition Verified Purchase
This book is very uneven. It devotes initial chapters to explaining some basic mathematical ideas (e.g., what an "error" is, what a derivative of is) in unnecessary detail, but abandons the attempt to imbue understanding when it gets to the meat of the book -- the math behind neural networks. It is as though he ran out of time and stopped trying to explain just when explanations were needed. The gradient decent of back propagation could have been explained without deriving derivatives, but instead any insights into the math were lost in a recitation of algorithmic steps. To illustrate how these algorithms work, the author includes poorly formatted computer outputs with which show values to 8 decimal places when one or two would have been fine: the result was essentially illegible and this made his explanations unintelligible. I was very disappointed.
10 of 10 people found the following review helpful
HASH(0x891faa68) out of 5 stars Great for anyone who is new to nerual networks and has relatively less math knowledge 18 Oct. 2012
By hjch - Published on Amazon.com
Format: Kindle Edition Verified Purchase
Unlike many neural network textbook that really focuses on difficult concepts, this book is straightforward and focuses on practical knowledge. Definitely worth $10. Lastly, although the book says that you only need to know high school algebra to understand it, I highly recommend that you have some knowledge of calculus (if you are interested in neural networks, you probably do already).
5 of 5 people found the following review helpful
HASH(0x891faac8) out of 5 stars I think all the pieces can be found within the book to make it a good self-contained piece of work 12 Mar. 2015
By Jason M. Nett - Published on Amazon.com
Format: Kindle Edition Verified Purchase
I think this book exhibits the downside of self-publishing. The content exhibits the expertise that Heaton likely has, but the writing and order of presentation needs a few more rounds of careful editing. Mathematical notation tends to change without notice and concepts are used without explanation. I think all the pieces can be found within the book to make it a good self-contained piece of work, but the writing clearly needs editing by a fresh pair of eyes.

I would instead suggest studying Richard Golden's and Sandhya Samarasinge's texts as a pair.
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