Download PDF Make Your Own Neural Network: An In-depth Visual Introduction For Beginners, by Michael Taylor
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Make Your Own Neural Network: An In-depth Visual Introduction For Beginners, by Michael Taylor
Download PDF Make Your Own Neural Network: An In-depth Visual Introduction For Beginners, by Michael Taylor
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Product details
Paperback: 248 pages
Publisher: Independently published (October 4, 2017)
Language: English
ISBN-10: 1549869132
ISBN-13: 978-1549869136
Product Dimensions:
6 x 0.6 x 9 inches
Shipping Weight: 12.8 ounces (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
76 customer reviews
Amazon Best Sellers Rank:
#49,398 in Books (See Top 100 in Books)
This is an excellent book which covers a complex topic such as Neural Networks - I started reading this book with no knowledge of Neural networks, but after reading it, I can say that I do understand the principles of Neural Networks, a second or 3rd read and I can be a probationer/developer/researcher - yes this book is comprehensive on the theory, practicals, tools and processes to get anyone started off on Neural Networks.First of all let us understand the Target Audience - this book is a lot of math, it will work even if you have a good background in Math that is enough to understand the concepts and understand, but if you are a totally from a non-math background, this is not for you. I graduated about 22 years back and totally out of touch with theory, but I was able to understand it, so some background/concept is necessary, but if you are really good in math - this book really goes deep into it.The author does a great job in simplifying the concepts, example a partial derivative is beautifully explained as "enables you a measure how a single variable out of many impacts another single variable" and chain rule is explained as "Discovering the error of the specific weight is an important aspect of training the networks"When I first read the first few chapters of the book, it felt that this was going nowhere - there were concepts around nodes, weights, error ... some of which I was able to understand and some I could not. So but it really all came together when I was on the practical example - a neural network which can read a image and determine if it is a chicken or a man. It explains a simple 64 pixel image, each pixel contains a number which represents the color and based on the color - we should arrive at 0 for chicken and 1 for man. And how we can keep on adjusting weights until we arrive at the right answer and minimize error.Essentially the image is reduced to a single number and that single number is derived by assigning weights assigned to each pixel - to me this was poetic and achieved my NNN (Neural Network Nirvana) 33,000 feet above ground while I read this book on my way back from on an international trip !
You will understand what a neural network is, how it works, and how to create one.First, a caveat: “For Beginners†does mean to beginners to the topic of neural networks, not to all topics per se. Required skills include a decent grounding in algebra, statistics, and calculus. My experience found the algebra foundational, the statistics very friendly (I’ve used statistics a lot in some of my hobbies), and the calculus somewhat daunting and alien, having not touched the subject since my schooldays.However, fear not, as Taylor does link to (free) Khan academy courses for any of those fields, should you need a refresher and/or primer. Myself, I found it more convenient to dig out an old Open University maths textbook for this purpose, but your mileage may vary.Towards the latter part of the book, Taylor fulfils his promise of walking us through creating our first neural network using Python. For those not familiar with this programming language, again, resources are linked, though in this case your needs would be more grounded in the peripheral tools, since the language itself he explains, at least insofar as everything used in this project, term by term, line by line. Can’t go wrong. Frankly, I found it more elucidating than my first foray into Python some years back, and that was from a purpose-built “Learn Python†app.Right at the end are a number of appendices including two glossaries of terms; if you are (or suspect you may be) hazy over terms, it couldn’t hurt to perhaps read those before the rest of the book, as while Taylor explains many things as he goes, there are some things that I didn’t grasp as quickly as I would have had I read the glossaries first.All in all, fantastic book, of great value to those such as myself who try to maintain a conversant hobby interest in such matters, without having the time or resources to devote to a more comprehensive study — or indeed, for those simply looking for an “in†to a topic that can be made quite opaque by the tendency of those who write about it to assume more knowledge of the field than we have. So yes, this on the contrary is a work of great value, and I look forward to enjoying more from this author.
This book is described as an in-depth introduction to neural networks which defines neural networks, describes how they work, and explains how to build one. While it certainly covers all these topics, I found this book to be very difficult overall. I do not have a background in Python or higher level math, and while I could grasp the basic concepts, I was honestly very lost for a good portion of the book.At the same time, I did appreciate the author’s attempts to make neural networks understandable to a lay audience. Neural networks are becoming more and more relevant and it is definitely worthwhile to have some grasp of what they are and how they work. I think this book is best for people who have a background in a programming language like Python and a strong foundation in algebra, statistics, and calculus. Since I do not have this background I would prefer to learn more about the general concept of a neural network and less of the mathematical and programming aspects of them. Overall, it was an interesting and informative book, but I personally found it too advanced to be a beginner’s guide.
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