Download PDF Make Your Own Neural Network
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Make Your Own Neural Network
Download PDF Make Your Own Neural Network
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Product details
Paperback: 222 pages
Publisher: CreateSpace Independent Publishing Platform; 1 edition (March 31, 2016)
Language: English
ISBN-10: 1530826608
ISBN-13: 978-1530826605
Product Dimensions:
8.5 x 0.5 x 11 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review:
4.4 out of 5 stars
258 customer reviews
Amazon Best Sellers Rank:
#53,405 in Books (See Top 100 in Books)
Who is Tariq Rashid? As I read more and more of this book, finding the answer to this question became paramount. I've read a lot of books, both fiction and non-fiction. In fact, I typically read a book a week. But never have I read a text which as so clear, and concise. Such a perfect marriage of simplicity and information about the subject. I actually didn't want to put the book down! Which is why I had to know about Mr. Rashid. It became clear to me that this man must be among the greatest teachers EVER!What would you normally expect when you buy a book like this? Lots of math? Plenty of dry paragraphs? Definitions on every page? Theorems and proofs? Well, if you do that in this case, you will be disappointed. Instead, what you find is that you will get exactly what you need to understand the topic, and put it into practice. No more, and no less.Mr. Rashid is an absolute master of communicating just what the reader needs to reach the point of understanding. And to get there, he uses a very simple, step-by-step approach. One that builds directly on what has gone before. No hiding the ball. No leaps in logic. Just a simple explanation that leaves no room for doubt (he works through simple examples to hammer needed points home). And at the end of the journey - and indeed, at many points along the way - where are you? I'll tell you where: you will be saying to yourself "I understand this! I got it! I am so happy I bought this book!"I got an inkling of what was to come when I read the appendices (e.g., about the basics of calculus). I figured the appendices in a book often tell you something about the author - what they decided to include, but not as part of the main text. When I finished them, I wondered why I never had a textbook, or even a teacher, like that? Why couldn't everyone explain things in this manner? I know when I give a lecture I try to make things interesting and easy to understand, but I suspect I've never reached this level, since I've never seen anything like it myself. And I've had almost 20 years in school!In the end, I have no doubt that I can use what I've learned. Indeed, I've got some nifty ideas about some ways to try this out, and am looking forward to doing so. So if you're thinking that the subject might be too difficult for you, I'm here to tell you that if you've got high school math, and even if you don't have a programming background, you can still do this!The bottom line is that if you want to learn about the basics of neural networks, THIS IS THE BOOK TO BUY! Don't even think about anything else. GET THIS BOOK!Oh yes, so who is Mr. Rashid? He's a man that is inspired to explain things to others. To really teach them, so that they can work to carry knowledge forward. But he is also a man that is too modest to publish anything about himself in his own books. You can read a little bit about him on amazon.com. That's what I did. He is truly an inspiration to me. Both in building my confidence in my own ability to learn this subject, and in restoring my confidence in others - proving to me that there are still plenty of people that truly want to do something good in this world.And Mr. Rashid, if you read this, I want you to know that you have not failed. No, you have definitely succeeded, in so many ways. Thank you for taking the time to do such great work with this book. I have never seen anything like it, and I hope you will be inspired to continue in your mission. Technical literature is not my favorite genre, not by a long shot, but in your case, I would be proud to stand first in line to buy whatever you care to write. It's that good!
I read several neutral or negative reviews about this book regarding the formatting. I am extremely pleased that I proceeded to purchase and read it despite those reviews.The author presented the core concepts of a simple neural network in the first section with a highly engaging style, taking the reader through each decision and clearly explaining the mathematics required with graphics a physical representation and text. The second section delved into Python code to exercise the concepts and walked through the code in sufficient detail that novice and experienced coders can easily understand the code. The final sections on Calculus refresher and Rasperry Pi are extra benefits for the casual reader. I am glad I elected to overlook the formatting situation, while was annoying at first - it is difficult to resize pages and do appear as PDF pages.
Very well written book with lots of explanatory images, charts, graphs, and a complete source code of a working neural network built step by step through the book. It takes the reader through building a real, working neural network without any required prior knowledge of complex math or any deep learning theory.The theory and the inner workings of the NN is explained first in a very approachable way, then in the next part the actual python program is being created, again in a very approachable and well explained way.My only regret is that it stops short from explaining some more advanced concepts like convolution layers, pooling layers, etc, that are found in modern neural networks for computer vision. Don't be put off by that though, it still explains A LOT of interesting and important concepts.For someone like me who didn't have a clue how NN work only a month ago this book was the perfect eye opener that gave me the foundations for future learning.Thanks Tariq, well done with the book!
This is a very nice introduction into Neural Networks. I have been recommending this to my friends and family. Even if you are afraid of the mathematics involved, the appendix in the book covers what you need to know in order to make sense of the math (most of it is simple algebra) with just a bit of derivatives that involve the chain rule. This is one of the few books that not only goes over the theory but also the step by step implementation (training your network to recognize handwritten numbers in Python) as well as testing the code and making minor tweaks to show how that will affect the overall accuracy of the network. For an added bonus, the author includes a chapter describing how you can train the network to recognize your own handwriting and things you can do to further increase the accuracy.Even though I highly recommend this book, there are a few grammatical errors as well as labels being incorrect in a few of the diagrams. You can still get through the book without the errors taking away from the content. I'm sure in future revisions, most of these errors will be corrected. Aside from that, I am pleased with my purchase of this book. Money well spent and I will continue referring back to this book.
The book itself can be painful to work through, as it is written for a novice, not just in algorithms and data analysis, but also in programming. For the neural network aspect, it jumped between overly simplistic and complicated, while providing neither in enough detail. That said, by the end I found it a worthwhile dive into neural networks, since once it got to the programming structure, it all made sense, but only because I stuck with it.
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