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Make Your Own Neural Network, by Tariq Rashid
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A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language.
Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work.
This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included.
The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already!
You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks.
Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples.
Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals.
Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi.
All the code in this has been tested to work on a Raspberry Pi Zero.
- Sales Rank: #4114 in eBooks
- Published on: 2016-04-16
- Released on: 2016-04-16
- Format: Kindle eBook
Most helpful customer reviews
26 of 28 people found the following review helpful.
I haven't found anything like it.
By john kane
I'm a retired sociology PhD trying to make sense out of machine learning and AGI with 50 years since my last math class. This book is remarkable for its clarity and coverage of the subject....I haven't found anything like it.
27 of 30 people found the following review helpful.
Such authors and teachers are invaluable to the human race !
By David L. Parnell
Tariq Rashid reminds me of Isaac Asimov who could marvelously render a highly technical topic into an interesting narrative that any average human could read, enjoy, and understand the fundamentals of technology or science. While I had Googled many documents purporting to explain neural networks, none penetrated my brain in any meaningful way. However, as soon as I began reading Tariq Rashid’s book, “Make Your Own Neural Network,” I realized I had found an author and a book which could load an average, non-mathematical mind with an understanding of this topic of interest. Such authors and teachers are invaluable to the human race !
12 of 13 people found the following review helpful.
The author explains neural networks, a topic that is often made more confusing than it should be, in very simple terms.
By Nathan
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.
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