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I think the book is still too long. Some of the passages are huge, with a long long blocks of text. There's a lot of filler words in there like 'which is a bummer', and a lot of 'say...' dot dot dots. As a reader, you need to spend mental energy in trying to figure out what is the key essence you are trying to convey in each paragraph--this would be fine if the book is dense to begin with, but since you are trying to make this a concise intro, maybe it would be best to reduce this mental energy requirement to the absolute minimum
A bit if feedback is, read the paragraphs your wrote, ask yourself 'what is the exact point I am trying to convey here?', and the remove words that can be removed, without taking away from the point. You want the paragraph to be as short and concise is possible, since that's what makes your book different from all the other 'dense' books out there on the same topic.
If someone in the book store opens up your book and 'skims', if your paragraphs are clearly small, with lots of whitespace between paragraphs, then even without reading the text content in detail, the person can immediately tell your book is special, and completely different from all the other books on NN in the bookstore, and will be more likely to buy it right away.
Grokking Deep Learning is the perfect place to begin the deep learning journey. Rather than just learning the “black box” API of some library or framework, readers will actually understand how to build these algorithms completely from scratch. Grokking Deep Learning download ebook PDF EPUB, book in english language [DOWNLOAD] Grokking Deep Learning in format PDF Grokking Deep Learning download free of book in format PDF. Grokking Deep Learning Pdf Artificial Intelligence is the most exciting technology of the century, and Deep Learning is, quite literally, the “brain” behind the world’s smartest Artificial Intelligence systems out there.
I think at the beginning it was great. The use of everyday examples and analogies made it very quick and simple for someone reading it to 'get' what you mean. But as the chapters progressed, e.g. chapter 3, the text gets more dense and examples get fewer--in the later chapters it looks like you got more excited about the technical details, and the calculus math etc, and the later chapters no longer seem to relate to examples as much or are as concise as before. The later chapters look more and more similar to the other denser literatures in the field
In any case, overall it's a great book. It's very unique I have never seen this condensed approach before, and very special compared to all the other NN literatures out there. It's very refreshing. I'm sure it will inspire a new generation of machine scientists who will remember this for years to come!
Thanks for writing this!
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