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Three problems with this book. (1) The Kindle version does not render the code well. The code wraps and indentation does not hold up as the page width varies, so it's impossible to tell whether a line of code belongs to one level of indentation or another. Sure, I can download the code from the repo and look at it in Jupyter, but what's the use of the book about code if you can't read the code? (2) The text between the code chunks is frequently trivial, and in many cases should have just been comments in the code. e.g. "And finally, here's the code for training the model with minibatches:" (3) The book assumes a moderately high level of understanding of TensorFlow. Do not buy this book unless you already have a strong understanding of terms like "dropout", "flattenizer", "softmax", and "leaky ReLU activation". Terms like these are used continuously without explanation. If you don't already understand them, this book will not help you understand TensorFlow.
Given (3), I have to wonder who the target audience is for this book - presumably if the reader already understands TensorFlow they will have access to code examples for different types of project. If the reader has data science skills but does not understand TensorFlow, it's not helpful. This book missed out on an important niche of helping data scientists learn TensorFlow through practical projects.