Grokking Deep Learning

This book came highly recommended to me by a colleague at work. I and others at work have agreed that it's a great primer to learn how machine learning works and how to build your first model. It walks through every step and detail with examples, code, and visuals to bring it all together.

Just arrived and diving in this week, the first impressions are that this is a deep dive on the mechanisms of Deep learning, but exceptional in the way the material is accessible to those without classical math background. You just need to devote some effort and basic reasoning and you should be plenty out of this book, Bon appetit ! I will update this if my description changes, this study effort will take a few weeks. Peace.

Andrew W. Trask has reminded me that most of us need to know more about Deep Learning. His book is a great way for people to get up to speed on the basics. His explanations are clear and fairly easy to digest. I like that he addresses several known issues with deep learning while providing many useful examples. While this may not be the best book for people with a Ph. D. in mathematics, it's a great introduction for the rest of us. Enjoy!

I'm going to be brief and list the good and the bad. * Good: 1- Easy to read (one of the best books to get you started) 2- Hands on approach to implementing Neural networks 3- Some introduction to popular AI libraries such as Numpy 4- Good guidance on next steps * Bad: 1- Syntax and coding problems that are easy to detect by a trained eye but not as easy for a novice learner 2- Some typographical errors throughout the book I have to clarify that book on its own is good. I think there are probably some items that could have been taken care of (I mentioned above) through the editing process but hopefully the next versions of the books can take care of these issues.

I rarely write reviews, but I have to here. This is an excellent book. It's concise, clear, and does an excellent job of conveying understanding and intuition. Having read bits and pieces about neutral networks over the years, I'm glad I picked up this book and gained conceptual understanding.

I started reading this book as an introduction to deep learning. The first half of the book does a good job of explaining the concepts and code, but around chapter ten the book becomes confusing. I ended up having to use other recourses just to try and understand what the author was trying to explain. I ended up starting a different book recommend by a friend in this field. I think this book needs some revision in a lot of ways. I am not sure if the code became hard to read because they used numpy or poor explanations.

This is a wonderful, plain-English discussion of the mechanics that go on under the hood of neural networks - from data flow to updating of weights. Specifically written without a slant on normally-wonky math, the concepts are presented and then advanced at a digestable pace for anyone. It makes for a wonderful textbook for a course, and should be required reading for product managers or marketing people getting into deep learning, alike.

I had high hopes for this book. With so many below-average books about ML, I have to say this book is better than those, but still not to my satisfaction. Some intro topics are discussed painfully detailed, but then some other more important topics are just mentioned. For example, back propagation and stochastic gradient descent are not explained in a satisfactory way.

Pour moi c'est le meilleur livre pour comprendre le fondement de deep learning comment fonctionne a l'interieur

This is a great introduction to deep learning. It throws light on the fundamental concepts of deep learning, and helps with practical code snippets. It gives some hands on experience to the reader to build simple models on realistic use cases. It provides progressive, code-first approach to reinforce the advanced concepts being discussed.

The author has done an astounding job at distilling the essence and complexities of the topic into a format that is almost child’s play. At first this book for me was a supplementary read whilst I did the fast.ai course, but since has become the main focus and resource for learning. Thank you Andrew for bringing this topic to the layman and propelling me Into this field.

Awesome book on Deep Learning. It builds up concepts in a lucid manner. The coverage is comprehensive and logical. Must for any one who is interested in Deep Learning.

Despite having a mathematical background (and having grokked many concepts of DL) I found this book insightful. On some shortcomings: I agree with some of the other reviewers that, having mastered the maths of DL, this approach might rather confuse. Maybe math is the simplest language in this regard. Personally I found a few points more distracting than this, specifically the backpropagation for the NN for sigmoids in chapter 11 (an approximation is used which is never explained) and the batch gradient descent in chapter 8 (just wrong). A minor point: Personally I prefer PEP8 coding standard, the code is also a bit unique and sometimes hard to read. On its strengths: This book invites to deal with DL deeply, try out for yourself, to learn from your mistakes (and maybe deliberately from shortcomings in the explanations). This is sometimes painful, yet this is the way you learn. Specifically the strongest points are the step-by-step explanations (e.g. backprop) where each step is executed "on paper". I very much liked the part on Autograd. Having Worked with PyTorch and TF for years I took many things for granted which, surprisingly, I really never understood. Building a framework step by step from ground up helped me a lot. An other great point are the many tips throughout the book (sometimes well hidden), which are also worded so that they invite deeper understanding (and more research into the topic). Overall (for me) the strong points outweigh the shortcomings by a wide margin.