Grokking Deep Learning First Edition

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.

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!

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.

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 networks3- Some introduction to popular AI libraries such as Numpy4- 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 learner2- Some typographical errors throughout the bookI 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.

Covered my expectations