I am finished with the number of chapters that have been released so far There have been three in total The material is a little rough but it is an early release One should have some basic understanding of statistics and probability before attempting to digest the material Looking forward to the additional chapters. This book strikes a good balance between the DL textbooks which are quite dense and the many practitioners guides which have code examples but are light on theory math There are equations here as well as code I ve been checking this one out from the library, but I m going to go ahead an order my own copy This book strikes a good balance between the DL textbooks which are quite dense and the many practitioners guides which have code examples but are light on theory math There are equations here as well as code I ve been checking this one out from the library, but I m going to go ahead an order my own copy If you expect code example, you would be disappointed This book is very good at covering fundamentals, which I like I suggest this book as a supplement with other deep learning book. Strengths Gives a really good overview of computer vision history and why traditional machine learning methods don t perform as good as convolutional networks The section that talks about Gradient Descent is really well explained and destroy some myths around gradient descent even though there is no math Gives a clear and intuitive idea of how convolutional layers can capture patterns in images It includes attention methods for NLP Weaknesses Lacks math and precise definitions but that Strengths Gives a really good overview of computer vision history and why traditional machine learning methods don t perform as good as convolutional networks The section that talks about Gradient Descent is really well explained and destroy some myths around gradient descent even though there is no math Gives a clear and intuitive idea of how convolutional layers can capture patterns in images It includes attention methods for NLP Weaknesses Lacks math and precise definitions but that is ok if the book was done for beginners It uses tensorflow for all examples which turns hard and cumbersome for beginners It doesn t talk about other frameworks some of the examples could have been written on top of tensorflow but using keras tensorlearn or using pytorch Code Snippets are long, hard to follow and sometimes present errors Some images have font size really small which turns impossible to read not read chapter 8 good start point to read open AI gym This book does not provide much details about each algorithm It basically just mentions what it is Therefore, read multiple books at the same time is a great help to understand how deep learning works Some codes syntax are old and should be corrected However, it definitely worths time reading the example codes.