Full description not available
A**R
Five Stars
Great product!
B**N
When you want to progress
A few word about myself:I am a Analyst, I have a MSc. in Mathematics and Statistics and do analytics for a living. While I have studied about neural networks and machine learning a while ago, only past year have I (re)-discovered the power of neural nets and Deep Learning.In my quest to improve my knowledge, I have taken many certificates in ML and have bought a few books about Machine Learning. Among these are:-Python Machine Learning by Sebastian Raschka (recommended)-Building Machine Learning Systems with Python by by Luis Pedro Coelho and Willi Richert (nice to have for additional perspective)However, I wanted to go beyond what one can find in those two books. The topics I was specifically interested in were:-Deep Belief Networks (inc. Restricted Boltzmann Machine)-Autoencoders-Convolutional Neural NetworksSo where does Advanced Machine Learning rank among these?I must say, and that will be my main criticism of the book that it is not for the faint of heart. It is fast, sometimes too fast... I suppose there is so much you can put in 250 pages to explain about these topics, and it is easy to become lost.However, do not get me wrong. This book is a small gem in itself.Why? Because while I have found online many tutorials or courses about the topics I was interested, the book gives you additional information and explanations that I haven't found anywhere else. How do you set your hyper-parameters in a CNN? What is the depth exactly representing, what are the current architectures, are they really all that good? Why?It is the difference between the how and the more precise what and why. Tutorials online are great but many people just do things without clearly showing why. This books gives you the clues.In conclusion, for me currently (after having bought 8 books):The book is difficult but not super difficult. It gives more understanding and depth than I could ever obtain with all the material available online currently (including the very good Stanford courses). So, yes, I feel I am making progress.-Python Machine Learning by Sebastian Raschka is the way to go for Machine Learning foundations-Advanced Machine Learning with Python by John Hearty is a super helpful complement to what one can already find online dispersed all over the place, it just make sense with better hindsight.
E**N
One Star
Example codes are not working.Very difficult to debug and make them work.
G**D
Too many errata to even trust the content
The very first code sample has so many bugs in it, it won't even run (bugs on 8 lines out of 23). Once you fix the bugs, the graph it outputs doesn't match the graph in the book. Wait, looking closer, the graph in the book doesn't match the code in the book. Better check the downloadable code file from Packt. Wait, the downloaded code doesn't match the code in the book!With so many errors in *the very first code sample*, I'm setting this book down and stepping slowly away. I don't know if what this book is trying to teach me is even correct, much less useful. Perhaps the authors should learn basic coding and editing skills before, you know, machine learning?
