Deep learning con Python. Imparare a implementare algoritmi di apprendimento profondo

Based on the simple metric of frequency / density of highlighted passage, this text is by far the best book have ever read on subject of machine learning (or most subjects for that matter). The language is clear and easy to follow, even the coding language for that matter - Keras, the machine learni Leggi tutto

This book focuses on hands-on approach to deep learning written by the author of Keras. You'll need another book for theory such as deep learning(Ian, Yoshua, Aaron) if you want to study further (whether good or not, Keras abstracts away internal functions of the neural networks). This books covers

Best practical introduction to deep learning by author of Keras framework and Google researcher. First part of the book gives fundamental understanding and mathematical building blocks needed. Second part introduces different practical applications of deep learning networks: 1. Computer vision with co Leggi tutto

Deep learning is the newest fad in the computer science world. There are really just two reasons for its popularity: it has solved some difficult problems, and there is a lot of money in it. It has worked pretty well for some problems that looked really hard before, like distinguishing pictures of c Leggi tutto

This book is good, provided you do not believe the author's facile claims that it is the only book you need. This book explains almost nothing about how deep learning actually works, and is actually more like a user manual for Keras. Provided you actually want an instruction manual for Keras, it's a Leggi tutto

Always remember that when it comes to markets, past performance is not a good predictor of future returns—looking in the rear-view mirror is a bad way to drive. Machine learning, on the other hand, is applicable to datasets where the past is a good predictor of the future. Really good general intro t Leggi tutto

Great introductory book, that brings a pretty clear practical explanations of DeepLearning and its limitations. As a seasonal ML practitioner, you can still find a couple of interesting tricks. The books also touches more advanced topics like building Variational Autoencoders and Generative Adversaria Leggi tutto

The perfect practical introduction to DL, it doesn't claim to be a mathematical explanation of DL and it doesn't get bogged down in maths. Instead it gives lots of examples and explains in intuitive ways the implementation where necessary.

This book is really excellent! I would actually rate it as 4.5 stars. I have just made it 4 stars because I think some concepts in the last chapters could have been better explained. The first chapters introducing how deep learning works are very informative and didactic as well. I have even incorpo Leggi tutto

Quyển này chính là Dế mèn phiêu lưu kí trong deep learning =,= nhẹ nhàng, dễ hiểu, dễ tưởng tượng. Hiuhiu.