Deep Learning with Python

Amazing book from an amazing and experienced person in machine learning

If you are a beginner who wants to learn how to implement deep learning using Python this book may provide relevant information. It is very easy to understand and you can Google most of the things that helps you learn which makes this a good companion at the initial stage. however if you are more into advanced things would refer to more advanced online documentation such as fast.ai or google learning platforms.

The delivery came to my door step yesterday. The book is just the size of my hand(20 cm) and it's quite small. So, the written words of explanation seems to be smaller and further more the explicit mentioning of Terms in diagram is way more smaller. This book is amazing when it's come to providing Details of Concept and Best for Beginner who have just started it. Provided with best familiarity of what and why you should start with this book specifically.

The content is definitely great!! Keras is now the default wrapper not only for Tensorflow but also PyTorch. Hence, this book, whose focus is addressing deep learning from scratch using the Keras library, is a must for anyone who wishes to learn deep learning for advanced usage.

Very Good book, especially on the use of pretrained models like inception net, vgg, etc. The language used is very simple. Presentation of topics is well organised. It is very interesting to read with a computer by your side to practice the programming. Codes can be downloaded, of course. Expects some familiarity with tensorflow and keras. A working knowledge of python is also necessary. It is not a book for beginners.

This book has great content for learners who wants to grasp about Deep Learning. It is well structured and easy to understand. Even if you don't have strong background on Machine Learning, you can read this book to understand things. Highly recommended for both students and practitioners.

This book is good for the beginners and for those who want to get overview of different deep learning techniques. But don't think it will provide you any in-depth knowledge about the deep learning techniques discussed in this book. I can say it is brief summary of the deep learning with python language. And BTW, they have recently launched its 2nd edition.

The content of the book is very useful for beginners. This may not be the prescribed textbook I college syllabus but many professors suggest this book. The concepts are clearly explained and we can gain practical knowledge. The packaging and delivery by amazon is good but the book seems to be xerox copied book. It is not original print. But still it is comfortable to read.

+ Un des livres pilliers de l'IA (ou plutôt, Deep Learning et Machine Learning) avant même la vague de mode actuelle, à lire absolument

Ótimo livro. Fiquei muito satisfeito com a compra. Linguagem simples e de boa compreensão. Único ponto negativo é que ele é todo preto e branco. Não possui figuras coloridas.

Excellent book to get a quick start on deep learning! This is not a book to learn the theoretical aspects of deep-learning, rather it is a collection of hands-on examples to work through and learn by experience and the guidance provided by the author. That said, if you have seen neural networks from the 1990s along with the back propagation algorithm, and you can visualize the concepts of gradient descent and convolution, then this material is very easy to follow The examples are setup on the Keras framework using TensorFlow as the backend engine. I used an EC2 p2.xlarge instance as suggested by the author. The setup required a bit of help beyond what's provided in Appendix B. Once setup though you will need to run from a virtual environment: "source activate tensorflow_p36". . . . . . My final thought is that after having read Chapter 7, I want to do a second pass using callbacks and tensorboard for better insight.

I like this product

Libro increíble, escrito de forma muy clara y accesible. Se lee rápido y resulta mucho más sencillo que otros textos más académicos. Aun siendo introductorio, proporciona una base tremenda para entender los conceptos fundamentales del deep learning y aprender a aplicarlos en la práctica con Keras. Ideal para quienes quieran empezar en este campo con un enfoque práctico, sin perder rigor. Muy recomendable como primer contacto antes de pasar a lecturas más avanzadas.