A fantastic book I read these days, not just because the content quietly, but also the vision author would like to take us to, the last sentence is remarkable:
1. Staying up to date in a fast-moving field
2. Practice on real-world problems using Kaggle
3. Read about the latest developments on arXiv
4. Explore the Keras ecosystem
and this:
The term neural network is a reference to neurobiology, but although some of the central concepts in deep learning were developed in part by drawing inspiration from our understanding of the brain, deep-learning models are not models of the brain. There’s no evidence that the brain implements anything like the learning mechanisms used in modern deep-learning models. You may come across pop-science articles proclaiming that deep learning works like the brain or was modeled after the brain, but that isn’t the case. It would be confusing and counterproductive for newcomers to the field to think of deep learning as being in any way related to neurobiology; you don’t need that shroud of “just like our minds” mystique and mystery, and you may as well forget anything you may have read about hypothetical links between deep learning and biology. For our purposes, deep learning is a mathematical framework for learning representations from data.
I read the English version of Deep Learning with Python, as in a fast-pace field, the Chinese version issued later by 8 month seems like too slow, and maybe another several months to disseminate help everybody know, the language problem is a severe problem for Interdisciplinary learning
From this book, I learned more than a dozen concepts from scratch, such as ‘Scalars’, ‘Vectors’, ‘Matrices’, ‘tensors’, ‘Word embedding’ and many more, which help me create concrete concepts for the following learning process
If you encounter any programming problem in this book’s examples, you can find all the errata here
Author Francois Chollet’s talk at RAAIS 2018:
