《Deep Learning with Python》的作者是 Google 的研究员，开源深度学习框架 Keras 的作者 Francois Chollet，书中通过用 Python 解决现实中的问题为线索来展开，包括：图像分类、语音识别、问答系统、OCR 等等任务。Keras 是一个非常简单易用的二次框架，以 Theano 和 Tensorflow 为底层框架，使用风格与 Torch 类似。
Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.
In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.
链接：
https://www.manning.com/books/deep-learning-with-python
原文链接：
http://weibo.com/1402400261/Et0MF2pxN?ref=collection&type=comment#_rnd1485695129729
