In this chapter, we saw the essentials and some advanced models for deep networks. We were introduced to how neural networks work and the difference between shallow networks and deep learning. Then, we learnt ho to build a CNN deep network capable of classifying images of traffic signs. We also predicted the class of an image using a pre-trained network. Detecting the sentiment of a movie review using text found in reviews was also a part of the learning.

Deep learning models are indeed very powerful, though at the cost of having many degrees of freedom to handle and many coefficients to train, which requires having at hand large amounts of data.

In the next chapter, we'll see how Spark helps when the amount of data becomes too large ...

Get Python Data Science Essentials - Third Edition now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.