Inside Deep Learning

4 Recurrent neural networks

This chapter covers

• Weight sharing and processing sequence data
• Representing sequence problems in deep learning
• Combining RNNs and fully connected layers for predictions
• Padding and packing to use sequences of different lengths

The previous chapter showed us how to develop neural networks for a particular type of spatial structure: spatial locality. Specifically, we learned how the convolution operator endowed our neural network with a prior that items near each other are related but items far from each other have no relationship. This allowed us to build neural networks that learned faster and provide more accurate solutions for classifying images.

Now we want to develop models that can handle a new type of structure: ...