As for love, head-to-toe positions provide exciting new possibilities: encoder and decoder networks use the same stack of layers but in their opposite directions.
Although it does not provide new modules to deep learning, such a technique of encoding-decoding is quite important because it enables the training of the networks 'end-to-end', that is, directly feeding the inputs and corresponding outputs, without specifying any rules or patterns to the networks and without decomposing encoding training and decoding training into two separate steps.
While image classification was a one-to-one task, and sentiment analysis a many-to-one task, encoding-decoding techniques illustrate many-to-many tasks, such as translation or image segmentation.
In the next chapter, we'll introduce an attention mechanism that provides the ability for encoder-decoder architecture to focus on some parts of the input in order to produce a more accurate output.
