Summary

In this chapter we discussed advanced sequential models, which are advanced techniques designed to process input sequences, especially when the length of output sequences may differ from that of the input. Autoencoders, a type of neural network architecture, are particularly adept at compressing data. They work by encoding input data into a smaller representation and then decoding it back to resemble the original input. This process can be useful in tasks like image denoising, where noise from an image is filtered out to produce a clearer version.

Another influential model is the Seq2Seq model. It’s designed to handle tasks where input and output sequences have varying lengths, making it ideal for applications like machine translation. However, traditional Seq2Seq models face the information bottleneck challenge, wherein the entire context of an input sequence needs to be captured in a single, fixed-size representation. Addressing this, the attention mechanism was...