This advanced chapter showed you how to create RNNs. You learned about LSTMs and its bi-directional implementation, which is one of the most powerful approaches for sequences that can have distant temporal correlations. You also learned to create an LSTM-based sentiment analysis model for the classification of movie reviews. You designed an autoencoder to learn a latent space for MNIST using simple and bi-directional LSTMs and used it both as a vector-to-sequence model and as a sequence-to-sequence model.
At this point, you should feel confident explaining the motivation behind memory in RNNs founded in the need for more robust models. You should feel comfortable coding your own recurrent network using Keras/TensorFlow. Furthermore, you should feel confident implementing both supervised and unsupervised recurrent networks.
LSTMs are great in encoding highly correlated spatial information, such as images, or audio, or text, just like CNNs. However, both CNNs and LSTMs learn very...
