How to Build RNNs in Keras

This guide walks through Keras RNNs—SimpleRNN, GRU, and LSTM—covering core outputs vs states, returning sequences, encoder-decoder wiring, and cross-batch statefulness. It shows when to use Bidirectional wrappers, how to reuse states, and how default LSTM/GRU settings unlock CuDNN speed on GPU. You’ll also see how cell-level APIs enable custom architectures and nested inputs (e.g., audio+video), with concise examples for training and inference in TensorFlow/Keras.