09_Recurrent_Neural_Networks

Ch 9: Recurrent Neural Networks

1. Introduction

• We introduce Recurrent Neural Networks and how they are able to feed in a sequence and predict either a fixed target (categorical/numerical) or another sequence (sequence to sequence).

2. Implementing an RNN Model for Spam Prediction

• We create an RNN model to improve on our spam/ham SMS text predictions.

3. Implementing an LSTM Model for Text Generation

• We show how to implement a LSTM (Long Short Term Memory) RNN for Shakespeare language generation. (Word level vocabulary)

4. Stacking Multiple LSTM Layers

• We stack multiple LSTM layers to improve on our Shakespeare language generation. (Character level vocabulary)

5. Creating a Sequence to Sequence Translation Model (Seq2Seq)

• We show how to use TensorFlow's sequence-to-sequence models to train an English-German translation model.

6. Training a Siamese Similarity Measure

• Here, we implement a Siamese RNN to predict the similarity of addresses and use it for record matching. Using RNNs for record matching is very versatile, as we do not have a fixed set of target categories and can use the trained model to predict similarities across new addresses.