Introduction

• Getting started with RNNs
• Scope and prerequisites for the course
• Setting up exercise files

1. Introduction to RNNs

• A review of deep learning
• Why sequence models?
• A recurrent neural network
• Types of RNNs
• Applications of RNNs

2. RNN Concepts

• Training RNN models
• Forward propagation with RNN
• Computing RNN loss
• Backward propagation with RNN
• Predictions with RNN

3. An RNN Example

• A simple RNN example: Predicting stock prices
• Data preprocessing for RNN
• Preparing time series data with lookback
• Creating an RNN model
• Testing and predictions with RNN

4. RNN Architectures

• The vanishing gradient problem
• The gated recurrent unit
• Long short-term memory
• Bidirectional RNNs

5. An LSTM Example

• Forecasting service loads with LSTM
• Time series patterns
• Preparing time series data for LSTM
• Creating an LSTM model
• Testing the LSTM model
• Forecasting service loads: Predictions

6. Word Embeddings

• Text based models: Challenges
• Intro to word embeddings
• Pretrained word embeddings
• Text preprocessing for RNN
• Creating an embedding matrix

7. Spam Detection with Word Embeddings

• Spam detection example for embeddings
• Preparing spam data for training
• Building the embedding matrix
• Creating a spam classification model
• Predicting spam with LSTM and word embeddings

Conclusion

• Next steps