Learn the basics of recurrent neural networks to get up and running with RNN quickly.
Overview
Syllabus
Introduction
- Getting started with RNNs
- Scope and prerequisites for the course
- Setting up exercise files
- A review of deep learning
- Why sequence models?
- A recurrent neural network
- Types of RNNs
- Applications of RNNs
- Training RNN models
- Forward propagation with RNN
- Computing RNN loss
- Backward propagation with RNN
- Predictions with RNN
- 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
- The vanishing gradient problem
- The gated recurrent unit
- Long short-term memory
- Bidirectional RNNs
- 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
- Text based models: Challenges
- Intro to word embeddings
- Pretrained word embeddings
- Text preprocessing for RNN
- Creating an embedding matrix
- 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
- Next steps
Taught by
Kumaran Ponnambalam
