Course Overview

Learn the basics of recurrent neural networks to get up and running with RNN quickly.

Course Circullum

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

Kumaran Ponnambalam

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