Section Recap

Introduction

This short lesson summarizes key takeaways from section 44.

Objectives

You will be able to:

• Understand and explain what was covered in this section
• Understand and explain why this section will help you become a data scientist

Key Takeaways

The key takeaways from this section include:

• A Sequence Model is a general term for a special class of Deep Neural Networks that work with a time series of data as an input
• In the deep learning context, sequence models are also referred to as "Recurrent Neural Networks" (RNNs)
• Sequence Models are often used for text classification, sequence generation, etc.
• A basic Recurrent Neural Network is a neural network that passes it's output from a given example back into itself as input for the next example (which feels a little bit like the time series models we've seen)
• RNN architectures use a special type of backpropagation referred to as "backpropagation through time"
• To special types of RNNs are referred to as "Long Short Term Memory" (LSTM) and "Gated Recurrent Unit" (GRU) models.