Module 1: Recurrent Neural Networks and LSTM
Module Overview
This module introduces Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, which are specialized architectures designed for processing sequential data. While the feed-forward neural networks we've explored previously work well for many tasks, they struggle with sequential data where the order and context matter. RNNs address this limitation by incorporating feedback loops that allow information to persist across time steps.
You'll learn how RNNs process sequences by maintaining a "memory" of previous inputs, how the vanishing gradient problem limits traditional RNNs, and how LSTM networks overcome this limitation through specialized memory cells. By the end of this module, you'll be able to implement LSTM networks for text generation tasks using Keras, opening up possibilities for applications in natural language processing, time series analysis, and other sequence modeling domains.
Learning Objectives
- Describe how Neural Networks are used for modeling sequences
- Implement LSTM models for a text classification problem and a text generation problem
Guided Project
Open DS_431_RNN_and_LSTM_Lecture.ipynb in the GitHub repository to follow along with the guided project.
Module Assignment
Build a Shakespeare Sonnet Generator using LSTM networks to create Shakespearean-style text from seed phrases.