Deep Learning for Text Analysis

4 hours of instruction

This course continues on tackling topics in deep learning that address specific problem types. In this course students will be getting to know RNNs and LSTMs – types of neural networks that are often used for solving problems in text analysis.

OBJECTIVES

1. Build basic predictive systems for text using deep learning
2. Build natural language understanding and generation models

PREREQUISITES

Neural Networks & Deep Learning

SYLLABUS & TOPICS COVERED

1. Intro To RNN
   • Introduction to RNN
   • Discuss best practices for RNN models
2. LSTM
   • LSTMs for text data and their use cases
   • LSTM implementation in TensorFlow

SOFTWARE REQUIREMENTS

You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.