Sequence Models for Time Series and Natural Language Processing

Description

This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length.

• Predict future values of a time-series
• Classify free form text
• Address time-series and text problems with recurrent neural networks
• Choose between RNNs/LSTMs and simpler models
• Train and reuse word embeddings in text problems

You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together.

Prerequisites: Basic SQL, familiarity with Python and TensorFlow