Advanced Deep Learning for Text Analysis
This course continues on tackling topics in deep learning for text analysis. In this course students will be getting to know how to use and implement Gated Recurrent Units (GRUs) and model and predict longer sequences of text by leveraging Seq2Seq models.
4 hours of instruction
This course continues on tackling topics in deep learning for text analysis. In this course students will be getting to know how to use and implement Gated Recurrent Units (GRUs) and model and predict longer sequences of text by leveraging Seq2Seq models.
OBJECTIVES
- Build advanced deep learning models to predict sequences of text
- Implement GRU using TensorFlow and predict on test data
- Explore use cases of Seq2Seq models
PREREQUISITES
Deep Learning for Text Analysis
SYLLABUS & TOPICS COVERED
- GRU
- Gated recurrent unit (GRU) in TensorFlow
- Implementing GRU in TensorFlow
- Implement the concept of stateful GRU
- Sequence-to-Sequence
- Seq2seq theory
- Implement seq2seq in TensorFlow
SOFTWARE REQUIREMENTS
You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.
About Instructor
