In the previous sessions, you have studied the basic architecture of the RNN, its various architectural variants such as bidirectional RNNs and some popular gated variants such as the LSTM and the GRU. In this session, you’ll implement all these concepts in Python using the Keras library.
In this session:
- POS tagging with RNNs
- Text generation with RNNs
Before moving ahead, please run the following code in a Jupyter notebook cell (on Nimblebox server) to install all the required libraries needed to proceed for the session.
Also, make sure that you download the required data and packaged on Paperspace machines from NLTK using the following code.
Prerequisites
There are no prerequisites for this session other than knowledge of the previous modules of Neural Networks and the courses Statistics and ML.
Guidelines for In-Module Questions
The in-video and in-content questions for this module are not graded. Note that graded questions are given on a separate page labelled ‘Graded Questions’ at the end of this session. The graded questions in this session will adhere to the following guidelines:
People you will hear from in this session:
Subject Matter Expert:
G. Srinivasaraghavan
Professor, IIIT-Bangalore
The International Institute of Information Technology, Bangalore, commonly known as IIIT Bangalore, is a premier national graduate school in India. Founded in 1999, it offers Integrated M.Tech., M.Tech., M.S. (Research) and PhD programs in the field of Information Technology.
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