In the module on CNNs, you learnt about 2-dimensional convolutions. In this session, you’ll learn about the 1-dimensional CNN architecture.
By why do we need 1D CNNs ? Just as you used 2-dimensional CNNs to extract features from an image, a1D CNN can be used to extract features from a text. You’ll also learn how to feed the features extracted by the 1D CNN to an RNN model.
In this session:
- Extracting textual features using 1-dimensional CNN
- Feeding the textual features to an RNN
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:
Chief Data Scientist, Data Semantics
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