Applying deep learning to biological sequence data: (basic sciences session 1 of 2)
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Recurrent neural networks (RNNs) are a class of neural networks that can process sequential data, such as text. RNNs have been successfully applied to many natural language processing tasks, including text generation, classification, and translation. In this two-part vLE, we will first introduce you to RNNs and their specific application to biological sequence data. In the second part, we will demonstrate how to build an RNN using PyTorch that can predict protein function based on amino acid sequence data
Part 1: What is a recurrent neural network?
Part 2: Implementing an RNN to predict protein function from sequence
Note: If you would like a basic introduction to neural networks, please go through the Week 1 material of the course "Introduction to Machine Learning" on Coursera. https://www.coursera.org/learn/machine-learning-duke
On the day before the session, all registrants will receive an e-mail with a link and meeting information. Please register at least 15 minutes prior to the start of the session to allow time for the system to generate a personalized e-mail with the access link.
|Date||Wednesday, April 7th, 2021|
|Time||4:30pm - 5:30pm|