6
Recurrent Neural Networks in Genomics
Deep learning (DL) models are so versatile that they can adapt to any input data distribution and, at the same time, generalize very well to previously unseen data. A variety of deep neural network (DNN) architectures have been designed to suit a particular task. For example, we saw how feedforward neural networks (FNNs) are good at making predictions from structured data, such as tabular data, in Chapter 4, Deep Learning for Genomics. We also saw how convolutional neural networks (CNNs) are good at making predictions from unstructured data such as images, audio, text, and DNA sequence data; we saw this in Chapter 5, Introducing Convolutional Neural Networks for Genomics. But what about sequential data? ...
Get Deep Learning for Genomics now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
