In the deep learning journey so far on this website, I've introduced dense neural networks and convolutional neural networks (CNNs) which explain how to perform classification tasks on static images. We've seen good results, especially with CNN's. However, what happens if we want to analyze dynamic data? There are ways to do some of this using CNN's, but the most popular method of performing classification and other analysis on sequences of data is recurrent neural networks. This tutorial will be a very comprehensive introduction to recurrent neural networks and a subset of such networks – long-short term memory networks (or LSTM networks). I'll also show you how to implement such networks in TensorFlow – including the data preparation step. It's going to be a long one, so settle in and enjoy these pivotal networks in deep learning – at the end of this post, you'll have a very solid understanding of recurrent neural networks and LSTMs.
Dec-27-2017, 02:27:12 GMT