Deep Learning - Recurrent Neural Networks with TensorFlow - RNN for Image Classification (Theory)

The video explores the unconventional use of Recurrent Neural Networks (RNNs) for image classification, typically a domain for Convolutional Neural Networks (CNNs). It emphasizes using imagination to treat images as multidimensional time series, allowing RNNs to process them. The tutorial covers the conceptual shift needed to view data differently, using examples like survey data and time series. It explains the structure of multidimensional time series and how RNNs can scan images row by row. The implementation section guides viewers through setting up an LSTM network for image classification using MNIST data, encouraging experimentation with different techniques like global max pooling.

What are the steps outlined for preparing the code to implement RNNs for image classification?

What does the speaker encourage the audience to do as an exercise after the lecture?