Explains the definition, use, and some real world examples of deep learning. It also explains their recurrent nature. It also illustrates the difference between feed forward and recurrent neural networks.
Uses examples of sequential music and text generation to show how and when recurrent neural networks (specifically, long short-term memory networks) are useful.
Explains how generative adversial neural networks create new data from existing data. The clip also contains an analogy of how a cashier and a counterfeiter are like the generative and discriminitory aspects of a generative adversarial network.
Explains how convolutional neural networks can analyze and process greyscale and color images by examining their pixels and applying features and pooling.