09:03 - 10:41
1m 38s
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.
Video Transcript
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Explains how convolutional neural networks can analyze and process greyscale and color images by examining their pixels and applying features and pooling.
Defines and gives some brief examples of neural networks.
Gives an example of using a neural network to predict one's salary based on a number of different characteristics and by using an activation function.
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 activation functions help make sense of data whilst feature generation groups data into its own categories.