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Description
Not all machine learning problems are created equal. Some tasks require working with no labels either to discover similarities between data points or to spot anomalies. Clustering is an important task of grouping similar data together. Dimensionality reduction helps with understanding the input space with a lot of input features. An autoencoder is a type of neural network that aims to learn the encoding of unlabeled data. They could be used for noise removal and dimensionality reduction. They can be useful to generate new data from arbitrary encoding. However, we need to learn the latent code distribution for that. This is where variational autoencoders come in handy.
In this tutorial, you will write your own clustering algorithm, use a dimensionality reduction algorithm to visualize and understand the data and train a variational autoencoder to generate new data.
