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Function Approximation and Clustering

Perform regression, classification, and clustering using shallow neural networks

Generalize nonlinear relationships between example inputs and outputs, perform unsupervised learning with clustering and autoencoders.

Function Approximation and Nonlinear Regression
Create a neural network to generalize nonlinear relationships between example inputs and outputs
Pattern Recognition
Train a neural network to generalize from example inputs and their classes, train autoencoders
Clustering
Discover natural distributions, categories, and category relationships
Autoencoders
Perform unsupervised learning of features using autoencoder neural networks
Define Shallow Neural Network Architectures
Define shallow neural network architectures and algorithms