Neural Network Toolbox

Data Fitting, Clustering, and Pattern Recognition

Like its counterpart in the biological nervous system, a neural network can learn and therefore can be trained to find solutions, recognize patterns, classify data, and forecast future events. The behavior of a neural network is defined by the way its individual computing elements are connected and by the strengths of those connections, or weights. The weights are automatically adjusted by training the network according to a specified learning rule until it performs the desired task correctly.

Neural Network Toolbox includes command-line functions and apps for creating, training, and simulating neural networks. The apps make it easy to develop neural networks for tasks such as data-fitting (including time-series data), pattern recognition, and clustering. After creating your networks in these tools, you can automatically generate MATLAB® code to capture your work and automate tasks.

House Pricing Estimation with Neural Net Fitting App 4:21
Estimate median house prices for neighborhoods based on various neighborhood attributes.

Iris Flower Clustering with Neural Net Clustering App 3:48
Cluster iris flowers based on petal and sepal size.

Wine Classification with Neural Net Pattern Recognition App 3:36
Identify the winery that particular wines came from based on chemical attributes of the wine.

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Machine Learning with MATLAB

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