|Neural Net Fitting||Fit data by training a two-layer feed-forward network|
|Function fitting neural network|
|Feedforward neural network|
|Cascade-forward neural network|
|Train shallow neural network|
|Bayesian regularization backpropagation|
|Scaled conjugate gradient backpropagation|
|Mean squared normalized error performance function|
|Plot error histogram|
|Plot function fit|
|Plot network performance|
|Plot linear regression|
|Plot training state values|
|Generate MATLAB function for simulating neural network|
Train a shallow neural network to fit a data set.
Prepare a multilayer shallow neural network.
Train and use a multilayer shallow network for function approximation or pattern recognition.
Analyze network performance and adjust training process, network architecture, or data.
Simulate and deploy trained neural networks using MATLAB® tools.
Learn how to deploy training of a network.
Use parallel and distributed computing to speed up neural network training and simulation and handle large data.
Save intermediate results to protect the value of long training runs.
Make neural network training more efficient.
Preprocess inputs and targets for more efficient training.
Learn how to manually configure the network before
training using the
Use functions to divide the data into training, validation, and test sets.
Comparison of training algorithms on different problem types.
Learn methods to improve generalization and prevent overfitting.
Learn how to use error weighting when training neural networks.
Learn how to fit output elements with different ranges of values.
Learn the primary steps in a neural network design process.
Learn the different levels of using neural network functionality.
Workflow for designing a multilayer shallow feedforward neural network for function fitting and pattern recognition.
Learn the architecture of a multilayer shallow neural network.
Learn how the format of input data structures affects the simulation of networks.
Learn properties that define the basic features of a network.
Learn properties that define network details such as inputs, layers, outputs, targets, biases, and weights.