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Typical Workflow for Designing Neural Networks

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Each neural network application is unique, but developing the network typically follows these steps:

  1. Access and prepare your data
  2. Create the artificial neural network
  3. Configure the network’s inputs and outputs
  4. Tune the network parameters (the weights and biases) to optimize performance
  5. Train the network
  6. Validate the network’s results
  7. Integrate the network into a production system

Classification and Clustering of Shallow Networks

MATLAB and Deep Learning Toolbox provide command-line functions and apps for creating, training, and simulating shallow neural networks. The apps make it easy to develop neural networks for tasks such as classification, regression (including time-series regression), and clustering. After creating your networks in these tools, you can automatically generate MATLAB code to capture your work and automate tasks.

Preprocessing, Postprocessing, and Improving Your Network

Preprocessing the network inputs and targets improves the efficiency of shallow neural network training. Postprocessing enables detailed analysis of network performance. MATLAB and Simulink® provide tools to help you:

  • Reduce the dimensions of input vectors using principal component analysis
  • Perform regression analysis between the network response and the corresponding targets
  • Scale inputs and targets so they fall in the range [-1,1]
  • Normalize the mean and standard deviation of the training data set
  • Use automated data preprocessing and data division when creating your networks

Improving the network’s ability to generalize helps prevent overfitting, a common problem in artificial neural network design. Overfitting occurs when a network has memorized the training set but has not learned to generalize to new inputs. Overfitting produces a relatively small error on the training set but a much larger error when new data is presented to the network. Learn more about how you can use cross-validation to avoid overfitting.

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Two solutions to improve generalization include:

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  • Regularization modifies the network’s performance function (the measure of error that the training process minimizes). By including the sizes of the weights and biases, regularization produces a network that performs well with the training data and exhibits smoother behavior when presented with new data.
  • Early stopping uses two different data sets: the training set, to update the weights and biases, and the validation set, to stop training when the network begins to overfit the data