Pattern Recognition - MATLAB & Simulink (original) (raw)

Train a neural network to generalize from example inputs and their classes, train autoencoders

Apps

Classes

Functions

Examples and How To

Basic Design

• Pattern Recognition with a Shallow Neural Network
  Use a shallow neural network for pattern recognition.
• Deploy Shallow Neural Network Functions
  Simulate and deploy trained shallow neural networks using MATLAB® tools.
• Deploy Training of Shallow Neural Networks
  Learn how to deploy training of shallow neural networks.

Optimal Solutions

• Choose Neural Network Input-Output Processing Functions
  Preprocess inputs and targets for more efficient training.
• Configure Shallow Neural Network Inputs and Outputs
  Learn how to manually configure the network before training using theconfigure function.
• Divide Data for Optimal Neural Network Training
  Use functions to divide the data into training, validation, and test sets.
• Choose a Multilayer Neural Network Training Function
  Comparison of training algorithms on different problem types.
• Improve Shallow Neural Network Generalization and Avoid Overfitting
  Learn methods to improve generalization and prevent overfitting.
• Train Neural Networks with Error Weights
  Learn how to use error weighting when training neural networks.
• Normalize Errors of Multiple Outputs
  Learn how to fit output elements with different ranges of values.

Classification

• Crab Classification
  This example illustrates using a neural network as a classifier to identify the sex of crabs from physical dimensions of the crab.
• Wine Classification
  This example illustrates how a pattern recognition neural network can classify wines by winery based on its chemical characteristics.
• Cancer Detection
  This example shows how to train a neural network to detect cancer using mass spectrometry data on protein profiles.
• Character Recognition
  This example illustrates how to train a neural network to perform simple character recognition.

Autoencoders

• Train Stacked Autoencoders for Image Classification
  This example shows how to train stacked autoencoders to classify images of digits.

Concepts

• Workflow for Neural Network Design
  Learn the primary steps in a neural network design process.
• Four Levels of Neural Network Design
  Learn the different levels of using neural network functionality.
• Multilayer Shallow Neural Networks and Backpropagation Training
  Workflow for designing a multilayer shallow feedforward neural network for function fitting and pattern recognition.
• Multilayer Shallow Neural Network Architecture
  Learn the architecture of a multilayer shallow neural network.
• Understanding Shallow Network Data Structures
  Learn how the format of input data structures affects the simulation of networks.
• Sample Data Sets for Shallow Neural Networks
  List of sample data sets to use when experimenting with shallow neural networks.
• Neural Network Object Properties
  Learn properties that define the basic features of a network.
• Neural Network Subobject Properties
  Learn properties that define network details such as inputs, layers, outputs, targets, biases, and weights.