|Neural Net Pattern Recognition||Solve pattern recognition problem using two-layer feed-forward networks|
Interactive and Visualization Tools
|Train an autoencoder|
|Train a softmax layer for classification|
|Decode encoded data|
|Encode input data|
|Reconstruct the inputs using trained autoencoder|
|Stack encoders from several autoencoders together|
Pattern Recognition and Learning Vector Quantization
Training Options and Network Performance
|Train shallow neural network|
|Bayesian regularization backpropagation|
|Scaled conjugate gradient backpropagation|
|Mean squared normalized error performance function|
|Receiver operating characteristic|
|Plot classification confusion matrix|
|Plot error histogram|
|Plot network performance|
|Plot linear regression|
|Plot receiver operating characteristic|
|Plot training state values|
|Neural network performance|
|Generate MATLAB function for simulating shallow neural network|
Examples and How To
- 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.
Training Scalability and Efficiency
- Shallow Neural Networks with Parallel and GPU Computing
Use parallel and distributed computing to speed up neural network training and simulation and handle large data.
- Automatically Save Checkpoints During Neural Network Training
Save intermediate results to protect the value of long training runs.
- 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 the
- 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.
- 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.
- Train Stacked Autoencoders for Image Classification
This example shows how to train stacked autoencoders to classify images of digits.
- 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.