Erkennung von Mustern
Trainieren eines neuronalen Netzes zur Verallgemeinerung von Beispieleingaben und deren Klassen, Trainieren von Autoencodern
Apps
| Neural Net Pattern Recognition | Lösung eines Mustererkennungsproblems mit zweischichtigen Feed-Forward-Netzen |
Klassen
Autoencoder | Autoencoder class |
Funktionen
Beispiele und Gewusst wie
Grundlegendes Design
- Mustererkennung mit einem flachen neuronalen Netz
Verwendung eines flachen neuronalen Netzes zur Mustererkennung. - 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.
Skalierbarkeit und Effizienz des Trainings
- 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.
Optimale Lösungen
- 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 theconfigurefunction. - 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.
Klassifizierung
- 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.
Autoencoder
- Train Stacked Autoencoders for Image Classification
This example shows how to train stacked autoencoders to classify images of digits.
Konzepte
- 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.
- Mehrschichtige flache neuronale Netze und Backpropagationstraining
Workflow zur Entwicklung eines mehrschichtigen, flachen neuronalen Feed-Forward-Netzes für die Funktionsanpassung und Mustererkennung.
- 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.
- Beispieldatensätze für flache neuronale Netze
Liste der Beispieldatensätze, die zum Experimentieren mit flachen neuronalen Netzen verwendet werden können.
- 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.
