Build Deep Neural Networks
Create new deep networks for tasks such as image classification and regression by defining the network architecture from scratch. Build networks using MATLAB or interactively using Deep Network Designer.
For most tasks, you can use built-in layers. If there is not a built-in layer that you need for your task, then you can define your own custom layer. You can specify a custom loss function using a custom output layer and define custom layers with learnable and state parameters. After defining a custom layer, you can check that the layer is valid, GPU compatible, and outputs correctly defined gradients. For a list of supported layers, see List of Deep Learning Layers.
For models that cannot be specified as networks of layers, you can define the model as a function. To learn more, see Define Custom Training Loops, Loss Functions, and Networks.
App
Deep Network Designer | Progetta, visualizza e addestra le reti di Deep Learning |
Funzioni
Argomenti
Built-In Layers
- Creazione di una rete neurale semplice di Deep Learning per la classificazione
Questo esempio mostra come creare e addestrare una rete neurale convoluzionale semplice per la classificazione tramite Deep Learning. - Train Convolutional Neural Network for Regression
This example shows how to train a convolutional neural network to predict the angles of rotation of handwritten digits. - List of Deep Learning Layers
Discover all the deep learning layers in MATLAB. - Build Networks with Deep Network Designer
Interactively build and edit deep learning networks in Deep Network Designer. - Deep Learning in MATLAB
Scoprire le capacità del Deep Learning in MATLAB utilizzando le reti neurali convoluzionali per la classificazione e la regressione, incluse le reti preaddestrate e il transfer learning, nonché l’addestramento su GPU, CPU, cluster e cloud. - Deep Learning Tips and Tricks
Learn how to improve the accuracy of deep learning networks. - Data Sets for Deep Learning
Discover data sets for various deep learning tasks. - Multiple-Input and Multiple-Output Networks
Learn how to define and train deep learning networks with multiple inputs or multiple outputs. - Example Deep Learning Networks Architectures
This example shows how to define simple deep learning neural networks for classification and regression tasks.
Custom Layers
- Define Custom Deep Learning Layers
Learn how to define custom deep learning layers. - Check Custom Layer Validity
Learn how to check the validity of custom deep learning layers.