Get Started with Deep Learning Toolbox
Deep Learning Toolbox™ provides functions, apps, and Simulink® blocks for designing, implementing, and simulating deep neural networks. The toolbox provides a framework to create and use many types of networks, such as convolutional neural networks (CNNs) and transformers. You can visualize and interpret network predictions, verify network properties, and compress networks with quantization, projection, or pruning.
With the Deep Network Designer app, you can design, edit, and analyze networks interactively, import pretrained models, and export networks to Simulink. The toolbox lets you interoperate with other deep learning frameworks. You can import PyTorch®, TensorFlow™, and ONNX™ models for inference, transfer learning, simulation, and deployment. You can also export models to TensorFlow and ONNX.
You can automatically generate C/C++, CUDA® and HDL code for trained networks.
Tutorial
- Come iniziare a utilizzare Deep Network Designer
Questo esempio mostra come utilizzare Deep Network Designer per adattare una rete GoogLeNet preaddestrata alla classificazione di una nuova raccolta di immagini. - Get Started with Time Series Forecasting
This example shows how to create a simple long short-term memory (LSTM) network to forecast time series data using the Deep Network Designer app. - Come iniziare a utilizzare il transfer learning
Questo esempio mostra come utilizzare il transfer learning per riaddestrare SqueezeNet, una rete neurale convoluzionale preaddestrata, per classificare un nuovo set di immagini. - Creazione di una rete di classificazione di immagini semplice con Deep Network Designer
Questo esempio mostra come creare e addestrare una rete neurale convoluzionale semplice per la classificazione tramite Deep Learning con l’uso di Deep Network Designer. - Prova il Deep Learning in 10 righe di codice MATLAB
Scopri come utilizzare il Deep Learning per identificare gli oggetti su una webcam live con la rete preaddestrata SqueezeNet. - Classificazione di immagini con una rete preaddestrata
Questo esempio mostra come classificare un’immagine con la rete neurale convoluzionale profonda preaddestrata GoogLeNet. - Creazione di una rete di classificazione di immagini semplice
Questo esempio mostra come creare e addestrare una rete neurale convoluzionale semplice per la classificazione tramite Deep Learning.
App Workflows
Command-Line Workflows
Esempi in primo piano
Apprendimento interattivo
Deep Learning Onramp
This free, two-hour deep learning tutorial provides an interactive introduction to practical deep learning methods. You will learn to use deep learning techniques in MATLAB® for image recognition.
Video
Interactively Modify a Deep Learning Network for Transfer Learning
Deep Network Designer is a point-and-click tool for creating or modifying deep neural networks. This video shows how to use the app in a transfer learning workflow. It demonstrates the ease with which you can use the tool to modify the last few layers in the imported network as opposed to modifying the layers in the command line. You can check the modified architecture for errors in connections and property assignments using a network analyzer.
Deep Learning with MATLAB: Deep Learning in 11 Lines of MATLAB Code
See how to use MATLAB, a simple webcam, and a deep neural network to identify objects in your surroundings.
Deep Learning with MATLAB: Transfer Learning in 10 Lines of MATLAB Code
Learn how to use transfer learning in MATLAB to re-train deep learning networks created by experts for your own data or task.