Get Started with Deep Learning Toolbox
Deep Learning Toolbox™ provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. The Experiment Manager app helps you manage multiple deep learning experiments, keep track of training parameters, analyze results, and compare code from different experiments. You can visualize layer activations and graphically monitor training progress.
You can import networks and layer graphs from TensorFlow™ 2, TensorFlow-Keras, and PyTorch®, the ONNX™ (Open Neural Network Exchange) model format, and Caffe. You can also export Deep Learning Toolbox networks and layer graphs to TensorFlow 2 and the ONNX model format. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
You can speed up training on a single- or multiple-GPU workstation (with Parallel Computing Toolbox™), or scale up to clusters and clouds, including NVIDIA® GPU Cloud and Amazon EC2® GPU instances (with MATLAB® Parallel Server™).
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. - 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. - Creazione di una rete di classificazione di sequenze semplice con Deep Network Designer
Questo esempio mostra come creare una rete long short-term memory di classificazione semplice 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.