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 graphics 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™).
- Get Started with Deep Network Designer
This example shows how to use Deep Network Designer to adapt a pretrained GoogLeNet network to classify a new collection of images.
- Try Deep Learning in 10 Lines of MATLAB Code
Learn how to use deep learning to identify objects on a live webcam with the AlexNet pretrained network.
- Classify Image Using Pretrained Network
This example shows how to classify an image using the pretrained deep convolutional neural network GoogLeNet.
- Get Started with Transfer Learning
This example shows how to use transfer learning to retrain SqueezeNet, a pretrained convolutional neural network, to classify a new set of images.
- Create Simple Image Classification Network
This example shows how to create and train a simple convolutional neural network for deep learning classification.
- Create Simple Image Classification Network Using Deep Network Designer
This example shows how to create and train a simple convolutional neural network for deep learning classification using Deep Network Designer.
- Create Simple Sequence Classification Network Using Deep Network Designer
This example shows how to create a simple long short-term memory (LSTM) classification network using Deep Network Designer.
- Shallow Networks for Pattern Recognition, Clustering and Time Series
Use apps and functions to design shallow neural networks for function fitting, pattern recognition, clustering, and time series analysis.
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.
Interactively Modify a Deep Learning Network for Transfer
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.