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.
Tutorials
- Get Started with Deep Network Designer
This example shows how to create a simple recurrent neural network for deep learning sequence classification using Deep Network Designer. - 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. - Get Started with Transfer Learning
This example shows how to use Deep Network Designer to prepare a network for transfer learning. - Get Started with Image Classification
This example shows how to create a simple convolutional neural network for deep learning classification using the Deep Network Designer app. - Try Deep Learning in 10 Lines of MATLAB Code
Learn how to use deep learning to identify objects on a live webcam with the SqueezeNet pretrained network. - Classify Image Using Pretrained Network
This example shows how to classify an image using the pretrained deep convolutional neural network GoogLeNet. - Create Simple Image Classification Network
This example shows how to create and train a simple convolutional neural network for deep learning classification.
App Workflows
Command-Line Workflows
Featured Examples
Classify Webcam Images Using Deep Learning
Classify images from a webcam in real time using the pretrained deep convolutional neural network GoogLeNet.
Retrain Neural Network to Classify New Images
Retrain a pretrained SqueezeNet neural network to perform classification on a new collection of images.
Time Series Forecasting Using Deep Learning
Forecast time series data using a long short-term memory (LSTM) network.
Interactive Learning
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.
Videos
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.
