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Deep Learning Toolbox

Design, train, analyze, and simulate deep learning networks

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.

Get Started

Learn the basics of Deep Learning Toolbox


Extend deep learning workflows with computer vision, image processing, automated driving, signals, audio, text analytics, and computational finance

Deep Learning Fundamentals

Import, build, train, tune, visualize, verify, and export deep neural networks

Image Data Workflows

Use pretrained networks or create and train networks from scratch for image classification and regression

Sequence and Numeric Feature Data Workflows

Create and train classification, regression, and forecasting neural networks for sequence and tabular data

Parallel and Cloud

Scale up deep learning with multiple GPUs locally or in the cloud and train multiple networks interactively or in batch jobs

Automatic Differentiation

Customize deep learning layers, networks, training loops, and loss functions

Deep Learning with Simulink

Extend deep learning workflows using Simulink

Code Generation

Generate C/C++, CUDA, or HDL code and deploy deep learning networks

Function Approximation, Clustering, and Control

Perform regression, classification, clustering, and model nonlinear dynamic systems using shallow neural networks