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Deep Learning Toolbox

Design, train, and analyze deep learning networks

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™).

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