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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 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™).