Deep Learning Toolbox
Create, analyze, and train deep learning networks
Deep Learning Toolbox (formerly Neural Network 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. Apps and plots help you visualize activations, edit network architectures, and monitor training progress.
For small training sets, you can perform transfer learning with pretrained deep network models (including SqueezeNet, Inception-v3, ResNet-101, GoogLeNet, and VGG-19) and models imported from TensorFlow-Keras and Caffe.
To speed up training on large datasets, you can distribute computations and data across multicore processors and GPUs on the desktop (with Parallel Computing Toolbox), or scale up to clusters and clouds, including Amazon EC2 P2, P3, and G3 GPU instances (with MATLAB Distributed Computing Server)