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Deep Learning Toolbox
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. 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® Parallel Server™).
Getting Started
Learn the basics of Deep Learning Toolbox
Deep Learning with Images
Train convolutional neural networks from scratch or use pretrained networks to quickly learn new tasks
Deep Learning with Time Series, Sequences, and Text
Create and train networks for time series classification, regression, and forecasting tasks
Deep Learning Tuning and Visualization
Plot training progress, assess accuracy, make predictions, tune training options, and visualize features learned by a network
Deep Learning in Parallel and in the Cloud
Scale up deep learning with multiple GPUs locally or in the cloud and train multiple networks interactively or in batch jobs
Deep Learning Applications
Extend deep learning workflows with computer vision, image processing, automated driving, signals, and audio
Deep Learning Import, Export, and Customization
Import and export networks, define custom deep learning layers, and customize datastores
Deep Learning Code Generation
Generate MATLAB code or CUDA® and C++ code and deploy deep learning networks
Function Approximation and Clustering
Perform regression, classification, and clustering using shallow neural networks
Time Series and Control Systems
Model nonlinear dynamic systems using shallow networks; make predictions using sequential data.
