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Deep Learning for Regression

Train convolutional neural networks (also known as ConvNets, CNNs) for regression tasks

Pretrained Models

Transfer learning with pretrained CNN models AlexNet, VGG-16, and VGG-19, and import models from Caffe (including Caffe Model Zoo)

Deep Learning with Cloud Instances

Train convolutional neural networks using multiple GPUs in MATLAB and MATLAB Distributed Computing Server for Amazon EC2

Deep Learning with Multiple GPUs

Train convolutional neural networks on multiple GPUs on PCs (using Parallel Computing Toolbox) and clusters (using MATLAB Distributed Computing Server)

Deep Learning with CPUs

Train convolutional neural networks on CPUs as well as GPUs

Deep Learning Visualization

Visualize the features ConvNet has learned using deep dream and activations

Deep Learning with Arbitrary Sized Images

Run trained CNNs on images that are different sizes than those used for training

Deep Learning Using ImageDatastore

Train CNNs faster when using ImageDatastore object

Deploy Training of Models

Deploy training of a neural network model via MATLAB Compiler or MATLAB Compiler SDK

Latest Releases

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Version 10.0, part of Release 2017a, includes the following enhancements:

  • Deep Learning for Regression: Train convolutional neural networks (also known as ConvNets, CNNs) for regression tasks
  • Pretrained Models: Transfer learning with pretrained CNN models AlexNet, VGG-16, and VGG-19, and import models from Caffe (including Caffe Model Zoo)
  • Deep Learning with Cloud Instances: Train convolutional neural networks using multiple GPUs in MATLAB and MATLAB Distributed Computing Server for Amazon EC2
  • Deep Learning with Multiple GPUs: Train convolutional neural networks on multiple GPUs on PCs (using Parallel Computing Toolbox) and clusters (using MATLAB Distributed Computing Server)
  • Deep Learning with CPUs: Train convolutional neural networks on CPUs as well as GPUs
  • Deep Learning Visualization: Visualize the features ConvNet has learned using deep dream and activations

See the Release Notes for details.

Version 9.1, part of Release 2016b, includes the following enhancements:

  • Deep Learning with CPUs: Run trained CNNs to extract features, make predictions, and classify data on CPUs as well as GPUs
  • Deep Learning with Arbitrary Sized Images: Run trained CNNs on images that are different sizes than those used for training
  • Performance: Train CNNs faster when using ImageDatastore object
  • Deploy Training of Models: Deploy training of a neural network model via MATLAB Compiler or MATLAB Compiler SDK

See the Release Notes for details.

Version 9.0, part of Release 2016a, includes the following enhancements:

  • Deep Learning: Train deep convolutional neural networks with built-in GPU acceleration for image classification tasks (using Parallel Computing Toolbox)

See the Release Notes for details.

Version 8.4, part of Release 2015b, includes the following enhancements:

  • Autoencoder neural networks for unsupervised learning of features using the trainAutoencoder function
  • Deep learning using the stack function for creating deep networks from autoencoders​
  • Improved speed and memory efficiency for training with Levenberg-Marquardt (trainlm) and Bayesian Regularization (trainbr) algorithms​

See the Release Notes for details.

Version 8.3, part of Release 2015a, includes the following enhancements:

  • Progress update display for parallel training

See the Release Notes for details.