Version 11.1, part of Release 2018a, includes the following enhancements:

• Long Short-Term Memory (LSTM) Networks: Solve regression problems with LSTM networks and learn from full sequence context using bidirectional LSTM layers
• Deep Learning Optimization: Improve network training using Adam, RMSProp, and gradient clipping
• Deep Learning Data Preprocessing: Read data and define preprocessing operations efficiently for training and prediction
• Deep Learning Layer Validation: Check layers for validity, GPU compatibility, and correctly defined gradients
• Directed Acyclic Graph (DAG) Networks: Accelerate DAG network training using multiple GPUs and compute intermediate layer activations

See the Release Notes for details.

Version 11.0, part of Release 2017b, includes the following enhancements:

• Directed Acyclic Graph (DAG) Networks: Create deep learning networks with more complex architecture to improve accuracy and use many popular pretrained models
• Long Short-Term Memory (LSTM) Networks: Create deep learning networks with the LSTM recurrent neural network topology for time-series classification and prediction
• Deep Learning Validation: Automatically validate network and stop training when validation metrics stop improving
• Deep Learning Layer Definition: Define new layers with learnable parameters, and specify loss functions for classification and regression output layers
• Deep Learning Training Plots: Monitor training progress with plots of accuracy, loss, validation metrics, and more
• Deep Learning Image Preprocessing: Efficiently resize and augment image data for training
• Bayesian Optimization of Deep Learning: Find optimal settings for training deep networks (Requires Statistics and Machine Learning Toolbox)

See the Release Notes for details.

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.