As of R2019a, MATLAB supports the following deep neural network algorithms:
・Recurrent Neural Networks (RNN)
・Stacked Autoencoders (SAE)
・Convolutional Neural Networks (CNN)
・Regions with CNN (R-CNN)
・Fast R-CNN / Faster R-CNN
・Directed Acyclic Graph Networks (DAG)
・Long Short-Term Memory Networks (LSTM)
・Semantic Segmentation
Not only these algorithms, there are some related features such as transfer learning (using the pre-trained models based on MatConvNet/Caffe/Keras/Tensorflow) and deep dream (visualization of network features). Supported versions and required/recommended toolboxes are depending on which feature you want to use. Please check with the following table:
Table: Supported versions and required/recommended toolboxes of deep learning related algorithms
※1 Cannot import models with layers which are not supported by Neural Network Toolbox
※2 "Required" when GPU is used
※3 "Required" when the classification method is not Neural Network
Model importers and pre-trained models are provided via File Exchange by MathWorks Neural Network Toolbox Team.
The name of each toolbox is abbreviated in the table. The official name is as follows:
・PCT: Parallel Computing Toolbox
・NN: Neural Network Toolbox
・ST: Statistics and Machine Learning Toolbox
・IPT: Image Processing Toolbox
・CVST: Computer Vision System Toolbox
