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Neural Network Toolbox

Create, train, and simulate shallow and deep learning neural networks

Neural Network Toolbox™ provides algorithms, pretrained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.

Deep learning networks include convolutional neural networks (ConvNets, CNNs), directed acyclic graph (DAG) network topologies, and autoencoders for image classification, regression, and feature learning. For time-series classification and prediction, the toolbox provides long short-term memory (LSTM) deep learning networks. You can visualize intermediate layers and activations, modify network architecture, and monitor training progress.

For small training sets, you can quickly apply deep learning by performing transfer learning with pretrained deep network models (GoogLeNet, AlexNet, VGG16, and VGG19) and models from the Caffe Model Zoo.

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 GPU instances (with MATLAB® Distributed Computing Server™).

Getting Started

Learn the basics of Neural Network Toolbox

Deep Learning Basics

Discover deep learning capabilities in MATLAB using convolutional neural networks (ConvNets) for classification and regression

Deep Learning Image Classification

Use pretrained deep networks to quickly learn new tasks, perform transfer learning and fine-tune a network, or perform feature extraction

Deep Learning Training from Scratch

Create new deep networks for image classification and regression, including series, DAG, and LSTM networks, import from Caffe, or define your own layers

Deep Learning Tuning and Visualization

Plot training progress, assess accuracy and make predictions, tune deep network training options, visualize features learned by a network

Function Approximation and Clustering

Perform regression, classification, and clustering using shallow networks; unsupervised learning with autoencoders

Time Series and Control Systems

Model nonlinear dynamic systems using shallow networks; make predictions using sequential data.

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