This example shows how to classify new image data by fine-tuning an existing, pretrained convolutional neural network.
This example shows how to create and train a simple convolutional neural network for deep learning classification.
Discover deep learning capabilities in MATLAB® using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.
Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.
Train a neural network to fit a data set.
Use a neural network for classification.
Group data by similarity using the Neural Network Clustering App or command-line functions.
Make a time-series prediction using the Neural Network Time Series App and command-line functions.
Speed up training and simulation of large problems with Neural Network Toolbox™ and Parallel Computing Toolbox™.
See an illustration of the basic elements operating in a neural network.
Learn about the ways in which you can access the capabilities of the Neural Network Toolbox software
Review the range of applications for which neural networks have provided outstanding solutions.
Follow the standard steps for designing neural networks to solve problems in four application areas: function fitting, pattern recognition, clustering, and time-series analysis.
List of sample data sets to use while experimenting with the toolbox.
Refer to additional sources of information about neural networks.
Look up the meaning of neural network terminology.