# Pretrained Networks

Use transfer learning to take advantage of the knowledge provided by a pretrained network to learn new patterns in new image data. Fine-tuning a pretrained image classification network with transfer learning is typically much faster and easier than training from scratch. Using pretrained deep networks enables you to quickly create models for new tasks without defining and training a new network, having millions of images, or having a powerful GPU. To explore the pretrained networks available, use Deep Network Designer.

## Apps

Deep Network Designer | Design, visualize, and train deep learning networks |

## Functions

## Blocks

## Topics

**Classify Webcam Images Using Deep Learning**This example shows how to classify images from a webcam in real time using the pretrained deep convolutional neural network GoogLeNet.

**Train Deep Learning Network to Classify New Images**This example shows how to use transfer learning to retrain a convolutional neural network to classify a new set of images.

**Transfer Learning Using Pretrained Network**This example shows how to fine-tune a pretrained GoogLeNet convolutional neural network to perform classification on a new collection of images.

**Pretrained Deep Neural Networks**Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.

**Deep Learning in MATLAB**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.**Deep Learning Tips and Tricks**Learn how to improve the accuracy of deep learning networks.

**Data Sets for Deep Learning**Discover data sets for various deep learning tasks.