Deep learning uses neural networks to learn useful representations of features directly from data. For example, you can use a pretrained neural network to identify and remove Gaussian noise from images.
Use a pretrained neural network to remove Gaussian noise from a grayscale image, or train your own network using predefined layers.
This example shows how to remove Gaussian noise from an RGB image by using a pretrained denoising neural network on each color channel independently.
Deep Learning in MATLAB (Deep Learning Toolbox)
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.
Preprocess Images for Deep Learning (Deep Learning Toolbox)
Learn how to resize images for training, prediction and classification, and how to preprocess images using data augmentation and mini-batch datastores.
Pretrained Convolutional Neural Networks (Deep Learning Toolbox)
Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.
Semantic Segmentation Using Deep Learning (Computer Vision System Toolbox)
This example shows how to train a semantic segmentation network using deep learning.
Object Detection Using Deep Learning (Computer Vision System Toolbox)
This example shows how to train an object detector using deep learning and R-CNN (Regions with Convolutional Neural Networks).