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.
|Datastore for extracting random patches from images or pixel label images|
|Denoising image datastore|
|Datastore for image data|
|Transform batches to augment image data|
|Combine data from multiple datastores|
Datastores for Deep Learning (Deep Learning Toolbox)
Learn how to use datastores in deep learning applications.
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, transformations, and specialized datastores.
Preprocess Volumes for Deep Learning (Deep Learning Toolbox)
Read and preprocess volumetric image and label data for 3-D deep learning.
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.
Prepare Datastore for Image-to-Image Regression (Deep Learning Toolbox)
This example shows how to prepare a datastore for training an image-to-image regression network using the
combine functions of
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.
Pretrained Deep 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 Toolbox)
This example shows how to train a semantic segmentation network using deep learning.