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Deep Learning Applications

Extend deep learning workflows with computer vision, image processing, automated driving, and signals

Extend deep learning workflows in MATLAB® for applications including computer vision, image processing, automated driving, and signals. For example, you can train object detectors or perform semantic segmentation to classify every pixel of an image.

Topics

Computer Vision

Semantic Segmentation Using Deep Learning

This example shows how to train a semantic segmentation network using deep learning.

Semantic Segmentation of Multispectral Images Using Deep Learning

This example shows how to train a U-Net convolutional neural network to perform semantic segmentation of a multispectral image with seven channels: three color channels, three near-infrared channels, and a mask.

Semantic Segmentation Using Dilated Convolutions

This example shows how to train a semantic segmentation network using dilated convolutions.

Define Custom Pixel Classification Layer with Dice Loss

This example shows how to define and create a custom pixel classification layer that uses Dice loss.

Object Detection Using Deep Learning

This example shows how to train an object detector using deep learning and R-CNN (Regions with Convolutional Neural Networks).

Object Detection Using Faster R-CNN Deep Learning

This example shows how to train an object detector using a deep learning technique named Faster R-CNN (Regions with Convolutional Neural Networks).

Image Processing

Remove Noise from Color Image Using Pretrained Neural Network

This example shows how to remove Gaussian noise from an RGB image.

Single Image Super-Resolution Using Deep Learning

This example shows how to train a Very-Deep Super-Resolution (VDSR) neural network, then use a VDSR network to estimate a high-resolution image from a single low-resolution image.

JPEG Image Deblocking Using Deep Learning

This example shows how to train a denoising convolutional neural network (DnCNN), then use the network to reduce JPEG compression artifacts in an image.

Image Processing Operator Approximation Using Deep Learning

This example shows how to train a multiscale context aggregation network (CAN) that is used to approximate an image filtering operation.

Automated Driving

Train a Deep Learning Vehicle Detector

This example shows how to train a vision-based vehicle detector using deep learning.

Create Occupancy Grid Using Monocular Camera and Semantic Segmentation

This example shows how to estimate free space and create an occupancy grid using semantic segmentation and deep learning.

Signals

Classify ECG Signals Using Long Short-Term Memory Networks

This example shows how to classify heartbeat electrocardiogram (ECG) data from the PhysioNet 2017 Challenge using deep learning and signal processing.

Classify Time Series Using Wavelet Analysis and Deep Learning

This example shows how to classify human electrocardiogram (ECG) signals using the continuous wavelet transform (CWT) and a deep convolutional neural network (CNN).

Speech Command Recognition Using Deep Learning

This example shows how to train a simple deep learning model that detects the presence of speech commands in audio.

Denoise Speech Using Deep Learning Networks

This example shows how to denoise speech signals using deep learning networks.

Classify Gender Using Long Short-Term Memory Networks

This example shows how to classify the gender of a speaker using deep learning.

Featured Examples