Anwendungen

Train a Faster R-CNN (regions with convolutional neural networks) object detector.

Use 3-D simulation data to train a semantic segmentation network and fine-tune it to real-world data using generative adversarial networks (GANs).

Detect objects in point clouds using you only look once version 4 (YOLO v4) deep learning network. In this example, you will

Train a reinforcement learning agent to control a biped walking robot modeled in Simscape™ Multibody™.

Create a high-resolution image from a low-resolution image using a very-deep super-resolution (VDSR) neural network.

Use simulation data to train a neural network than can detect faults in a chemical process.

Create, train, and compare three deep learning networks for predicting credit default probability.

Convert decimal strings to Roman numerals using a recurrent sequence-to-sequence encoder-decoder model with attention.

Classify heartbeat electrocardiogram data using deep learning and signal processing.

Segment human electrocardiogram signals using time-frequency analysis and deep learning.