Anwendungen

Object Detection Using YOLO v4 Deep Learning

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

Train Deep Learning Semantic Segmentation Network Using 3-D Simulation Data

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

Lidar Object Detection Using Complex-YOLO v4 Network

Detect objects in lidar point clouds using Complex-YOLO v4 deep learning network.

Train Biped Robot to Walk Using Reinforcement Learning Agents

Compare DDPG and TD3 agent for the control a biped walking robot modeled in Simscape™ Multibody™.

(Reinforcement Learning Toolbox)

Increase Image Resolution Using Deep Learning

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

(Image Processing Toolbox)

Chemical Process Fault Detection Using Deep Learning

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

Compare Deep Learning Networks for Credit Default Prediction

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

Sequence-to-Sequence Translation Using Attention

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

Classify ECG Signals Using Long Short-Term Memory Networks

Classify heartbeat electrocardiogram data using deep learning and signal processing.

Waveform Segmentation Using Deep Learning

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