Applications
Use Deep Learning Toolbox™ to incorporate deep learning in computer vision, image processing, automated driving, signal processing, audio, text analytics, and computational finance applications.
Categories
- Image Processing and Computer Vision
Extend deep learning workflows with image processing, computer vision, medical imaging, lidar, and automated driving applications
- Signal Processing, Audio, and Wireless
Extend deep learning workflows with signal processing, audio processing, wireless communications, and radar processing applications
- Physics-Informed Machine Learning
Extend deep learning workflows in areas of physics-informed machine learning (PIML) and physics-informed neural networks (PINNs)
- Reduced Order Modeling
Extend deep learning workflows in areas of reduced order modeling
- Autonomous and Control Systems
Extend deep learning workflows with reinforcement learning, predictive maintenance, and autonomous navigation applications
- Text Analytics and Computational Finance
Extend deep learning workflows with text analytics and computational finance applications
- End-to-End AI Workflows
Use deep learning in end-to-end tasks including defining requirements, data preparation, deep neural training, compression, network testing and verification, Simulink integration, and deployment
Featured Examples
Waveform Segmentation Using Deep Learning
Segment human electrocardiogram signals using time-frequency analysis and deep learning.
ECG Signal Classification Using Deep Learning
Develop and verify a deep learning model that classifies electrocardiogram (ECG) signals to detect atrial fibrillation (AFib). The example guides you through a complete workflow for building an AI system, with a focus on robustness, explainability, and verification against defined requirements.
Sequence-to-Sequence Translation Using Attention
Convert decimal strings to Roman numerals using a recurrent sequence-to-sequence encoder-decoder model with attention.
Compare Deep Learning Networks for Credit Default Prediction
Create, train, and compare three deep learning networks for predicting credit default probability.
Solve Heat Equation Using Graph Neural Network
Solve a heat equation with using a graph neural network (GNN).
Semantic Segmentation Using Deep Learning
Segment an image using a semantic segmentation network.
Lidar Object Detection Using Complex-YOLO v4 Network
Detect objects in point clouds using you only look once version 4 (YOLO v4) deep learning network. In this example, you:
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™.
3-D Brain Tumor Segmentation Using Deep Learning
Perform semantic segmentation of brain tumors from 3-D medical images.
Chemical Process Fault Detection Using Deep Learning
Use simulation data to train a neural network than can detect faults in a chemical process.
Teaching Resources
Machine Learning for Biosciences
Learn the basics of machine learning with biologically motivated examples.
