Artificial Intelligence

Apply machine learning and deep learning

Deep Learning Toolbox R2024a: Major Update and New Examples

On March 20th, MATLAB R2024a was released with many updates for Deep Learning Toolbox. Exciting new features for deep learning help engineers create and use explainable, robust, and scalable deep learning models for automated visual inspection, wireless communications, computer vision, and many more applications.
MATLAB Deep Learning Toolbox product page
Some of the new Deep Learning Toolbox capabilities are:
  • Simulink co-execution blocks to simulate Python®-based (PyTorch®, TensorFlow™, ONNX™, and custom) models in a system-wide context.
  • Explainability and verification tools to explain network results and verify the reliability of deep neural networks.
  • Support for more deep learning architectures, including transformers, and training options.
Find out more about Deep Learning Toolbox features and new capabilities:
In this blog post, we present a few new examples that show how to use the new features and apply deep learning. In future blog posts, we will dive deeper into individual new features.

New Examples on Features

Model Design Explainability and Verification Integration with Python
Create and Train Network with Nested Layers Verification of Neural Networks Predict Responses Using PyTorch Model Predict Block

New Examples on Applications

Computer Vision Natural Language Processing Wireless Communications
Interactively Segment Image Using Segment Anything Model Out-of-Distribution Detection for LSTM Document Classifier AI for Positioning Accuracy Enhancement
Segment CT Scan Using MONAI Label Classify Documents Using Document Embeddings Data Preparation for Neural Network Digital Predistortion Design
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