AI with MATLAB and Python
Collaborate with colleagues who work in other deep learning frameworks to train and test PyTorch® or TensorFlow™ models using Python coexecution.
Use the Signal Processing toolboxes to preprocess signals, generate data sets, and extract features to train and test Python models.
Integrate your work in Model Based Design workflows. For more information, see Deep Learning with Simulink (Deep Learning Toolbox).
Import and deploy your system onto several possible platforms after qualifying your design. For more information, see Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX (Deep Learning Toolbox) and Code Generation for PyTorch and LiteRT Models (MATLAB Coder).
Exercise audio models in real time using low-latency audio I/O. You can also deploy models as audio plugins for portability and use in a digital audio workstation.
Related Information
Topics
- Python Coexecution
Coexecute Python models in MATLAB to implement AI signal processing workflows. (Since R2026a)
Featured Examples
Train PyTorch Speech Command Recognition Model
Learn how to combine MATLAB data processing and visualization capabilities with a Python deep learning framework.
Use Signal Feature Extraction to Train PyTorch Fault Detection Model
Coexecute a PyTorch long short-term memory neural network in MATLAB to identify faulty bearing signals.
Signal Imputation in Signal Analyzer Using Time-Series Foundation Model
Coexecute a PyTorch time-series foundation model in Signal Analyzer to restore missing samples in signals.
Anomaly Detection in Signal Labeler Using Time-Series Foundation Model
Coexecute a PyTorch time-series foundation model in Signal Labeler to detect anomalies in signals.
