AI and Statistics

R2026b

Accelerate statistics, machine learning, and deep learning applications with parallel computing

Use parallel computing to accelerate statistical computations, and training and prediction workflows using machine learning and deep learning models.

Apps

Classification Learner	Train models to classify data using supervised machine learning
Regression Learner	Train regression models to predict data using supervised machine learning
Experiment Manager	Explore parameters in your MATLAB code and compare experiment results (Since R2023b)

Analyze and Model Data on GPU (Statistics and Machine Learning Toolbox)
Accelerate your code by using GPU array input arguments.
Accelerate Linear Model Fitting on GPU (Statistics and Machine Learning Toolbox)
This example shows how you can accelerate regression model fitting by running functions on a graphical processing unit (GPU).

Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud (Deep Learning Toolbox)
Explore options for deep learning with MATLAB^® in parallel and using multiple GPUs, locally or in the cloud.
Run Experiments in Parallel (Deep Learning Toolbox)
Run multiple simultaneous trials or one trial at a time on multiple workers.
Train Deep Learning Networks in Parallel (Deep Learning Toolbox)
This example shows how to run multiple deep learning experiments on your local machine.