AI, Data Science, and Statistics
MATLAB® makes data science easy with tools to access and preprocess data, build machine learning and predictive models, and deploy models.
Using apps or with just a few lines of MATLAB code, you can apply statistical, machine, and deep learning techniques to your work for designing algorithms, preparing and labeling data, or generating code and deploying to embedded systems. Extend AI modeling and data fitting workflows with specialized tools for:
Data types such as images, video, signals, audio, and text
Applications such as computer vision, audio and signal processing, text analytics, wireless communications, and automated driving.
Products for AI, Data Science, and Statistics
- Machine Learning in MATLAB (Statistics and Machine Learning Toolbox)
Discover machine learning capabilities in MATLAB for classification, regression, clustering, and deep learning, including apps for automated model training and code generation.
- Deep Learning in MATLAB (Deep Learning Toolbox)
Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.
- What Is Reinforcement Learning? (Reinforcement Learning Toolbox)
Reinforcement learning is a goal-directed computational approach where a computer learns to perform a task by interacting with an uncertain dynamic environment.
- Train Classification Models in Classification Learner App (Statistics and Machine Learning Toolbox)
Workflow for training, comparing and improving classification models, including automated, manual, and parallel training.
- Train Regression Models in Regression Learner App (Statistics and Machine Learning Toolbox)
Workflow for training, comparing and improving regression models, including automated, manual, and parallel training.
- Build Networks with Deep Network Designer (Deep Learning Toolbox)
Interactively build and edit deep learning networks in Deep Network Designer.
Simulation and Deployment
- Battery State of Charge Estimation in Simulink Using Deep Learning Network (Deep Learning Toolbox)
This example shows how to use a feedforward deep learning network inside a Simulink® model to predict the state of charge (SOC) of a battery.
- Code Generation for Deep Learning Networks (GPU Coder)
Get started with CUDA code generation for image classification networks such as
- Code Generation for Deep Learning Simulink Model That Performs Lane and Vehicle Detection (Embedded Coder)
This example shows how to generate C++ code from a Simulink® model that performs lane and vehicle detection using convolutional neural networks (CNN).