MATLAB for Machine Learning
Train models, tune parameters, and deploy to production or the edge
Using MATLAB®, engineers and other domain experts have deployed thousands of machine learning applications. MATLAB makes the hard parts of machine learning easy with:
- Point-and-click apps for training and comparing models
- Advanced signal processing and feature extraction techniques
- Automatic machine learning (AutoML) including feature selection, model selection and hyperparameter tuning
- The ability to use the same code to scale processing to big data and clusters
- Automated generation of C/C++ code for embedded and high-performance applications
- 与Simulink作为本机或MAT金宝appLAB功能块的集成，用于嵌入式部署或模拟
- All popular classification, regression, and clustering algorithms for supervised andunsupervised learning
- Faster execution than open source on most statistical and machine learning computations
了解其他人如何使用MATLAB进行机器学习
Automotive
PathPartner.
PathPartner为基于雷达的汽车应用程序开发机器学习算法
Energy Production and IA&M
RWE Renewables, Hydro Quebec, IMCORP
Utility Asset Condition Monitoring and Predictive Maintenance using Machine Learning and Artificial Intelligence
Medical Devices and CES
Kinesis Health Technologies
Assessing the Risk of Falls in Older Adults with Inertial Sensors and Machine Learning
自动化机器学习（Automl）
自动生成功能from training data and optimize models using hyperparameter tuning techniques such as Bayesian optimization. Use specialized feature extraction techniques such as wavelet scattering for signal or image data, and feature selection techniques such as neighborhood component analysis (NCA), minimum redundancy maximum relevance (MRMR) or sequential feature selection.
代码生成和Simulink集成金宝app
Deploy statistics and machine learning models to embedded systems and generate readable C or C++ code for your entire machine learning algorithm, including pre and post processing steps. Accelerate verification and validation of your high-fidelity simulations using machine learning models through MATLAB function blocks and native blocks in Simulink.
Scaling & Performance
Use tall arrays train machine learning models to data sets too large to fit in memory, with minimal changes to your code. You can also speed up statistical computations and model training with parallel computing on your desktop, on clusters, or on the cloud.
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