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MATLAB® lets you develop algorithms much faster than in traditional languages such as C, C++, or Fortran. You can validate concepts, explore design alternatives, and distribute your algorithm in the form that best suits your application. MATLAB provides the tools you need to transform your ideas into algorithms, including:
Thousands of core mathematical, engineering, and scientific functions
Application-specific algorithms in domains such as ** and image processing, control design, computational finance, and computational biology
Development tools for editing, debugging, and optimizing algorithms
These capabilities, combined with MATLAB programs created by the worldwide user community, let you explore approaches that otherwise would be too time-consuming to consider.
Your completed algorithms can be converted into self-contained applications and software components for desktop and Web deployment. Alternatively, you can incorporate the algorithm in a system simulation or an embedded system.
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With MathWorks data acquisition products, you can access data from hardware such as data acquisition boards, test and measurement instruments, CAN bus interface devices, and imaging devices. You also have direct access to data from ODBC-compliant or JDBC-compliant databases, OPC servers, and several financial data servers.
MathWorks data acquisition products provide direct access to data to enable live visualization and analysis and streamline automation of repetitive tasks that require data from external sources.
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Engineering teams use Model-Based Design with MATLAB® and Simulink® to verify and validate embedded systems. Teams author requirements directly in their models and can then use those models to generate production code for certification.
Author requirements in your model, and verify and trace them to the design, tests, and code.
Prove that your design meets requirements, and automatically generate tests.
Check compliance of models and code using static analysis and formal methods.
Find bugs, security vulnerabilities, and prove the absence of critical run-time errors.
Produce reports and artifacts, and certify to standards (such as DO-178 and ISO 26262).
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使用 MATLAB 开发、扩展和部署深度学习模型
MATLAB 可让一个用户实施端到端的工作流程,使用 Deep Learning Toolbox™ 开发和训练深度学习模型。然后,可以通过 Parallel Computing Toolbox 和 MATLAB Parallel Server 使用云和集群资源扩展训练,随后使用 GPU Coder 部署到数据中心或嵌入式设备。
使用 GPU 开发深度学习和其他计算密集型分析
MATLAB 是端到端工作流程平台,适用于 AI 和深度学习开发。MATLAB 提供工具和应用程序,用于导入训练数据集、可视化和调试、利用GPU对CNN进行扩展训练和实现部署。
使用一行代码即可扩展到台式机、云和群集上的其他计算资源和 GPU 资源。
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