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原产地美国
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适用平台windows,mac,linux
科学软件网专注提供科研软件。截止目前,共代理千余款,软件涵盖各个学科。除了软件,科学软件网还提供课程,包含40款软件,80门课程。热门软件有:spsspro,stata,gams,mathematica,sas,spss,matlab,mathematica,lingo,hydrus,gms,pscad,eviews,nvivo,gtap,sequncher,simca等等。
Public Clouds
Use MATLAB on virtual machines in public cloud environments like Amazon Web Services (AWS) and Microsoft Azure. These vendors provide access to on-demand computing resources. They also offer wide-ranging, prebuilt services for data storage, data streaming, elastic scaling, load balancing, security, and more.
If you are not a cloud expert, or if you want a head start, use a MathWorks published reference architecture. Templates in these reference architectures automatically create and configure the cloud infrastructure for running MATLAB. You can also adapt or extend the reference architectures to better meet your specific needs.
MATLAB在数据科学中的应用:
Explore data; build machine learning models;
do predictive analytics
MATLAB® makes data science easy with tools to access and preprocess data, build machine learning and predictive models, and deploy models to enterprise IT systems.
Access data stored in flat files, databases, data historians, and cloud storage, or connect to live sources such as data acquisition hardware and financial data feeds
Manage and clean data using datatypes and preprocessing capabilities for programmatic and interactive data preparation, including apps for ground-truth labeling
Document data analysis with MATLAB graphics and the Live Editor notebook environment
Apply domain-specific feature engineering techniques for sensor, text, image, video, and other types of data
Explore a wide variety of modeling approaches using machine learning and deep learning apps
Fine-tune machine learning and deep learning models with automated feature selection and hyperparameter tuning algorithms
Deploy machine learning models to production IT systems, without recoding into another language
Automatically convert machine learning models to standalone C/C++ code
为什么使用 MATLAB 实现深度学习?
互操作性
不再有框架基于 MATLAB 还是 Python 的选择题。使用 ONNX 导入和导出功能,MATLAB 支持与开源深度学习框架的互操作性。使用 MATLAB 工具的大意义在于——访问 Python 中没有的功能与预置函数及应用程序。
预处理应用程序
快速开始网络训练。使用特定领域应用程序快速预处理音频、视频和图像数据集。使用 Deep Network Designer 应用程序创建复杂的网络架构,或修改预训练网络以进行迁移学习,在训练之前可视化、检查并修复问题。
多平台部署
可随处部署深度学习模型,包括 CUDA、C 代码、企业系统或云。若在意性能,您可以利用 Intel® (MKL-DNN)、NVIDIA(TensorRT、cuDNN)和 ARM® (ARM Compute Library) 优化库生成代码,创建具有高性能推理速度的可部署模型。
Behavioral Analog/Mixed-Signal Design
Modeling and simulating analog/mixed-signal systems in circuit-level tools is time consuming and limits the number of design alternatives engineers can evaluate. By contrast, MATLAB and Simulink provide a higher level of design abstraction and faster modeling and simulation, which enables engineers to quickly evaluate numerous design options.
Semiconductor engineers use MATLAB and Simulink models as golden references when they create transistor-level circuit models. These MATLAB and Simulink models integrate with analog/mixed-signal design tools such Cadence® Virtuoso® AMS Designer, and can also be used as behavioral verification models in production SystemVerilog test environments.
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