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北京天演融智软件有限公司（科学软件网）前身是北京世纪天演科技有限公司，成立于2001年，专注为国内高校、科研院所和以研发为主的企事业单位提供科研软件和服务的国家。
Acquire live data from individual instruments, cards, sensors, or internet of things approaches
Read or write stored data including **s, images, video, and ‘omics data from files, databases, spreadsheets, or via web access
Manage datasets too large to fit in memory
Model and simulate biological systems with an intuitive graphical interface or symbolic math
Refine your models with optimization, curve fitting, and parameter estimation
Design experiments and characterize results with frequentist or Bayesian statistics or machine learning
Report results
Scale up computing to multicore machines and GPUs, clusters and HPC centers, and the cloud
Collaborate and support teams with a range of deployment options including apps, desktop or web executables, and mobile devices
Deploy royalty-free integrations with third-party software and programming languages
Re-use code assets developed and shared by the global scientific community
Readily automate tasks into pipelines or processes using code that can be shared and evolved
MathWorks also provides training and consulting services to help you or your team become more proficient and complete projects faster.
Because MATLAB and Simulink® toolboxes have been trusted by the global science community for over 30 years, researchers and educators have created a large and diverse collection of domain-specific tools written in MATLAB. Many of these tools are freely available at MathWorks File Exchange, GitHub, or the MathWorks Connections Program.
Neuroscientists use MATLAB® to process and analyze their data, drive their experiments, and simulate models of brain circuits. With MATLAB, you can:
Analyze neural time-series data from electrode ** recordings
Understand structural and functional image data from neuroimaging and microscopy studies
Use machine and deep learning to classify, predict, and cluster using models trained with neuroscience data
Process and generate live data streams, including brain-computer interface (BCI) and behavioral control systems
Create and share interactive neuroscience data stories
MATLAB 实现深度学习
设计、构建和可视化卷积网络
只需要几行 MATLAB® 代码，*成为，您就能构建深度学习模型。了解如何使用 MATLAB 帮助您执行深度学习任务。
易于访问新的模型，包括 GoogLeNet、 VGG-16、 VGG-19、AlexNet、ResNet-50、ResNet-101 和 Inception-v3
。
加速 NVIDIA® GPU、云和数据中心资源上的算法，而*编程。
使用 MATLAB 应用程序和可视化工具，创建、修改和分析复杂的深度网络架构。
使用应用程序自动进行图像、视频和音频数据的真实值 (ground-truth) 标注。
处理来自 Caffe 和 TensorFlow-Keras 的模型。
MATLAB 支持 ONNX™，所以您可以使用 PyTorch 和 MxNet 之类的框架与同事协作。
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。
科学软件网不定期举办各类公益培训和讲座，让您有更多机会免费学习和熟悉软件。
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