MATLAB for Deep Learning
Data preparation, design, simulation, and deployment for deep neural networks
With just a few lines of MATLAB®code, you can apply deep learning techniques to your work whether you’re designing algorithms, preparing and labeling data, or generating code and deploying to embedded systems.
With MATLAB, you can:
- Create, modify, and analyze deep learning architectures using应用程序和可视化工具.
- 预处理数据和自动化地面真理标签使用应用程序的图像，视频和音频数据。
- Accelerate algorithms onNVIDIA®GPUS., cloud, and datacenter resources without specialized programming.
- Collaborate with peers using frameworks likeTensorflow，Pytorch，和mxnet。
- Simulate and train dynamic system behavior with加强学习.
- Generate仿真为基础training and test data from MATLAB and Simulink®models of physical systems.
了解其他人如何使用Matlab进行深度学习
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Uses semantic segmentation for terrain recognition in hyperspectral satellite data.
Autoliv
Labels LIDAR for verification of a radar-based automated driving system.
Ritsumeikan University
Trains convolutional neural networks on CT images to reduce radiation exposure risk.
Featuring: AI In Engineering
Integrate with Python-Based Frameworks
It’s not an either/or choice between MATLAB and open source frameworks. MATLAB allows you to access the latest research from anywhere using ONNX import capabilities, and you can also use a library of prebuilt models, including NASNet, SqueezeNet, Inception-v3, and ResNet-101, to get started quickly. The ability to call Python from MATLAB and MATLAB from Python allows you to easily collaborate with colleagues that are using open source.
德ploy Trained Networks
德ploy your trained model on embedded systems, enterprise systems, FPGA devices, or the cloud. MATLAB supports automatic CUDA® code generation for the trained network as well as for preprocessing and postprocessing to specifically target the latest NVIDIA GPUs.
When performance matters, you can generate code that leverages optimized libraries from Intel®, NVIDIA, and ARM®to create deployable models with high-performance inference speed. For edge deployment you can prototype your network on an FPGA and then generate production-ready HDL to target any device.
深度学习Topics
Signal Processing
获取和分析信号和时间序列数据。
计算机视觉
Acquire, process, and analyze images and video.
Reinforcement Learning
德fine, train, and deploy reinforcement learning policies.
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Have Questions?
Talk to a deep learning expert.
更深入学习
- 深度学习Tutorial Series(5 Videos)
- 深入学习计算机愿景（27:59）
- 德ep Neural Networks Series（2个视频）
- 9个Matlab备注用于数据科学和机器学习
- 深入学习，使用综合数据进行通信和雷达(White Paper)
