Deep Learning Solutions
Data preparation, design, simulation, and deployment for deep neural networks
Deep Learning Applications
With just a few lines of MATLAB code, you can incorporate deep learning into your applications whether you’re designing algorithms, preparing and labeling data, or generating code and deploying to embedded systems.
Discover the Applications : |
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Signal ProcessingAcquire and analyze signals and time-series data |
Computer VisionAcquire, process, and analyze images and video |
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Deep Reinforcement LearningDefine, train, and deploy reinforcement learning policies |
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RadarApply artificial intelligence techniques to radar applications |
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LidarApply artificial intelligence techniques to lidar applications |
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WirelessApply AI techniques to wireless communications applications |
RoboticsApply AI to enable autonomy in robotics applications |
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See How Others Use MATLAB for Deep Learning
Ebook
Integrating AI into System-Level Design
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Why MATLAB for Deep Learning?
MATLAB makes it easy to move from deep learning models to real-world artificial intelligence-driven systems.
Preprocess DataUse interactive apps to label, crop, and identify important features, and built-in algorithms to help automate the process of labeling. |
Train and Evaluate ModelsStart with a complete set of algorithms and prebuilt models, then create and modify deep learning models using the Deep Network Designer app. |
Simulate DataTest deep learning models by including them into system-level Simulink simulations. Test edge-case scenarios that are difficult to test on hardware. Understand how your deep learning models impact the performance of the overall system. |
Deploy Trained NetworksDeploy your trained model on embedded systems, enterprise systems, FPGA devices, or the cloud. Generate code from Intel®, NVIDIA®, and ARM® libraries to create deployable models with high-performance inference speed. |
Integrate with Python-Based Frameworks
MATLAB lets you access the latest research from anywhere by importing Tensorflow models and using ONNX capabilities. You can use a library of prebuilt models, including NASNet, SqueezeNet, Inception-v3, and ResNet-101 to get started. Calling Python from MATLAB and vice versa enables you to collaborate with colleagues who are using open source.
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