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.
Why MATLAB for Deep Learning?
MATLAB makes it easy to move from deep learning models to real-world artificial intelligence-driven systems.
Use interactive apps to label, crop, and identify important features, and built-in algorithms to help automate the process of labeling.
Train and Evaluate Models
Start with a complete set of algorithms and prebuilt models, then create and modify deep learning models using the Deep Network Designer app.
Test 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 Networks
Deploy 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.