MATLAB for Deep Learning

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.

Signal Processing

Acquire and analyze signals and time-series data

Computer Vision

Acquire, process, and analyze images and video

Deep Reinforcement Learning

Define, train, and deploy reinforcement learning policies

Radar

Apply artificial intelligence techniques to radar applications

Lidar

Apply artificial intelligence techniques to lidar applications

Wireless

Apply AI techniques to wireless communications applications

Robotics

Apply AI to enable autonomy in robotics applications

Why MATLAB for Deep Learning?

MATLAB makes it easy to move from deep learning models to real-world artificial intelligence-driven systems.

AI for Engineers: Building an AI System Video (3:40)

Preprocess Data

Use interactive apps to label, crop, and identify important features, and built-in algorithms to help automate the process of labeling.

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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.

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Simulate Data

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.

Integrating AI into System-Level Design

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.

Explore Edge AI options

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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