MATLAB for Deep Learning

Data preparation, design, simulation, and deployment for deep neural networks

With just a few lines of MATLAB^® code, you can build deep learning models without having to be an expert. Explore how MATLAB can help you perform deep learning tasks:

Create, modify, and analyze deep learning architectures using apps and visualization tools.
Preprocess data and automate ground-truth labeling of image, video, and audio data using apps.
Accelerate algorithms on NVIDIA^® GPUs, cloud, and datacenter resources without specialized programming.
Collaborate with peers using frameworks like TensorFlow, PyTorch, and MxNet.
Simulate and train dynamic system behavior with reinforcement learning.
Generate simulation-based training and test data from MATLAB and Simulink^® models of physical systems.

Learn how MATLAB can help with any part of the deep learning workflow: from preprocessing through deployment. Get a high-level overview of deep learning with MATLAB, and explore several applications.

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MATLAB for Deep Learning

Why Use MATLAB for Deep Learning?

Interoperability

It’s not an either/or choice between MATLAB and Python-based frameworks. MATLAB supports interoperability with open source deep learning frameworks using ONNX import and export capabilities. Use MATLAB tools where it matters most – accessing capabilities and prebuilt functions and apps not available in Python.

Use labeling apps for deep learning workflows like semantic segmentation.

Apps for Preprocessing

Get to network training quickly. Preprocess datasets fast with domain-specific apps for audio, video, and image data. Visualize, check, and fix problems before training using the Deep Network Designer app to create complex network architectures or modify pretrained networks for transfer learning.

Multi-Platform Deployment

Deploy deep learning models anywhere including CUDA, C code, enterprise systems, or the cloud. When performance matters, you can generate code that leverages optimized libraries from Intel^® (MKL-DNN), NVIDIA (TensorRT, cuDNN), and ARM^® (ARM Compute Library) to create deployable models with high-performance inference speed.