Deep Network Designer Matlab App Installation

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 usingapps and visualization tools.
Preprocess data and automateground-truth labeling of image, video, and audio data using apps.
Accelerate algorithms onNVIDIA® GPUs, cloud, and datacenter resources without specialized programming.
Collaborate with peers using frameworks likeTensorFlow, PyTorch, and MxNet.
Simulate and train dynamic system behavior withreinforcement learning.
Generatesimulation-based training and test data from MATLAB and Simulink® models of physical systems.

Top 5 Reasons to Use MATLAB for Deep Learning

ARE YOU READY TO EXPLORE NEW FEATURES FOR DEEP LEARNING SOLUTIONS

See How Others Use MATLAB for Deep Learning

Shell

SHELL Uses semantic segmentation for terrain recognition in hyperspectral satellite data.

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Autoliv

Autoliv Labels LIDAR for verification of a radar-based automated driving system.

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Ritsumeikan University

Ritsumeikan University Trains convolutional neural networks on CT images to reduce radiation exposure risk

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Shell

SHELL Uses semantic segmentation for terrain recognition in hyperspectral satellite data.

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Prepare and Label Image, Time-Series, and Text Data

MATLAB significantly reduces the time required to preprocess and label datasets with domain-specific apps for audio, video, images, and text data. Synchronize disparate time series, replace outliers with interpolated values, deblur images, and filter noisy signals. 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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