Deep Learning

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

Signal Processing

Acquire and analyze signals and time-series data

image processing and computer vision

Computer Vision

Acquire, process, and analyze images and video

reinforcement learning

Reinforcement Learning

Define, train, and deploy reinforcement learning policies

radar

Radar

Apply artificial intelligence techniques to radar applications

Why MATLAB for Deep Learning?

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

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.

Explore models

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.

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.

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Deep Learning with MATLAB Tutorials and Examples

Whether you are new to deep learning or looking for an end-to-end workflow, explore these MATLAB resources to help with your next project.

See How Others Use MATLAB for Deep Learning

Panel Navigation

Vitesco Technologies

Applying Deep Reinforcement Learning in Power Control

Panel Navigation

Airbus

Using AI and Deep Learning for Automatic Defect Detection

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UT Austin

Converting Brain Signals to Words and Phrases Using Wavelets and Deep Learning

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.

  • Easily access the latest models, including GoogLeNet, VGG-16, VGG-19, AlexNet, ResNet-50, ResNet-101, and Inception-v3.
  • Accelerate algorithms on NVIDIA® GPUs, cloud, and datacenter resources without specialized programming.
  • Create, modify, and analyze complex deep neural network architectures using MATLAB apps and visualization tools. 
  • Automate ground-truth labeling of image, video, and audio data using apps.
  • Work with models from Caffe and TensorFlow-Keras.
  • MATLAB supports ONNX™, so you can collaborate with peers using frameworks like PyTorch and MxNet.

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.