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

Deep Reinforcement Learning

Define, train, and deploy reinforcement learning policies



Apply artificial intelligence techniques to radar applications



Apply artificial intelligence techniques to lidar applications



Apply AI techniques to wireless communications applications



Apply AI to enable autonomy in robotics applications


Natural Language Processing

Build AI systems that understand and derive information from human languages

Why MATLAB for Deep Learning?

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

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.

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.

Learn more

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.

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?


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.

Semantic Segmentation

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.