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.

Why, Use MATLAB for Deep learning?
Deep Learning Topics


MATLAB® supports the entire workflow—from exploration to implementation of signal processing systems built on deep networks. You can easily get started with specialized functionality for signal processing such as:  

  • Analyzing, preprocessing, and annotating signals interactively

  • Extracting features and transforming signals for training deep neural networks

  • Building deep learning models for real-world applications, including biomedical, audio, communications, and radar

  • Acquiring and generating signal datasets through hardware connectivity and simulations


MATLAB® provides an environment to design, create, and integrate deep learning models with computer vision applications.


You can easily get started with specialized functionality for computer vision such as:

  • Image and video labeling apps

  • Image datastore to handle large amounts of data for training, testing, and validation

  • Image and computer vision-specific preprocessing techniques

  • Ability to import deep learning models from TensorFlow™-Keras and PyTorch for image recognition



Deep reinforcement learning lets you implement deep neural networks that can learn complex behaviors by training them with data generated dynamically from simulation models.

MATLAB® and Simulink® support the complete workflow for designing and deploying a reinforcement learning based controller. You can:

  • Get started with reinforcement learning using examples for simple control systems, autonomous systems, and robotics

  • Quickly switch, evaluate, and compare popular reinforcement learning algorithms with only minor code changes

  • Use deep neural networks to define complex reinforcement learning policies based on image, video, and sensor data

  • Train policies faster by running multiple simulations in parallel using local cores or the cloud

  • Deploy reinforcement learning controllers to embedded devices

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