Deep Learning Tutorials & Examples - MATLAB & Simulink

Deep Learning Tutorials and Examples with MATLAB

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Get Started with Deep Learning Using MATLAB

Perform four common deep learning tasks with MATLAB.

This video series addresses deep learning topics for engineers such as accessing data, training a network, using transfer learning, and incorporating your model into a larger design.

Open and Explore This Interactive Example in MATLAB Online

Classification with a pre-trained model

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Create Simple Deep Learning Network for Classification

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Deep Neural Networks (4 videos)

MATLAB makes it easy to create and modify deep neural networks. These tutorial videos outline how to use the Deep Network Designer app, a point-and-click tool that lets you interactively work with your deep neural networks.

Deep Learning Cheat Sheet

This quick start guide shows some common use cases for deep learning with MATLAB.

Deep Learning for Computer Vision Specialization

In this course you will learn how to apply deep learning and computer vision to real-world projects.

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Apply Your Expertise

Train Models from Scratch

Train a convolutional neural network (CNN) to identify handwritten digits.

Pretrained Deep Learning Models

Explore and download deep learning models that you can use directly with MATLAB.

MATLAB for Deep Learning Tutorial

This hands-on, two-hour tutorial is an introduction to practical deep learning methods for image recognition.

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Enroll in Instructor-Led Courses

Deep Learning with MATLAB

Create and modify deep neural networks for classification, regression, and object detection with image and sequence data.

Automated Driving with MATLAB

Use MATLAB to perform essential automated driving tasks. Label ground truth data, detect lanes and objects, generate driving scenarios and modeling sensors, and visualize sensor data.

Signal Preprocessing and Feature Extraction for Data Analytics with MATLAB

Prepare time-series data for machine learning analysis. Topics include importing signals, removing outliers, and extracting features in time and frequency domains.