Deep Learning Onramp


 

Access to MATLAB through your web browser

 

Engaging video tutorials

 

Hands-on exercises with automated assessments and feedback

 

Lessons available in English and Japanese


Select a Lesson to Get Started


1.

Introduction

Familiarize yourself with Deep Learning concepts and the course.

  • Deep Learning for Image Recognition
  • Course Overview

2.

Using Pretrained Networks

Perform classifications using a network already created and trained.

  • Course Example - Identify Objects in Some Images
  • Making Predictions
  • CNN Architecture
  • Investigating Predictions
  • Image Datastores

3.

Managing Collections of Data

Import folders of images and make them usable with a given network.

  • Image Datastores
  • Preparing Images to Use as Input
  • Processing Images in a Datastore
  • Modifying Network Layers
  • Create a Datastore Using Subfolders

4.

Performing Transfer Learning

Modify a pretrained network to classify images into specified classes.

  • What is Transfer Learning
  • Components Needed for Transfer Learning
  • Preparing Training Data
  • Modifying Network Layers
  • Setting Training Options
  • Training the Network
  • Evaluating Performance
  • Transfer Learning Summary

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