Deep Learning Onramp

Learn the basics of deep learning for image classification problems in MATLAB®. Use a deep neural network that experts have trained and customize the network to group your images into predefined categories.

Course modules

Introduction

Familiarize yourself with deep learning concepts and the course.

Lessons:
  • Deep Learning for Image Recognition

Use Pretrained Networks

Perform classifications using a network that is already created and trained.

Lessons:
  • Course Example - Identify Objects in Images
  • Make Predictions
  • CNN Architecture
  • Investigate Predictions

Manage Collections of Image Data

Organize and process images to make them usable with a given network.

Lessons:
  • Image Datastores
  • Prepare Images to Use as Input
  • Process Images in a Datastore
  • Create a Datastore Using Subfolders

Perform Transfer Learning

Modify a pretrained network to classify images.

Lessons:
  • What is Transfer Learning?
  • Components Needed for Transfer Learning
  • Prepare Training Data
  • Modify Network Layers
  • Set Training Options
  • Train the Network
  • Evaluate Performance
  • Transfer Learning Summary

Conclusion

Learn next steps and give feedback on the course.

Lessons:
  • Project - Roundworm Vitality
  • Additional Resources
  • Survey

Deep Learning with MATLAB

Learn the theory and practice of building deep neural networks with real-life image and sequence data.

Machine Learning with MATLAB

Explore data and build predictive models.

MATLAB Fundamentals

Learn core MATLAB functionality for data analysis, modeling, and programming.

MATLAB Onramp

Get started quickly with the basics of MATLAB.