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

Using Pretrained Networks

Perform classifications using a network already created and trained.

Lessons:
  • Course Example - Identify Objects in Some Images
  • Making Predictions
  • CNN Architecture
  • Investigating Predictions

Managing Collections of Image Data

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

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

Performing Transfer Learning

Modify a pretrained network to classify images into specified classes.

Lessons:
  • 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

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.