Transfer Learning

Transfer learning is a machine learning technique that uses a previously trained model as the starting point for a new, yet related model. Reusing a model saves compute time and helps manage model accuracy.

1 hour 30 minutes 0 Enrolled No ratings yet Beginner

How Transfer Learning Works

The most common transfer learning techniques are feature extraction and fine-tuning. The techniques differ in how the base layer is treated. Feature extraction freezes the base layer; fine-tuning adjusts the base layer.

What Makes a Model Similar?

Often the best strategy for transfer learning is to start with a proven architecture for your network. When selecting a model, consider the following questions.

How similar is the dataset in terms of categories? (For example, dogs versus cats.)
How similar is the type of model architecture? (For example, ResNet* and edges.)
How similar is the type of task? (For example, image classification versus object detection.)

Show More

What's included

  • 12 hours video
  • Certificate
  • 12 Article
  • Watch Offline
  • Lifetime access


0.0Instructor Rating
View Details