Deep learning models are data-hungry; but what if you could leverage a pre-trained network to solve your problem with much less data?
In this 90-minute session, we’ll explore transfer learning, a powerful technique that enables you to use knowledge gained from one task and apply it to another.
You’ll learn about the types of transfer learning (fine-tuning vs feature extraction), the benefits of transfer learning, and how to implement these strategies using Keras and pre-trained models.
We’ll dive into feature extraction, where we use frozen pre-trained layers, and fine-tuning, where we allow some layers to be updated during training.
Through hands-on examples on an image dataset, you’ll see how to apply transfer learning to boost model performance with less data and fewer resources.
Whether you’re tackling a new classification problem or trying to improve an existing model, this session will equip you with the knowledge to use transfer learning effectively in your projects.
Duration: 90 mins
Course Code: BDT497
Learning Objectives:
After this course, you will be able to:
Training material provided: Yes (Digital format)
Hands-on Lab: Instructions will be provided to install Jupyter notebook and other required python libraries. Students can opt to use ‘Google Colaboratory’ if they do not want to install these tools
