Transfer learning is one of the core concepts leveraged for building Generative AI applications. This course teaches the essentials of transfer learning as well as advanced strategies such as fine-tuning and feature extraction.

Transfer learning is the basis of transformer architecture and it is also one of the concepts on which generative AI large language models are based. It is a technique to leverage base models on domain-specific applications for prediction and outcomes.

In this course, Transfer Learning: Tailoring Neural Networks for Your Data, you'll gain the ability to implement transfer learning on your custom datasets.

First, you'll explore some principles and benefits of transfer learning.

Next, you'll understand different types of transfer learning strategies such as fine-tuning and feature extraction.

Finally, you'll learn about some challenges in transfer learning such as data mismatch, bias in models, and ethical considerations.

When you’re finished with this course, you’ll have the skills and knowledge of transfer learning needed to tailor neural networks for your data.