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.
Author Name: Ranjan Relan
Author Description:
Ranjan Relan is a Founder/CTO of GenAI company – AgentAnalytics.com . He previously worked as AI, Data and Tech Strategy Consultant with more than 16+ years of experience in the field of Analytics which includes working on Machine Learning, Big data and Data ware housing projects. He has helped clients across Hi Tech, Telecom and Pharma domain in developing data strategy, cloud adoption strategy, analytics strategy ,solution architecture.

Table of Contents

• Course Overview
  1min
• Understanding Transfer Learning
  13mins
• Implementation and Challenges of Transfer Learning
  14mins