here is a 4-lesson mini-curriculum on the subject of Transfer Learning.
Lesson 1: Introduction to Transfer Learning
Understanding the Basics of Transfer Learning
Types of Transfer Learning: Inductive, Transductive, Unsupervised
Key Terminology: Source Domain, Target Domain, Fine-tuning, Feature Extraction
Benefits and Use Cases of Transfer Learning
Challenges and Considerations in Transfer Learning
Lesson 2: Pre-trained Models and Architectures
Exploring Pre-trained Models: BERT, GPT-3, VGG, ResNet, etc.
Model Architectures and Variants
Pre-trained Models for NLP, Computer Vision, and Beyond
Leveraging Pre-trained Features and Representations
Evaluation Metrics and Techniques for Model Selection
Lesson 3: Fine-tuning and Feature Extraction
Fine-tuning Strategies and Approaches
How to Prepare Datasets for Fine-tuning
Fine-tuning in Practice: Code Examples and Best Practices
Feature Extraction from Pre-trained Models
Real-world Applications of Fine-tuned Models
Lesson 4: Applied Transfer Learning with Frameworks
Practical Transfer Learning with TensorFlow and PyTorch
Utilizing Pre-trained Models in Hugging Face Transformers
Hands-on Fine-tuning and Feature Extraction
Building Custom Transfer Learning Pipelines
Developing Transfer Learning Models for NLP and Computer Vision Tasks
Case Studies: Implementing Transfer Learning in Real-world Projects
This curriculum progresses from the fundamentals of transfer learning to hands-on application using popular frameworks and libraries. It provides a well-rounded understanding of transfer learning concepts and practical skills for implementing transfer learning in various domains.
