Transfer Learning Mini-curriculum

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.