Deep Learning
Transfer Learning — Stand on the Shoulders of Giants
Learn how to leverage pre-trained models to solve new problems with less data and compute.
- Knowledge transfer — reuse learned features from large models
- Fine-tuning — adapt pre-trained weights to your task
- Data efficiency — achieve great results with small datasets
If I have seen further, it is by standing on the shoulders of giants.
Transfer Learning — Complete Guide
Transfer learning reuses a pre-trained model on a new task, dramatically reducing data and training requirements. This is now the default approach in modern ML — training from scratch is the exception.
Why Transfer Learning?
Feature Hierarchy in Pre-trained Models
Transfer Learning Strategies
Implementation
When to Use Transfer Learning
Catastrophic Forgetting
Key Takeaways
What to Learn Next
-> BERT Apply transfer learning in NLP.
-> GPT Architecture Explore large language models.
-> Transformers Master the foundation of modern AI.
-> CNNs Learn about computer vision models.
-> Fine-tuning LLMs Adapt large models to your specific needs.
-> Training Deep Networks Master optimizers and regularization.