11 - Transfer Learning and Fine-Tuning: Reusing Pre-trained Models

Accelerate ML: reuse pre-trained models with transfer learning. Learn when it makes sense, how fine-tuning works, layer freezing vs unfreezing, data augmentation. Use cases: image classification, NLP, when it\

What you'll learn

• Transfer learning reuses models pre-trained on large datasets for new problems
• Feature extraction: freeze the backbone, train only the new classification head
• Fine-tuning: progressively unfreeze layers with a low learning rate
• Works best when source and target domains are similar and target dataset is small
• ResNet/EfficientNet for images, BERT/GPT for text are the standard models

This article is part of the Machine Learning series on federicocalo.dev.

Read the full article

The complete article (15 min read) with code examples, diagrams, and practical exercises is available here:

➡️ 11 - Transfer Learning and Fine-Tuning: Reusing Pre-trained Models

https://federicocalo.dev/en/blog/transfer-learning-fine-tuning-pretrained-models

By Federico Calò — Software Developer & Technical Writer