# 04 - Knowledge Distillation: Compress Models Efficiently

Complete guide to Knowledge Distillation: Compress Models Efficiently: architecture, practical implementation and best practices for developers and technical teams.

## What you'll learn

- Distillation theory: soft labels, temperature, and dark knowledge
- Complete standard distillation implementation with PyTorch
- Feature Distillation: transferring intermediate representations
- Attention Transfer: distilling Transformer attention maps
- Self-Distillation and Born Again Networks

*This article is part of the **Deep Learning Avanzato** series on federicocalo.dev.*

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## Read the full article

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

**➡️ [04 - Knowledge Distillation: Compress Models Efficiently](https://federicocalo.dev/en/blog/knowledge-distillation-compress-models-efficiently)**

`https://federicocalo.dev/en/blog/knowledge-distillation-compress-models-efficiently`

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*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*