Episode 4: Transfer Learning — Reusing Existing Knowledge

Where does it come from?

Someone who knows how to play tennis doesn't learn badminton from zero. They reuse their reflexes. In the same way, a model that has learned to recognize cats doesn't need to relearn what an edge or a texture is.

In AI, the idea took off with deep networks around 2012, then with LLMs in 2018. The key observation: the first layers learn generic things, the last ones learn specific things.

Why do we do it?

1. Saves time: training from scratch costs millions. Fine-tuning takes a few hours.
2. Saves data: a model pre-trained on 10M images has already "understood" what an image is.
3. Better performance: a fine-tuned model often beats a model trained from scratch on the same data.

The 3 strategies

1. Feature Extraction

You freeze all the layers and train only a new final layer. The model acts as a "feature detector".

2. Fine-Tuning

You unfreeze all or part of the model and retrain it with a very low learning rate.

3. LoRA (Low-Rank Adaptation)

You add small matrices next to the existing ones and train only those: often less than 1% of the model's parameters. Combined with a model loaded in 4-bit (QLoRA, 2023), you can even fine-tune a 70-billion-parameter model on a single high-end GPU.

The link with attention

• You keep the first layers (spelling, grammar, relationships)
• You retrain the last ones (your specific task)

Like a building: you keep the structure (everything the model has already learned) and adjust only a small part of it. Either you redo the top floors (fine-tuning the last layers), or you add a few partitions on top (LoRA).

Key takeaways

• Reuse existing knowledge to learn faster and better
• Three strategies: feature extraction, fine-tuning, LoRA
• Without transfer learning, only the giants could train models