Transfer learning is a machine learning methodology in which a model trained on one large task is reused as the starting point for a different, often narrower task, rather than training from random weights. The assumption is that useful representations—edges in images, grammar patterns in text, semantic relationships—learned during the first task will remain valuable for the second. In practice this means taking a large Foundation Model pretrained on broad data and then Fine-tuning it on a specialist dataset, updating only some or all parameters at a much lower cost than full training.

Think of it like hiring a seasoned professional who already speaks the industry's language: you invest a short onboarding period rather than teaching every concept from scratch. The professional's years of general experience transfer directly; you only fill the specific gaps.

from transformers import AutoModelForSequenceClassification, Trainer
# Load BERT pre-trained weights and add a classification head
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
# Fine-tune on your sentiment dataset — only a fraction of BERT's training cost
trainer = Trainer(model=model, train_dataset=train_ds, eval_dataset=eval_ds)
trainer.train()

The pre-trained BERT weights give the model a head-start; your labelled data steers it toward the specific task.

Transfer learning is the conceptual foundation for most modern NLP and vision pipelines; it explains why Fine-tuning and LoRA work, and why Knowledge Distillation (compressing large models into smaller ones) is feasible. Without it, each new application would require training a Neural Network from scratch.

You'll encounter transfer learning whenever you adapt a pre-trained model for a custom dataset, use a vision backbone for a niche classification task, or compare the cost of fine-tuning against training a purpose-built model from zero.