Transfer Learning
In the previous lesson, we learned about optimizers and how they control the learning process of models.
In this lesson, we explore a powerful idea that allows models to learn faster and better: Transfer Learning.
Transfer learning is one of the main reasons deep learning has become so successful in real-world systems.
What Is Transfer Learning?
Transfer learning is a technique where a model trained on one problem is reused for a different but related problem.
Instead of starting from scratch, we transfer knowledge from an existing model and adapt it to a new task.
This saves time, computation, and often improves performance.
Why Transfer Learning Works
Many real-world problems share common patterns.
For example, images share edges and shapes, and text shares grammar and structure.
A model trained on a large dataset already understands these basic patterns.
Transfer learning allows us to reuse this understanding instead of relearning everything.
Transfer Learning in Deep Learning
Transfer learning is most commonly used with deep neural networks.
Early layers of deep models learn general features.
Later layers learn task-specific details.
In transfer learning, we keep early layers and retrain later layers for a new task.
Relating Transfer Learning to Our Dataset
Suppose we have a large financial dataset used to predict loan defaults.
We can reuse that trained model to predict loan approval using the Dataplexa ML dataset.
The financial patterns learned earlier help the new model learn faster.
How Transfer Learning Is Applied
The most common steps are:
First, load a pretrained model.
Second, freeze some layers so their weights do not change.
Third, retrain remaining layers on the new dataset.
This approach reduces overfitting and speeds up training.
Real-World Examples
In image recognition, models trained on ImageNet are reused for medical imaging.
In natural language processing, language models trained on large text corpora are fine-tuned for chatbots or sentiment analysis.
These systems would be impossible to train from scratch for every task.
Benefits of Transfer Learning
Transfer learning reduces training time, requires less data, and improves accuracy.
It is especially useful when labeled data is limited.
This makes it ideal for many business applications.
Mini Practice
Think about a problem where data is limited.
Consider how a pretrained model could help solve it faster.
Exercises
Exercise 1:
Why is transfer learning faster than training from scratch?
Exercise 2:
Which layers are usually frozen in transfer learning?
Quick Quiz
Q1. Is transfer learning useful only for deep learning?
In the next lesson, we apply everything we have learned by analyzing real-world Machine Learning Case Studies.