24 Sep Transfer Learning in Machine Learning

Transfer Learning is a technique in machine learning where a model developed for one task is reused as the initial point for a model on a second, related task. This type of learning is useful when:

• The data is limited for the second task
• Training models from the beginning is expensive
• Training models from the beginning takes a lot of time

Transfer learning examples in real-life

Let’s say you are keen to learn how to play the guitar. After learning the guitar and playing it a lot of time, now you want to learn the piano. Since now you know how music works, the notes, hand coordination, voices, rhythm, etc, you can easily learn the piano. And this learning would be better if you started learning piano directly from the beginning without any prior knowledge of music, rhythm, notes, etc.

The concept of transfer learning works similarly in Machine Learning. Consider a pre-trained model instead of training a new model from the beginning. Use this pre-trained model as the initial point for a second related task. Here:

• Pre-trained model: Completed Guitar learning
• New (i.e. second) related task: Learning the piano

Transfer learning examples to recognize images

You already have a model trained to recognize the images of dogs and wolves. The model learned to identify some attributes of these animals, such as long muzzles, upright ears, tails, etc.

Now you want to recognize different types of animals, such as coyotes, and jackals. For that, train a model to recognize these animals.

1. Pre-trained Model: You initiated the dogs and wolves’ recognition model.
2. Feature Extraction: Under this, the knowledge i.e. the features the model has already learned about animals, such as dogs and wolves are used.
3. Fine-Tuning: The model is trained further with the images of coyotes, and jackals, adjusting it slightly to recognize these new animals.

Steps in Transfer Learning

1. Load a Pre-trained Model: Use a model that has already been trained on a large dataset.
2. Freeze the Base Layers: Keep the learned features intact and only train the new layers.
3. Add New Layers: Add layers specific to your new task, such as recognizing coyotes and jackals.
4. Train the Model: Train the new layers with your specific dataset.

Advantages of Transfer Learning

• Nothing from scratch: No need to train a model from scratch.
• Requires Less Data: You can achieve good performance even with a smaller dataset. Limited data for the new task is not a problem. Transfer learning is effective even with limited data.
• Enhances Accuracy: Working with pre-trained models often results in better performance.
• Requires less training time: Training a model from scratch takes a lot of time. A model developed for one task is reused as the initial point for a model on a second, related task. This saves time.

• Enhanced Performance: Pre-trained models often provide better performance, especially when the new dataset is small.

Applications of Transfer Learning

• Computer Vision: Image classification, object detection, etc.
• Natural Language Processing: Sentiment analysis, language translation, text classification, etc.

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Read More:

• Overview of Machine Learning
• Applications of Machine Learning
• What is a Machine Learning Model
• Steps to Build a Machine Learning Model
• Train/ Test in a Machine Learning Model
• Inductive vs Deductive Machine Learning
• Bagging vs Boosting in Machine Learning
• What are Large Language Models (LLMs)
• Artificial Intelligence vs Machine Learning vs Data Science vs Deep Learning
• Types of Deep Learning
• Techniques for implementing Generative AI
• What are Transformers in Deep Learning