Transfer Learning Tutorial

What is Transfer Learning?

Transfer learning is a machine learning technique where a model trained on one task is reused as the starting point for a model on a related task. 📈

Key Benefits

• Faster Training: Leverage pre-trained weights to reduce computation time.
• Improved Accuracy: Utilize knowledge from large datasets for smaller ones.
• Resource Efficiency: Minimize data and hardware requirements.

Steps to Implement Transfer Learning

1. Choose a Pretrained Model
   Model Library offers popular architectures like ResNet, BERT, etc.
2. Freeze Layers (Optional)
   Prevent gradients from updating certain layers during training.
3. Modify the Output Layer
   Adapt the model to your specific task (e.g., classification labels).
4. Train & Fine-tune
   Use your dataset to adjust the model's performance.

Example Use Cases

• Image classification with pre-trained CNNs
• Text generation using language models
• Object detection with pre-trained frameworks

Extend Your Knowledge

For deeper insights, explore our Deep Learning Tutorials Index to learn about other techniques like fine-tuning or model architecture design.