🔄 What is Transfer Learning?
Discover how to leverage pre-trained models to solve new problems with less data and time
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Dropout & Regularization
Stand on the Shoulders of Giants
Transfer learning allows you to use knowledge from models trained on large datasets (like ImageNet with 14M images) for your specific task. Instead of training from scratch, you leverage pre-learned features to achieve better results with less data and time.
🎯 Why Transfer Learning?
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Less data needed: Work with 100s vs millions of images
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Faster training: Hours instead of weeks
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Better performance: Pre-learned features boost accuracy
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Lower cost: Less compute power required
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Training from Scratch
•Need millions of images
•Weeks of training time
•Expensive GPU requirements
•Risk of poor convergence
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Transfer Learning
•Work with 100-1000 images
•Hours of training time
•Single GPU sufficient
•Reliable, proven results
🏆 Popular Pre-trained Models
ResNet-50
50 layers, 25M params
VGG-16
16 layers, 138M params
Inception V3
48 layers, 23M params
EfficientNet
Optimized, 5M params