Training a deep learning model from scratch is often resource-heavy. It requires huge datasets, advanced hardware, and weeks of computation. But developers today have a faster and smarter option — Transfer Learning.
So, what exactly is transfer learning in deep learning? In simple words, it’s about reusing a model that has already been trained on one task and adapting it to a new, but related, task. Instead of starting from zero, you fine-tune a pre-trained model using a smaller dataset. This drastically cuts down training time and cost, while still giving strong performance.
Think of it like this: if you know how to ride a scooter, learning to ride a bike is easier. The balance you learned is transferred. In the same way, a neural network that has already learned features like edges, colours, or shapes can reuse that knowledge when solving new problems.
How it works:
Pre-training: The model learns generic features from a large dataset.
Fine-tuning: The same model is adjusted to a specific dataset with fewer samples.
Where it’s used:
Healthcare: Detecting tumours in medical images.
NLP: Sentiment analysis, chatbots, and machine translation.
Computer Vision: Self-driving cars, object detection, facial recognition.
Finance & Agriculture: Fraud detection, crop disease monitoring.
For developers, transfer learning means faster prototyping, less dependency on massive datasets, and better accuracy with limited resources. It’s one of the key methods driving modern AI adoption across industries.
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