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Transfer learning is a technique where a model trained on one task is reused as the starting point for a different but related task. Instead of training from scratch, you leverage the knowledge the model has already gained, which saves time, data, and computational resources.
Transfer learning makes AI accessible to smaller organisations that lack the massive datasets and computing power needed to train large models from the ground up.
A small radiology startup takes a model pre-trained on millions of general images and fine-tunes it on a few thousand chest X-rays to detect pneumonia with high accuracy.
Fine-Tuning
Fine-tuning is the process of taking a pre-trained model and further training it on a smaller, specialised dataset to adapt it for a specific task. It allows you to leverage the general knowledge of a large model while tailoring its behaviour to your particular needs.
Foundation Model
A foundation model is a large AI model trained on broad, diverse data that can be adapted for a wide range of downstream tasks. These models serve as a starting point — or foundation — that can be fine-tuned or prompted for specific applications.
Deep Learning
Deep learning is a subset of machine learning that uses neural networks with many layers to learn complex patterns from large amounts of data. The 'deep' refers to the multiple layers that progressively extract higher-level features from raw input.
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