Transfer Learning

Transfer Learning is a machine learning technique where a model developed for a specific task is reused as the starting point for a model on a second task. This approach allows for leveraging the knowledge gained from one domain (the source domain) to improve learning in another related but different domain (the target domain). It is particularly useful when the target task has limited data available, as the pre-trained model can harness learned features and representations from the source task, which often has abundant data.Typically, Transfer Learning involves taking a pre-trained model, such as those trained on large datasets (e.g., ImageNet for visual tasks, or BERT for natural language processing), and fine-tuning it on a smaller dataset specific to the target task. This can significantly reduce training time and improve the performance of the model on the target task compared to training a model from scratch. The technique is widely used in various fields such as computer vision, natural language processing, and speech recognition, as it enhances the efficiency and effectiveness of model training and deployment.