Transfer learning – day 29
Understanding Transfer Learning in Deep Neural Networks Understanding Transfer Learning in Deep Neural Networks: A Step-by-Step Guide In the realm of deep learning, transfer learning has become a powerful technique for leveraging pre-trained models to tackle new but related tasks. This approach not only reduces the time and computational resources required to train models from scratch but also often leads to better performance due to the reuse of already-learned features. What is Transfer Learning? Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second, similar task. For example, a model trained to recognize cars can be repurposed to recognize trucks, with some adjustments. This approach is particularly useful when you have a large, complex model that has been trained on a vast dataset, and you want to apply it to a smaller, related dataset without starting the learning process from scratch. Key Components of Transfer Learning In transfer learning, there are several key components to understand: Base Model: This is the pre-trained model that was initially developed for a different task. It has already learned various features from a large dataset and can provide...
