Training and fine-tuning pretrained deep learning models in MATLAB
Transfer learning is a machine learning approach wherein a model created for a particular task is repurposed as the initial foundation for a model addressing a second task. This technique entails leveraging knowledge acquired from one problem and applying it to a distinct yet related problem. Transfer learning is particularly useful in deep learning and neural networks, where pretrained models can be fine-tuned or used as feature extractors for new tasks.
In pretrained models, you start with a pretrained model that has been trained on a large dataset for a specific task, such as image classification, natural language processing, or speech recognition. These pretrained models are often complex neural networks with many layers. In many cases, you can use the layers of the pretrained model as feature extractors. You remove the final classification layer(s) and use the activations from the earlier layers as features...