What is Fine-tuning?
Fine-tuning is a technique used in machine learning and deep learning where a pre-trained model is further trained on a new, target dataset to adapt its weights and biases for the specific task. Fine-tuning is particularly useful when the target dataset is small, as it leverages the learned features from the pre-trained model, which has usually been trained on a much larger dataset.
How does Fine-tuning work?
Fine-tuning involves initializing a model with the pre-trained weights from a base model, then training the model on the target dataset for a smaller number of epochs or with a smaller learning rate. This allows the model to adapt to the specific task while preserving the learned features from the base model. Fine-tuning can be performed on the entire model or on specific layers, depending on the problem and the desired level of adaptation.
Resources for Fine-tuning:
Fine-tuning Open AI - A guide for fine-tuning models using OpenAI platform.
A Comprehensive Guide to Fine-tuning Deep Learning Models - A detailed guide on fine-tuning deep learning models using Keras.
Fine-Tuning in Keras - Official guide from Keras on fine-tuning and transfer learning.
Saturn Cloud - A platform for free cloud compute resources.