what is Fine-tuning
Pre-Trained Models: Fine-tuning starts with a pre-trained model that has been trained on a
large dataset for a specific task, such as image classification or natural language
processing. Pre-trained models are often trained on vast amounts of data, which enables them
to learn general features and patterns that are transferable to other tasks.
Task-specific Adaptation: The pre-trained model is then adapted or fine-tuned to a new task
or dataset by modifying its architecture or updating its parameters. Depending on the
specific requirements of the new task, adjustments may be made to the final layers of the
model, such as the output layer for classification tasks, or additional layers may be added
to capture task-specific features.
Transfer Learning: During fine-tuning, the parameters of the pre-trained model are updated
using gradient descent optimization to minimize a task-specific loss function. This process
allows the model to learn task-specific features from the new dataset while retaining the
knowledge and representations learned during pre-training.
Hyperparameter Tuning: Fine-tuning often involves tuning hyperparameters such as learning
rate, batch size, and regularization strength to optimize performance on the new task.
Hyperparameter tuning is typically performed using techniques such as grid search, random
search, or Bayesian optimization.
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