Fine tuning is a concept in machine learning (ML) and deep learning (DL) that involves slightly adjusting the parameters of an already trained model to make it perform better on a new, similar task. This technique is particularly useful when there is a lack of sufficient training data for the new task, or when the new task is very similar to the task the model was originally trained on.

In the context of neural networks, fine tuning typically involves using a pre-trained model as a starting point, and then adjusting the weights of the model slightly to optimize its performance on the new task. This is done by continuing the backpropagation process on the new task's data, which adjusts the model's weights based on the error it makes on the new task.

Fine tuning is a form of transfer learning, which is a broader concept that involves leveraging the knowledge gained from one ML task to improve performance on another. However, unlike other forms of transfer learning that may involve significant changes to the model's architecture or training process, fine tuning involves only minor adjustments to the model's parameters.

The fine tuning process typically involves the following steps:

1. Selection of Pre-trained Model: The first step in fine tuning is to select a pre-trained model that has been trained on a similar task or dataset. This model serves as the starting point for fine tuning.

2. Adjustment of Model Architecture: Depending on the new task, it may be necessary to adjust the architecture of the pre-trained model. This could involve adding, removing, or modifying layers in the neural network.

3. Fine Tuning: The pre-trained model is then trained on the new task's data, with the weights initialized to the values learned during pre-training. The learning rate is typically set to a smaller value than during pre-training, to avoid large changes to the weights.

4. Evaluation: Finally, the fine-tuned model is evaluated on a validation set to assess its performance on the new task.

Fine tuning is widely used in various fields of AI and ML, including computer vision, natural language processing, and reinforcement learning. For example, in computer vision, a common practice is to fine tune convolutional neural networks (CNNs) that have been pre-trained on large image datasets like ImageNet, to perform tasks like object detection or segmentation on specific domains.

In natural language processing, models like BERT or GPT-2 are often fine-tuned on specific tasks like sentiment analysis or question answering, leveraging the language understanding these models have learned from large-scale pre-training on diverse text corpora.

Fine tuning is a powerful technique in ML and DL that allows leveraging the knowledge captured by pre-trained models to improve performance on new tasks. By making minor adjustments to the model's parameters, fine tuning can achieve high performance with less data and computational resources than training a model from scratch.