Retraining is the process of updating an AI model with new data after its initial training, to enhance the model’s accuracy and adaptability.
Imagine you are learning to play a musical instrument, like a piano. At first, you only learn simple songs. As you get better, you start learning tougher songs. But you don’t stop there; you keep learning more songs to get better and better. This continuous learning and updating your piano skills is exactly like how AI is retrained - AI learns more when it’s exposed to new data, just as you get better with each new song you learn.
In machine learning, models are initially trained on a dataset to learn the underlying patterns or relationships that govern the data – this initial stage is known as training. However, data is dynamic and tends to change over time, causing the model to make less accurate predictions. This is where retraining comes in.
Retraining is the process of feeding new data to an already trained model to fine-tune the model’s parameters. It’s a way of upgrading the model to cater to the latest changes in the data and environment. It often results in an improvement of the model’s accuracy over time and the model’s capability to adapt to new conditions.
In retraining, the model’s initial weights, learned from the first training process, are typically kept as a foundation. The model is then exposed to new data which further adjusts these weights. This implies that the model does not forget its past learning but builds upon it with new information. This can be especially useful in scenarios where the underlying data distribution may change over time or where new categories of data may emerge that were not present during the initial training.
In the domain of deep learning, retraining can often involve a process known as fine-tuning. Initially, a deep learning model may be trained on a large, general dataset. When the model is subsequently used for a more specific task with less available data, it is retrained or fine-tuned on this smaller, specific dataset while preserving the learned features from the initial training.
However, retraining must be handled with caution, because continually retraining a model on new data without proper evaluations can lead to a problem known as ‘concept drift’, where the model begins to slowly lose its ability to accurately predict outcomes due to the changes in input data patterns.
Fine-Tuning, Transfer Learning, Initial Training, Online Learning, Concept Drift