Fine-tuning can mean making very small changes to something to make it work in the best possible manner. For example, you can fine-tune a radio to the correct frequency in order to get the best and noise free reception of your favorite program.
Another real-world example in cooking analogy would be to adjust the temperature/duration or quality/quantity of ingredients in order to get the best texture, moistness and a delicious cake!
In deep learning, fine-tuning is a technique where we start with a pre-trained machine learning model and then adapt it to perform tasks that we require it to do. The need for fine-tuning stems from the fact that a model is not performing as required or is not capable of doing a task.
Going back to our cake analogy, it’s like, you have the recipe to create a plain vanilla flavor cake. The chef has now received an order to bake a strawberry cake. Although not an expert in baking, we would imagine the process to be similar with just some ‘fine tuning’ adjustments to add the strawberry flavor.
If our chef is inexperienced, it would require him or her to try a few iterations before they get the ‘right’ output. These iterations where the chef tries out different combinations of ingredients, various temperature settings and timings, and then tastes the output to check the ‘deliciousness’, are exactly what fine tuning is.
Using the cake baking parlance, we can understand how to use an existing model and fine tune it to achieve the desired outcome in terms of deep learning. We have many open-source models capable of content generation. We can take such models, train them for specific tasks, evaluate the outcomes and generate a model which will perform much better.
Using fine-tuning we can train models to provide many functionalities not limited by the following:
- Question Answering or Chatbot
- Content summarization
- Dialogue summarization
- Entity extraction
- Pattern recognition
- Data Scoring
Fine-tuning is offered by various commercial providers but there are open-source options as well. Making a choice is a decision to be made based on hardware costs, ease of fine-tuning and of course your data privacy concerns.
We at Beaconcross help companies with consultancy and implementation of any fine-tuning needs they have. Get in touch with us for any need you have in this area.