What is the difference between fine-tuning and training from scratch?
An example of an AI chatbot conversation about fine-tuning vs training from scratch in AI models.
What is the difference between fine-tuning and training from scratch?
Fine-tuning involves taking a pre-trained model and adjusting it on a specific dataset, while training from scratch means building a model from the ground up without prior knowledge.
When should I choose fine-tuning over training from scratch?
You should choose fine-tuning when you have a limited dataset or want to leverage existing knowledge, as it can save time and resources.
Can you give me an example of when to use each method?
Sure! Use fine-tuning for tasks like sentiment analysis on a small dataset, and training from scratch for unique tasks with ample data, like creating a new language model.
What are the advantages of training from scratch?
Training from scratch allows for complete control over the model architecture and can lead to better performance on specialized tasks if sufficient data is available.
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