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      • Fine-tuning in machine learning is the process of adapting a pre-trained model for specific tasks or use cases. It has become a fundamental deep learning technique, particularly in the training process of foundation models used for generative AI.
      www.ibm.com/topics/fine-tuning
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  2. Fine-tuning in AI is the process of adapting a pre-trained model to perform a specific task more effectively. By refining an existing model with new data, we can enhance its performance on specialized tasks without starting from scratch. This technique is crucial for optimizing AI models efficiently.

  3. Fine-tuning is the process of taking a pretrained machine learning model and further training it on a smaller, targeted data set. The aim of fine-tuning is to maintain the original capabilities of a pretrained model while adapting it to suit more specialized use cases.

  4. Feb 9, 2024 · In summary, fine-tuning is about adjusting and improving an AI model for specific tasks and datasets. It’s essential when you need the model to understand and predict more...

    • A. Zhang
  5. Jul 22, 2023 · Fine-tuning is a technique for adapting a pre-trained machine learning model to new data or tasks. Rather than training a model from scratch, fine-tuning allows you to start with...

  6. Aug 25, 2024 · In artificial intelligence, fine tuning refers to the process of adjusting a pre-trained AI model to improve its performance for specific tasks or datasets. This involves training the model on new data, allowing it to adapt and enhance its capabilities to better suit the targeted applications. What else does fine tuning cover in the context of AI?

  7. Jan 8, 2024 · Fine tuning is a technique used to improve the performance of a pre-trained AI model on a specific task. If you are familiar with prompting ChatGPT using examples of inputs and outputs, then you are already halfway there to understanding how to fine tune an AI model.