Fine-Tuned Deep Neural Network (DNN) Model

A Fine-Tuned Deep Neural Network (DNN) Model is a DNN model that is created with the use of a training data set and a pre-trained DNN model.

• Example(s):
  • OpenAI Codex Model, based on GPT-3.
  • … based on BERT.
  • …
• See: DNN Fine-Tuning Training, DNN Fine-Tuning Algorithm.

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References

2023

• chat
  • Q: What is a fine-tuned DNN model? how is it a type of transfer learning? provide some example models.
  • A: A fine-tuned DNN (Deep Neural Network) model is a type of neural network that has been trained on a specific task or set of tasks using pre-existing neural network architectures and weights. Fine-tuning involves taking a pre-trained model and adapting it to a new task by further training on a new dataset.

    Fine-tuning is a type of transfer learning, where knowledge learned from one task is transferred to another task. Transfer learning can significantly reduce the amount of data and training required to achieve good performance on the new task, especially when the new task has limited data available. Fine-tuning takes advantage of the learned features and knowledge from the pre-training phase, which can be transferred to the new task.

    Some examples of fine-tuned DNN models are:

    • VGG-16: VGG-16 is a popular pre-trained convolutional neural network (CNN) that was trained on the ImageNet dataset for image classification. By fine-tuning the VGG-16 model on a new dataset with a new set of classes, the model can be adapted to the new task, such as identifying different types of food items in images.
    • BERT: BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that was trained on a large corpus of text data. By fine-tuning the BERT model on a specific natural language processing (NLP) task, such as sentiment analysis or question answering, the model can be adapted to the new task.
    • Inception-v3: Inception-v3 is another pre-trained CNN model that was trained on the ImageNet dataset. By fine-tuning the Inception-v3 model on a new dataset with a new set of classes, the model can be adapted to the new task, such as identifying different types of animals in images.
  • In summary, fine-tuned DNN models are a type of transfer learning that adapt pre-trained models to new tasks by further training on a new dataset. They can save time and resources, and achieve better performance on new tasks with limited data.

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