Finetuned NNet Model
(Redirected from fine-tuned NNet)
Jump to navigation
Jump to search
A Finetuned NNet Model is a pretrained neural network model that has undergone a transfer learning NNet fine-tuning.
- Context:
- It can enhance the model's performance on specific tasks by continuing the training process on a task-specific dataset after the initial pretraining.
- It can leverage a large, generalized pre-trained model as a starting point, reducing the need for extensive data and computational resources typically required for training from scratch.
- It can range from simple architectures like Feedforward Neural Networks to complex ones like Transformers depending on the task and data.
- It can (often) result in significant improvements in accuracy and efficiency, particularly in domains like Image Recognition, Natural Language Processing, and Speech Recognition.
- It can exhibit variation in performance depending on factors such as the size and quality of the finetuning dataset, initial model architecture, and the specificities of the task.
- ...
- Example(s):
- a Finetuned BERT Model (BERT model) finetuned for Question Answering that can accurately respond to queries based on context provided in a passage.
- a Finetuned ResNet Model ([[ResNet model]) finetuned for Skin Cancer Detection capable of identifying malignancies in dermatological images with high precision.
- ...
- Counter-Example(s):
- Generic Neural Network Models that are only pretrained and not finetuned on specific task data.
- Untrained Neural Network Models that have not undergone any form of training.
- ...
- See: Transfer Learning, Domain-Specific Models, Adaptive Learning, Neural Network Architecture.