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What is fine-tuning in deep learning?

From Deep Learning Course Wiki. Fine-tuning is a process to take a network model that has already been trained for a given task, and make it perform a second similar task.

How do you fine tune a deep learning model?

The common practice is to truncate the last layer (soft max layer) of the pre-trained network and replace it with our new soft max layer that are relevant to our own problem. Use a smaller learning rate to train the network.

What is the difference between transfer learning and fine-tuning of the Deep learning model?

Transfer learning is when a model developed for one task is reused for a model on a second task. Fine-tuning is one approach to transfer learning, and it is very popular in computer vision and NLP. The most common example given is when a model is trained on Imagine is fine-tuned on a second task.

How can we improve performance of deep learning model?

Analyze errors (bad predictions) in the validation dataset. Monitor the activations. Monitor the percentage of dead nodes. Apply gradient clipping (in particular NLP) to control exploding gradients. Shuffle dataset (manually or programmatically).

What is model tuning?

Tuning Machine Learning Models. Tuning is the process of maximizing a model's performance without overfitting or creating too high of a variance. Hyperparameters differ from other model parameters in that they are not learned by the model automatically through training methods.

How do you fine tune a network?

The common practice is to truncate the last layer (soft max layer) of the pre-trained network and replace it with our new soft max layer that are relevant to our own problem. Use a smaller learning rate to train the network.

What is fine-tuning neural network?

Fine-tuning is a process to take a network model that has already been trained for a given task, and make it perform a second similar task.

What is fine-tuning in transfer learning?

Fine-tuning is one approach to transfer learning. In Transfer Learning or Domain Adaptation we train the model with a dataset, and after we train the same model with another dataset that has a different distribution of classes, or even with other classes than in the training dataset).

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