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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).
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.
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.
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.
In deep learning, transfer learning is a technique whereby a neural network model is first trained on a problem similar to the problem that is being solved. One or more layers from the trained model are then used in a new model trained on the problem of interest.
Transfer learning is an optimization that allows rapid progress or improved performance when modeling the second task. Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned.
Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned.
10 Ways to Improve Transfer of Learning. Focus on the relevance of what you're learning. Take time to reflect and self-explain. Use a variety of learning media. Change things up as often as possible. Identify any gaps in your knowledge. Establish clear learning goals. Practice generalizing.
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