Transfer Learning

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Who is the predator? Who is the prey? Have you ever seen a live dinosaur? Not ever seeing a live dinosaur and knowing the predator/prey answers is 'transfer' learning.


Transfer learning (aka Knowledge Transfer, Learning to Learn) is a machine learning technique where a model trained on one task is re-purposed on a second related task. Transfer learning is an optimization that allows rapid progress or improved performance when modeling the second task. A Gentle Introduction to Transfer Learning for Deep Learning | Jason Brownlee - Machine Learning Mastery

Transfer learning is a type of learning where a model is first trained on one task, then some or all of the model is used as the starting point for a related task. It is a useful approach on problems where there is a task related to the main task of interest and the related task has a large amount of data. It is different from multi-task learning as the tasks are learned sequentially in transfer learning, whereas multi-task learning seeks good performance on all considered tasks by a single model at the same time in parallel.An example is image classification, where a predictive model, such as an artificial neural network, can be trained on a large corpus of general images, and the weights of the model can be used as a starting point when training on a smaller more specific dataset, such as dogs and cats. The features already learned by the model on the broader task, such as extracting lines and patterns, will be helpful on the new related task.As noted, transfer learning is particularly useful with models that are incrementally trained and an existing model can be used as a starting point for continued training, such as deep learning networks. 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery