In this video, we will cover Transfer Learning technique. Transfer learning is a machine learning technique in which an AI that has been trained to perform a specific task is being reused (repurposed) as a starting point for another similar task.

Transfer learning is widely used since starting from a pre-trained AI model can dramatically reduce the computational time required if training is performed from scratch.

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, Handbook of Research on Machine Learning Applications, 2009.

In transfer learning, a base (reference) Artificial Neural Network on a base dataset and function is being trained. Then, this trained network weights are then repurposed in a second ANN to be trained on a new dataset and function.

Transfer learning works great if the features are general, such that trained weights can effectively repurposed. Intelligence is being transferred from the base network to the newly target network.

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