Transfer learning is a methodology based on machine learning that exploits the memorization of the knowledge that's acquired during the resolution of a problem and the application of the same to different (but related) problems. The need to use transfer learning takes place when there is a limited supply of training data. This could be due to the fact that data is rare or expensive to collect or label, or inaccessible. With the growing presence of large amounts of data, the transfer learning option has become more frequently used.
Convolutional neural networks (CNNs) are essentially artificial neural networks (ANNs). In fact, just like the latter, CNNs are made up of neurons that are connected to one another by weighted branches (weight); the training parameters of the networks are once again the weight and the bias. In...
