Transfer Learning in Deep Learning #transferlearning #deeplearning #imageclassification #keras

Transfer learning uses a pre-trained model which is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as is or use transfer learning to customize this model to a given task. The intuition behind transfer learning for image classification is that if a model is trained on a large and general enough dataset, this model will effectively serve as a generic model of the visual world. You can then take advantage of these learned feature maps without having to start from scratch by training a large model on a large dataset. In this video, you will learn how to classify images of cats and dogs by using transfer learning from a pre-trained network.