#012 TF Transfer Learning in TensorFlow 2.0 Master Data Science

Highlights: In this post we are going to show how to build a computer vision model without building it from scratch. The idea behind transfer learning is that a neural network that has been trained on a large dataset can apply its knowledge to a dataset that it has never seen before. That is, why it's called a transfer learning; we transfer the learning of an existing model to a new dataset. Previously we have explored how to improve the models performance using a data augmentation. The question now is, "what if we don't have enough data to train our network from scratch?".