Fine-tuning means tweaking our neural network in such a way that it becomes more relevant to the task at hand. We can freeze some of the initial layers of the network so that we don't lose information stored in those layers. The information stored there is generic and of useful. However, if we can freeze those layers while our classifier is learning and then unfreeze them, we can tweak them a little so that they fit even better to the problem at hand. Suppose we have a pre-trained network that identifies animals. However, if we want to identify specific animals, such as dogs and cats, then we can tweak the layers a little bit so that they can learn how dogs and cats look. This is like using the whole pre-trained network and then adding a new layer that consists of images of dogs and cats. We will be doing a similar activity by using a pre-built network and adding a classifier on top of it, which will be trained on pictures of dogs and cats.
There is a three...
