We humans have an amazing ability to learn, and then we take what we have learned and apply the knowledge to different types of tasks. The more closely related the new task is to tasks we already know, the easier it is for us to solve the new task. Basically, we never really have to start from scratch when learning something new.
However, neural networks aren't afforded this same luxury; they need to be trained from scratch for each individual task we want to apply them to. As we have seen in previous chapters, neural networks are very good at learning how to do one thing very well, and because they only learn what lies within an interpolation of the distribution they have been trained to recognize, they are unable to generalize their knowledge to deal with tasks beyond what they have encountered in the training dataset.
In addition, deep neural networks...