Transfer learning is the reuse of a pre-trained model for a new problem, it is very popular nowadays in deep learning
because it can train deep neural networks with relatively little data, and it is very useful in data science because of most real
problems., you don't have millions of data points marked to train these complex models. Let's take a look at what transfer
learning is, how it works, why and when to use it. Includes several resources for models that have been previously trained in
learning transfers for example, when you train the classifier to predict whether an image contains food, you can use the
knowledge gained during training to recognize drinks, for example, if you trained a simple classifier to predict, if the image
includes a backpack, you can use the knowledge gained by the model during training
