Lets play with Transfer Learning (TL)

An experiment and a poll for you.

Transfer Learning (TL) in Machine Learning is a technique where we use the learning of a model trained on a different dataset to then adapt and retrain on our own dataset. TL is one of the main ways by which one could make ML successful, specially in situations where the dataset that we have access to is limited.

How does Transfer Learning work?

Lets consider how our human body works. When we learn one skill such as riding a bicycle, we find it much easier to learn related skills which involve balancing, such as learning how to skate, or use roller blades, or ride a scooter.

Training a ML model works in similar ways. If we train a ML model from scratch, we start with random values for the parameters of the ML model, and then start a process known as stochastic gradient descent to reach optimal values. In the case of transfer learning, we would start with values acquired from a previous training exercise instead of picking completely random values. This often leads to a more optimal model…