🚙Transfer Learning

Applying a knowledge to separate tasks

In short: Learning from one task and applying knowledge to separate tasks 🛰🚙

❓ What is Transfer Learning?

• 🕵️‍♀️ Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task.

• 🌟 In addition, it is an optimization method that allows rapid progress or improved performance when modeling the second task.

• 🤸‍♀️ Transfer learning only works in deep learning if the model features learned from the first task are general.

Long story short: Rather than training a neural network from scratch we can instead download an open-source model that someone else has already trained on a huge dataset maybe for weeks and use these parameters as a starting point to train our model just a little bit more with the smaller dataset that we have ✨

💫 Traditional ML vs Transfer Learning

🙄 Problem

Layers in a neural network can sometimes end up having similar weights and possible impact each other leading to over-fitting. With a big complex model it's a risk. So if you can imagine the dense layers can look a little bit like this.

We can drop out some neurons that has similar weights with neighbors, so that overfitting is being removed.

🔃 Comparison

🤸‍♀️ An NN before and after dropout

✨ Accuracy before and after dropout

🤔 When is it practical?

It is practical when we have a lot of data for problem that we are transferring from and usually relatively less data for the problem we are transferring to 🕵️‍

More accurately:

For task A to task B, it is sensible to do transfer learning from A to B when:

• 🚩 Task A and task B have the same output x

• ⭐ We have a lot more data for task A than task B

• 🔎 Low level features from task A could be helpful for learning task B

🧐 References

• More about transfer learning in Tensorflow
• Understanding Dropout