Introduction
Imagine if we didn’t transfer the learnings from one generation to another then the world couldn’t have made significant progress. Thus it’s a very common practice which has always brought improvement. Similarly, Deep Learning also works on the same principle and it tries to mimic the human brain. It uses the concept of Transfer Learning due to which we get such overwhelming results generally. Let’s explore more about Transfer Learning.
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What is Transfer Learning?
Transfer learning is an important part of Deep Learning. It refers to re-usage of the pre-computed knowledge to improve the predictions. It aims to transfer knowledge to closely linked problems. Let’s consider the image classification example. What we do is simply feed the image dataset to a CNN which uses several convolution layers to process the images and extract features and then classify the images.

In forming each convolution layer we transfer the previous layer features and process them to form the new layer and finally it’s fed to a Neural Network. Then the internal computation of Neural Networks also use transfer learning where weights are transferred from one layer to another to compute and update the weights.
Traditional vs Transfer Learning
The traditional learning mechanisms in machine learning included isolated learning and training where prior knowledge was not taken into account and is not retained.

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In case of Transfer Learning, the prior computed knowledge like features, weights, etc, can be transferred from one layer to another which works well for even smaller amounts of datasets.
Key Steps to Transfer learning
There are three important steps to perform transfer learning and they are as follows:
What to Transfer
This is the very first step and most important question to be answered while performing transfer learning. In this step all the important information and knowledge that can be transferred has to be identified by looking at the common information required by source and target.
When to Transfer
It’s important to look at the effect of the knowledge being transferred. It should have a positive impact on the results. Thus it’s important to recognize at what point it’s necessary to transfer knowledge.
How to Transfer
The third and the last step in the transfer learning process is to figure out how the transfer of knowledge will occur. This can be figured out by usage of existing algorithms or modifying them for our purpose.