Transfer learning is one of the machine learning techniques which allows to repurpose a trained model trained for one task on a second related task. When doing so, it offers rapid progress and improved performance. It concerns problems such as concept drift and multitask learning. Transfer learning is a popular aspect of deep learning. However, it only works if the model trained for the first task has general features.

Using Transfer Learning

In order to use transfer learning, the following are the two common approaches:

• Developed Model Approach
• Pre-trained Model Approach

Developed Model Approach

This approach follows some simple steps to execute transfer learning. These are:

1. Selecting the task source: You must keep in mind that only a related predictive modeling problem can be used. As it comes with an abundance of data, there has to be a relationship between input and output data. The concepts used to map from input data to output data can also be related.
2. Developing a source model: Now, develop a skillful model that is better than a naïve model. It shows that you have performed some feature learning.
3. Reusing the model: The model must fit on one task. Then, it can act as a starting point for a second related task. You can use the complete model or some parts of the model, as required.
4. Tuning the model: This is an optional step. You can further refine the model to adapt well to the new task.

Pre-Trained Model Approach

Following are the steps to follow to execute a pre-trained model approach:

1. Selecting a source model: From all the available models, choose a source model. Many models included in the pool of models offered from research institutions come with large datasets, from which to choose from.
2. Reusing the model: Then, you can reuse the model as a starting point for another model. You can use the complete model or some parts of the model, as required.
3. Tuning the model: This is an optional step. You can further refine the model to adapt well to the new task.

When is Transfer Learning Used?

Transfer learning saves a lot of time and provides better performance. Only after the model is developed and executed can the benefit of using transfer learning becomes obvious. When using transfer learning, following are the three main benefits to look for:

• Higher initial skill: Even before refining the model, the initial skill is higher on the source model comparative to the otherwise source used.
• Higher slope: Skill improves at a steeper rate than it could be otherwise observed.
• Better asymptote: Asymptote is the converged skill which is obviously better when using transfer learning than it otherwise would have been.

You will be able to observe these benefits when the application has been successfully developed. In case you do not have much data, transfer learning helps develop skillful models that you could not have developed if you didn’t know transfer learning. With deep learning, you can use image data as well as language data to learn transfer learning.

Where to Learn?

In order to learn transfer learning, there are a number of different online platforms, each better than the other. Codeacademy and Pluralsight are two good examples. However, when it comes to choosing one between the two, you can find out which is better. While Codeacademy has excellent material to offer, it might be difficult for beginners to grasp. That’s where Pluralsight wins. It also is a better option as far as the variety of material and price is concerned.