Tech Term Decoded: Transfer Learning

Definition

In transfer learning in AI, the knowledge of an already trained machine learning model is been put to use in a different but related task. The idea behind the concept is to use the knowledge a model has gained from a task with a lot of available labeled training data, in a new task that doesn’t have much data. Instead of starting the learning process from scratch, we start with patterns learned from solving a related task [1]. For example, imagine a language translation app that initially learns English. When it wants to learn Yoruba, instead of starting from scratch, it uses its existing English language understanding as a foundation.

Transfer learning in AI

 Transfer learning process in AI [2].

Origin

The roots of transfer learning in AI can be traced back to the early 1990s, where researchers began exploring ways to leverage knowledge from one domain to solve related problems in another, evolving into sophisticated methodologies as data volumes expanded.

Context and Usage

Transfer learning is an emerging technology that is being applied in various fields of machine learning. It is already been put into use in industries such as autonomous driving, gaming industry, healthcare sector, emails, and ecommerce industry [3].

Why it Matters

At its heart, transfer learning is essentially a design approach to enhancing efficiency. Instead of training models from scratch, which requires a vast amount of resources, money, and time in the case of cutting-edge generative AI models, transfer learning allows models to learn more quickly and effectively in new tasks by leveraging the knowledge acquired in the past.

Transfer learning shines when little data is available to train a model in a second task. By using the knowledge of a pre-trained model, transfer learning can help prevent overfitting and increase overall accuracy [4].

In Practice

A real-life case study of Transfer Learning in AI been practiced can be seen in the case of Google Translate, which uses transfer learning to improve translations across multiple languages, including Nigerian languages like Yoruba, Igbo, and Hausa, by leveraging knowledge from one language to another. This approach saves time, reduces the amount of training data needed, and makes language learning for AI much more efficient.

See Also 

Related Learning Approaches:

References

  1. Donges, N. (n.d). What Is Transfer Learning? Exploring the Popular Deep Learning Approach.
  2. Geeksforgeeks. (2025). What is Transfer Learning?
  3. Kanade, V. (2025). What Is Transfer Learning? Definition, Methods, and Applications.
  4. Lunar, J., C. (2024). What is Transfer Learning in AI? An Introductory Guide with Examples. 

Kelechi Egegbara

Kelechi Egegbara is a Computer Science lecturer with over 12 years of experience, an award winning Academic Adviser, Member of Computer Professionals of Nigeria and the founder of Kelegan.com. With a background in tech education, he has dedicated the later years of his career to making technology education accessible to everyone by publishing papers that explores how emerging technologies transform various sectors like education, healthcare, economy, agriculture, governance, environment, photography, etc. Beyond tech, he is passionate about documentaries, sports, and storytelling - interests that help him create engaging technical content. You can connect with him at kegegbara@fpno.edu.ng to explore the exciting world of technology together.

Post a Comment

Previous Post Next Post