Transfer learning is a process used in deep learning technology, where a previously learned model is used to train a new model. This helps in reducing the data load on the new model while helping it improve its learning quickly. This technique is very well-utilized in data science, where the requirement of teaching systems large data sets with millions of data points is necessary. So, now that you have a basic understanding of transfer learning let's dive deeper into this topic and learn more about transfer learning and how it works.

What Is Transfer Learning: Example

Transfer learning, as discussed, is a process of teaching a new machine a solution to a previously solved problem by another device. Some examples to help you comprehend this process are:

• You have a machine learning model that knows how to identify what cuisine you are eating from the picture of the food; you can use that to train a model you are currently making to identify any beverage in the picture.

• You have a machine learning model that can identify if the person or the image has any sunglasses or not. You can use that model to train a new model whose goal is to find out whether the picture has any backpacks.

As you can comprehend from these examples, the two data sets are closely related but not quite the same. The relevant data from the previous model can help this model cross out a distinct possibility from the results. This allows the model to narrow down the options of the answer and helps it find the solution quicker. The cut down in processing time for the model to reach its response is the goal of transfer learning.

Advantages Of Using Transfer Learning

Improved Training Speed

When new models are being developed, it can take ungodly time for systems to create solutions where transfer learning is unavailable. This excellent method drastically cuts down on the new system training period, allowing new designs to be developed faster and deployed quicker. This can be a game-changing difference, especially in the high-speed world of AI and ML.

Reducing Data Load On New Systems

Using transfer learning allows new systems to reduce the data load during deployment. When the data load is low on new designs, it reduces a system's infrastructural and energy requirements, which can be crucial in building sustainable strategies for the future.

Improved Neural Network Performance

Once you are using tried and tested data models to train a new system, the new model's performance can easily outdo itself. This is because the new model can use and improve the inferences pulled from the learning model. This helps in better decision-making ability and quicker response times. This improvement in the performance of the neural networks would not be possible if transfer learning is not used in the development and deployment of the new model.

Conclusion

Transfer learning is just one of the key concepts behind the exponential growth of AI and ML today. The best and perhaps the most exciting part about this technology is that it uses AI and ML to develop the same further. This is a significant step forward, and if you are interested in this field, you can also learn about transfer learning and much more. How? You can enroll in a dedicated degree in machine learning and artificial intelligence. And if you want to know more about it, you can visit JIET Jodhpur’s official site.