Pseudo Labelling - A Guide To Semi-Supervised Learning. In this article, I’ll be discussing how to generate pseudo labels using the semi-supervised learning technique.
There are 3 kinds of machine learning approaches- Supervised, Unsupervised, and Reinforcement Learning techniques. Supervised learning as we know is where data and labels are present. Unsupervised Learning is where only data and no labels are present. Reinforcement learning is where the agents learn from the actions taken to generate rewards.
Imagine a situation where for training there is less number of labelled data and more unlabelled data. A new technique called Semi-Supervised Learning(SSL) which is a mixture of both supervised and unsupervised learning. As the name suggests, semi-supervised learning has a set of training data which is labelled and another set of training data, which is unlabelled. We can think of this situation as when Google photos or Facebook identify people in the picture by their faces(data) and generate a suggested name(label) based on the previously stored images of that person.
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AI, Machine learning, as its title defines, is involved as a process to make the machine operate a task automatically to know more join CETPA
You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives. It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.