Issue #35 Training Edwin Dong
Self-Supervised Learning, Simply Explained
Machine learning (ML) is divided into 3 main categories: supervised learning, unsupervised learning, and reinforcement learning.
As a quick refresh, supervised learning involves training a model on labeled data, learning a mapping from input to output. An example of a supervised task is classification.
Unsupervised learning trains the model on unlabeled data, learning an underlying structure or pattern without the guidance of labels, such as clustering data into groups.
Reinforcement learning is a machine learning algorithm that allows an agent to learn environment behavior by trial and error using feedback and reward from its own actions and experiences.
In addition to these three categories, in many of our issues, we discuss deep learning, a field of machine learning that uses neural networks to learn from input data. Tasks solved in deep learning are usually supervised—such as image classification, speech recognition, and translation.
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