📌 Self-Supervised Learning Summary
Self-supervised learning is a type of machine learning where a system teaches itself by finding patterns in unlabelled data. Instead of relying on humans to label the data, the system creates its own tasks and learns from them. This approach allows computers to make use of large amounts of raw data, which are often easier to collect than labelled data.
🙋🏻♂️ Explain Self-Supervised Learning Simply
Imagine you are trying to solve a puzzle without anyone telling you what the final picture looks like. You use the pieces you have and clues from the puzzle itself to figure out how they fit together. Self-supervised learning works in a similar way, as the computer tries to learn from the information already present in the data, without needing extra instructions.
📅 How Can it be used?
Self-supervised learning can be used to train a speech recognition system using hours of unlabelled audio recordings.
🗺️ Real World Examples
A photo management app uses self-supervised learning to recognise objects and people in photos without needing users to label each image. The system learns by predicting missing parts of images or matching similar photos, improving its ability to sort and find pictures automatically.
A language translation tool uses self-supervised learning to better understand sentence structures by masking random words in large volumes of text and training itself to predict the missing words. This helps the tool understand language patterns without needing hand-labelled data.
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