{"id":10850,"date":"2023-10-04T12:12:23","date_gmt":"2023-10-04T06:42:23","guid":{"rendered":"https:\/\/dataaspirant.com\/?p=10850"},"modified":"2023-10-04T12:12:27","modified_gmt":"2023-10-04T06:42:27","slug":"one-shot-learning","status":"publish","type":"post","link":"https:\/\/dataaspirant.com\/one-shot-learning\/","title":{"rendered":"One-Shot Learning: Learn How to Build Models with Limited Labeled Data"},"content":{"rendered":"
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\"One-Shot

One of the biggest challenges in machine learning<\/strong><\/a> is the need for large amounts of labeled data to train models effectively. However, in many real-world scenarios, obtaining labeled data can be difficult, time-consuming, or expensive. This is where one-shot learning<\/strong> comes in – a technique that enables machines to learn and generalize from a single example.<\/p>\n

One-shot learning is a type of machine learning that falls under the category of few-shot learning<\/strong>. This means that instead of having access to a large dataset with many examples for each class, one-shot learning models are trained on a small set of labeled<\/strong> examples per class.<\/p>\n

The goal of one-shot learning is to teach the machine to learn from a few examples so that it can accurately classify new, unseen examples with a high degree of accuracy. This is a critical capability in situations where acquiring large amounts of labeled data is impractical or impossible.<\/p>\n

In this article, we will delve into the world of one-shot learning and provide a comprehensive guide to mastering this technique. We will cover topics such as<\/p>\n