22 lines
1.2 KiB
Markdown
22 lines
1.2 KiB
Markdown
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title: One-Shot Learning
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---
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# One-Shot Learning
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Humans learn new concepts with very little need for repetition – e.g. a child can generalize the concept
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of a “monkey” from a single picture in a book, yet our best deep learning systems need hundreds or
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thousands of examples to grasp any object even upto a point of decent accuracy. This motivates the setting we are interested in: “one-shot” learning, which
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consists of learning a class from a single (or very few) labelled example.
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There are various approaches to One-Shot learning such as [similarity functions](https://www.coursera.org/lecture/convolutional-neural-networks/one-shot-learning-gjckG),
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[Bayes' probability theorem](https://www.youtube.com/watch?v=FIjy3lV_KJU), DeepMind has come up with it's own version of Neural Networks using the One-Shot learning approach!
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### More information:
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* [Siraj Raval on YouTube](https://www.youtube.com/watch?v=FIjy3lV_KJU&feature=youtu.be)
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* [Andrew Ng (Deeplearning.ai)](https://www.coursera.org/lecture/convolutional-neural-networks/one-shot-learning-gjckG)
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* [Scholarly article](http://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.pdf)
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* [Wikipedia](https://en.wikipedia.org/wiki/One-shot_learning)
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