---
title: One-Shot Learning
---

# One-Shot Learning

Humans learn new concepts with very little need for repetition – e.g. a child can generalize the concept
of a “monkey” from a single picture in a book, yet our best deep learning systems need hundreds or
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
consists of learning a class from a single (or very few) labelled example.

There are various approaches to One-Shot learning such as [similarity functions](https://www.coursera.org/lecture/convolutional-neural-networks/one-shot-learning-gjckG), 
[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!



### More information:
* [Siraj Raval on YouTube](https://www.youtube.com/watch?v=FIjy3lV_KJU&feature=youtu.be)
* [Andrew Ng (Deeplearning.ai)](https://www.coursera.org/lecture/convolutional-neural-networks/one-shot-learning-gjckG)
* [Scholarly article](http://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.pdf)
* [Wikipedia](https://en.wikipedia.org/wiki/One-shot_learning)