  Since 2012, Deep learning has revolutionized Artificial Intelligence and has
achieved state-of-the-art outcomes in different domains, ranging from Image
Classification to Speech Generation. Though it has many potentials, our current
architectures come with the pre-requisite of large amounts of data. Few-Shot
Learning (also known as one-shot learning) is a sub-field of machine learning
that aims to create such models that can learn the desired objective with less
data, similar to how humans learn. In this paper, we have reviewed some of the
well-known deep learning-based approaches towards few-shot learning. We have
discussed the recent achievements, challenges, and possibilities of improvement
of few-shot learning based deep learning architectures. Our aim for this paper
is threefold: (i) Give a brief introduction to deep learning architectures for
few-shot learning with pointers to core references. (ii) Indicate how deep
learning has been applied to the low-data regime, from data preparation to
model training. and, (iii) Provide a starting point for people interested in
experimenting and perhaps contributing to the field of few-shot learning by
pointing out some useful resources and open-source code. Our code is available
at Github: https://github.com/shruti-jadon/Hands-on-One-Shot-Learning.
