This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images. This is Part 2 of How to use Deep Learning when you have Limited Data. Checkout Part 1 here.

…Why is there a need for a large amount of data?

When you train a machine learning model, what you’re really doing is tuning its parameters such that it can map a particular input (say, an image) to some output (a label). Our optimization goal is to chase that sweet spot where our model’s loss is low, which happens when your parameters are tuned in the right way.

Naturally, if you have a lot of parameters, you would need to show your machine learning model a proportional amount of examples, to get good performance. Also, the number of parameters you need is proportional to the complexity of the task your model has to perform. Read More