Dropout is a widely used regularization technique for neural networks. Neural networks, especially deep neural networks, are flexible machine learning algorithms and hence prone to overfitting. In this tutorial, we'll explain what is dropout and how it works, including a sample TensorFlow implementation.

If you [have] a deep neural net and it's not overfitting, you should probably be using a bigger one and using dropout, ... - Geoffrey Hinton [2]

Dropout algorithm

Dropout is a regularization technique where during each iteration of gradient descent, we drop a set of neurons selected at random. By drop, what we mean is that we essentially act as if they do not exist.

Introduction

Statistics is a very broad branch of mathematics that deals with everything related to data, from collection and organization of data to its analysis, interpretation and presentation. With the ever increasing amount of data, statistics has become an indispensable tool in every field where one has to work with data.

When the amount of data we are dealing with is fairly small, then it might be possible to talk about all the data items individually. However, when we are dealing with large quantities of data, which is almost always the case in real world situations, we need to have some characteristic values that can represent the data.

In this tutorial, we'll introduce such measures first for a single variable. For example, say the weight of students in a particular school. These measures will include measures of central tendency and measures of dispersion. Then, we'll look at measures for und...