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Support Vector Machine (SVM) Theory
In this tutorial, we'll describe the mathematics behind Support Vector Machines. This tutorial is going to be much more math heavy as compared to other tutorials. If you're mostly interested in applying SVMs to solve problems, then our first tutorial on SVMs is sufficient. However, if you would like to understand the mathematical basis of Support Vector Machines, then you'll find this tutorial interesting.
In this tutorial, we will focus on the hard-margin SVM and soft-margin SVM. However, we will not be considering kernels or the hyper-parameter \gamma (gamma).
In SVM, the decision function for predicting data points x corresponds to the equation of an hyperplane:
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 
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.
Statistics: Central Tendency metrics, Dispersion and Correlation
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...