If real world data-sets contain numeric, texts, alpha-numeric, time-stamps, and various other unstructured data types, then how does one store, retrieve and easily manipulate these multidimensional data-sets? The answer is a data science library like Pandas! Pandas is a powerful data analysis toolkit with high-performance and easy-to-use data structures. Unlike Excel and SQL, it carries a host of useful tools, methods and other functionalities that set it apart when it comes to row-wise and column-wise data manipulations. We will visit these functionalities in this tutorial.

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A Support Vector Machine (SVM) is a classification algorithm, typically used for binary classification. An SVM with gaussian kernel has been consistently shown to be one of the best machine learning models, achieving the highest accuracy in a large variety of datasets, specially datasets which do not involve images or audio.

Understanding the various hyper-parameters of an SVM are central to achieving high accuracy. In this tutorial, we'll learn what an SVM is, and what is the purpose of its various hyper-parameters.

Max-margin classifier

Given a binary classification dataset, an SVM aims to find a separating hyperplane between positive and negative instances. For example, in the figure below, each of the blue lines is a plane which separates the positive and negative data points. All blue points lie on one side, and all red points lie on the other rise. New unseen samples are categorized based on the side of the hyperplane in which they fall.

This post is an intuitive introduction to computational graphs and backpropagation - both important and core concepts in deep learning for training neural networks.

Doesn't Tensorflow perform backprop automatically?

Deep learning frameworks such as Tensorflow and Torch perform backpropagation and gradient descent automatically. So, why do we need to understand it?

The answer is, because although the calculations are abstracted out by libraries, gradient descent on neural networks is susceptible to various issues such as vanishing gradients, dead neurons, etc. When we are trying to train a neural network and we face these problems, we will not be able to resolve it unless we understand whats going on behind the scenes.

Why computational graphs?

Language Syntax and Structure

Syntax and structure of a natural language such as English are tied with a set of specific rules, conventions, and principles which dictate how words are combined into phrases, phrases get combined into clauses, and clauses get combined into sentences. All these constituents exist together in any sentence and are related to each other in a hierarchical structure.

Let’s consider a very basic example of language structure which explains a specific example in the light of subject and predicate relationship. Consider a simple sentence:

Harry is playing football

This sentence is talking about two subjects - Harry and football. To find the subject of the sentence, it is easier to fir...