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Nadig Blog

SVM Tutorial: The Algorithm and sklearn Implementation

July 13, 2017

Support Vector Machines are perhaps one of the most(if not the most) used classification algorithms. One of the prime advantages of SVM is that it works very good right out of the box. You can take the classifier in it’s generic form, without any explicit modifications, run it directly on your data and get good results.

Machine Learning: Understanding Decision Tree Learning

April 23, 2017

Decision Tree learning is one of the most widely used and practical methods for inductive inference. Decision Trees are easily understood by human and can be developed/used without much pain. In this post I will walk through the basics and the working of decision trees.

Parallel Computing in JavaScript : The Guide

March 29, 2017

Parallel programming in JavaScript is not as straight-forward as it is in languages such as C/C++ and Java or even Python due to its event based paradigm. JavaScript, while traditionally being used for performing small computations, is being increasingly used for heavy-wight applications. In this post I will focussing on parallel computation in JavaScript through Web Workers API.

Implementing K Means Clustering from Scratch - in Python

March 04, 2017

k-means clustering is a method of vector quantization, that can be used for cluster analysis in data mining. K Nearest Neighbours is one of the most commonly implemented Machine Learning clustering algorithms. In this post I will implement the K Means Clustering algorithm from scratch in Python.

Parallel Computing in C using OpenMP

February 25, 2017

OpenMP, short for "Open Multi-Processing", is an API that supports multi-platform shared memory multiprocessing programming in C, C++, and Fortran - on most platforms, processor architectures and operating systems. OpenMP consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior. In this post, we will be exploring OpenMP for C.

Implementing K Nearest Neighbours in Parallel from scratch

February 09, 2017

One of the prime drawbacks of k-NN is its efficiency. The brute force version of k-NN that was written previously is highly parallelizable. The computation of distances between the attributes is independent of one another. Also, the classification of incoming data points is independent of one another and can be easily accomplished in parallel. In this post I will implement the algorithm from scratch in Python in parallel.

Parallel Programming in Python with ease

January 25, 2017

Parallel programming in Python is a bit tricky as compared to languages such as C/C++ and Java. Python is restricted to a single OS thread; therefore, it cannot make use of the multiple cores and processors available on modern hardware. In this post I will use the `multiprocessing` library to easily create and coordinate multiple Python processes and run code in parallel.

Implementing K Nearest Neighbours from Scratch - in Python

January 13, 2017

k -Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. K Nearest Neighbours is one of the most commonly implemented Machine Learning classification algorithms. In this post I will implement the algorithm from scratch in Python.

First Post

December 31, 2016

Hey there! This is my first post. This post contains some random quotes.