Updated GH landing page

Author: PyDataBlog

README.md

@@ -9,6 +9,16 @@ ________________________________________________________________________________
 **Authors:** [Bernard Brenyah](https://www.linkedin.com/in/bbrenyah/) & [Andrey Oskin](https://www.linkedin.com/in/andrej-oskin-b2b03959/)
 _________________________________________________________________________________________________________
 
+<div align="center">
+    <b>Classic & Contemporary Variants Of K-Means In Sonic Mode<b>
+</div>
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/2630519/80216880-70b60b00-8647-11ea-913b-7977ef1c156c.gif">
+</p>
+
+_________________________________________________________________________________________________________
+
 ## Table Of Content
 
 1. [Documentation](#Documentation)
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 ### Features
 
-- Lightening fast implementation of Kmeans clustering algorithm even on a single thread in native Julia.
+- Lightening fast implementation of K-Means clustering algorithm even on a single thread in native Julia.
 - Support for multi-theading implementation of K-Means clustering algorithm.
 - Kmeans++ initialization for faster and better convergence.
 - Implementation of all the variants of the K-Means algorithm.

docs/src/index.md

@@ -170,6 +170,9 @@ c = [ParallelKMeans.kmeans(X, i; tol=1e-6, max_iters=300, verbose=false).totalco
 Currently, this package is benchmarked against similar implementations in both Python and Julia. All reproducible benchmarks can be found in [ParallelKMeans/extras](https://github.com/PyDataBlog/ParallelKMeans.jl/tree/master/extras) directory. More tests in various languages are planned beyond the initial release version (`0.1.0`).
 
 *Note*: All benchmark tests are made on the same computer to help eliminate any bias.
+|PC Name                      |CPU                       |Ram               |
+|:---------------------------:|:------------------------:|:----------------:|
+|iMac (Retina 5K 27-inch 2019)|3 GHz 6-Core Intel Core i5|8 GB 2667 MHz DDR4|
 
 Currently, the benchmark speed tests are based on the search for optimal number of clusters using the [Elbow Method](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) since this is a practical use case for most practioners employing the K-Means algorithm.