Soft Clustering with Gaussian Mixture Models (GMM)

GMM Theory¶ The Gaussian Mixture Model is a generative model that assumes that data are generated from multiple Gaussion distributions each with own Mean and variance. the Gaussian Mixture Models or Mixture of Gaussians models a convex combination of the various distributions. Unlike K-Means, with Gaussian Mixture Models we want to define a probability distribution on the data. In order to do that, we need to convert our clustering problem

Hard clustering with K-means

kmeans Theory¶ K-Means is the simplest and most fundemental clustering algorithm. Given: $x_1,x_2,…,x_n$, Where $x \in I\!R^d$ Output: Clusters $C_1,C_2,…,C_n$, Where $C_i \in \{1,2,..K\}$ Goal: Partition data into K clusters(groups) where each cluster has similar data. The goal is pretty clear. you have a bunch of data from which you may or may not know the generative distrubition. you want to learn the structure of the data in a such

Data does not lie.  Or does it?

Do you recall Beer and Nappies? … I never get tired of this legend, “diapers and beer”. Wal-Mart discovered through data mining that the sales of diapers and beer were correlated on Friday nights. It determined that the correlation was based on working men who had been asked to pick up diapers on their way home from work. wow. this is just a drop in the ocean of success stories about data. it is no more a

Predict  house prices with dense neural networks and tensorflow

In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where we aim to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture).

Multi Label Classification Of Texts With NLTK

MultiLabelClassification-with-nltk MULTI LABEL CLASSIFICATION WITH NLTK¶ In this tutorial, I will show you how to predict tags for a text. In this post, we will build a multi-label model that’s capable of detecting different types of toxicity in a large number of Wikipedia comments which have been labeled by human raters for toxic behavior. The types of toxicity are: toxic severe_toxic obscene threat insult identity_hate The data set used can

Neural Nets: Back propagation intuition

As I was preparing for a discussion with a group of friends on back propagation, I came across this video by professor Patrick Winston and I could not be happier. In only about 40 min, Prof Winston gives the most impressive intuition ever about what backpropagation is. Prof Winston starts from the definition of the problem and then follows through with the representation of the inputs. he uses a not normal

Discrete Probability Distributions

8 Discrete Probability Distributions¶ 8.2 Binomial Distribution¶ The following code plots the probability mass function (PMF) of $B_{p,n}$, the binomial distribution with parameters $p$ and $n$. It contains interactive sliders that you can use to vary $n$ over the interval $[0,30]$ and $p$ over the interval $[0, 1]$. In [1]: %matplotlib inline Let us now load the required code and analyze it part by part. In [2]: # %load plot_pmf.py import numpy


AI and assisted voice apps have become a new trend in almost every news media, social networks, professional meetups and forums. You probably have watched the movie “Her” a 2013 American romantic science-fiction drama film written, directed and produced by Spike Jonze. The film follows Theodore Twombly (Joaquin Phoenix), a man who develops a relationship with Samantha (Scarlett Johansson), an intelligent computer operating system personified through a female voice. The

Cost of Ignorance

You may find it hard to invest your time into learning new things. But be advised that bearing the consequences of ignorance is much harder.

Recommender systems are confining us into a digital imprisonment.

Gone are the times when you needed to be a handyman to get search engines returning what you are searching for. In today’s digital era you dare to watch a movie on Netflix and the next thing you will see is a rain of suggestions of movies that really match your taste, pouring on your screen. Youtube has moved from being a collection of videos to a personalized basket of