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Don’t clench too hard on your vision, ideas, features, or improvements on your product.

As a kid, we always dreaded sharing our toys with others. It was ours and ours alone. No one at times not even our siblings or parents had right over it. We had a sense of attachment to our toys, something very personal that even despite our parent's request, we wouldn’t share it with our guests less so with someone of our age.

As we grew older, we outgrew our toys (though I’m still scared my mom would throw away my Pokemon Jenga collection). We started…

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Understanding the distribution of a variable(s) is one of the first and foremost tasks done while exploring a dataset. One way to test the distribution of continuous variables graphically is via a Q-Q plot. Personally, these plots come in handy in the case of parametric tests as they insist on the assumption of normality even though they can be used for any underlying distribution.

What is a Q-Q plot?

Quantile-Quantile plot or Q-Q plot is a scatter plot created by plotting 2 different quantiles against each other. The first quantile is that of the variable you are testing the hypothesis for and the second one…

The assumption of I.I.D is central to almost all machine learning algorithms and an explicit assumption in most statistical inferences

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Let’s try to understand what it is and why it is so important in machine learning & statistics

Independent and Identical distribution is when a distribution is well, both independent and identically distributed. Let’s try to breakdown this further.

What makes variables Independent?

By independent, we mean the samples taken from the individual random variables are independent of each other. Samples drawn from random variables do not contain any internal dependency amongst themselves

Lets look at simple examples of dependent and independent distributions:

Independent Event

  • Imagine a coin toss. If you get heads on the first trial, the probability of getting heads or tails in the…

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Eigenvalues & Eigenvectors are central (but not limited) to many of the well-known machine learning algorithms. Algorithms like SVD, PCA, spectral clustering, image segmentation, 3D reconstruction use eigenvalues & eigenvectors as their core logic to function.

Despite their usefulness, their criticality in many well-known applications & being part of linear algebra 101, they are often considered difficult to understand & even feared upon by budding statisticians/data scientists primarily due to how non-intuitive the concept can be.

If you’re hearing the word eigenvalue & eigenvector for the first time, the story will help to understand the core concept behind eigenvectors &…

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What is a black hole ?

Black hole is a place where the pull of gravity is so intense that even light cannot get out. This is as a result of matter been squeezed into an infinitesimal space due to death of a star or due to direct collapse of massive dust clouds (causing supermassive like TON 618). They have bright accretion disk of superheated particles which encompasses the event horizon & the singularity of the blackhole

Event horizon is the nearest point in space around the blackhole where the universal constant, speed of light in vacuum has a chance to escape the clutches of black…

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In the previous article, we discussed discrete text representation for feeding text to machine learning or artificial intelligence algorithms. We learnt few techniques, its workings, advantages & disadvantages of each of them. We also discussed on the drawbacks of discrete text representations & how it ignores the positioning of words & doesn’t try to explain word similarities or meanings inherently.

In this article, we will look at distributed text representation of text & how it solves some of the drawbacks of discrete representation.

Distributed Text Representation:

Distributed text representation is when the representation of a word is not independent or mutually exclusive of…

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Computers are brilliant when dealing with numbers. They are faster than humans in calculations & decoding patterns by many orders of magnitude. But what if the data is not numerical? What if its language? What happens when the data is in characters, words & sentences? How do we make computers process our language? How does Alexa, Google Home & many other smart assistants understand & reply to our speech? If you are looking to get answers to some of these questions, this article will be a stepping stone for you in the right direction.

Natural Language Processing is a sub-field…

Sundaresh Chandran

Data Scientist @Royal Dutch Shell | Deep Learning | NLP | TensorFlow 2.0 | Python | Astrophysics ❤

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