Sajil C. K.

I am an inquisitive person with interests falling at the intersection of mathematics, programming, and research. I like to understand concepts to the core by building things from scratch.

Active Learning with Uncertainty Sampling from Scratch

Introduction Availability of mass quantities of digital data and feasible computing power brought to the creation of learning algorithms. These learning algorithms have been benchmarked to perform specialized tasks such as classification, object detection, image segmentation, etc. The key assumption here is on data that is supposed to be free from human biases. Active learning …

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Review of RL Tutorial from Center for Brains Minds and Machines

The Center for Brains, Minds and Machines (CBMM) is a premier institute and NSF Science and Technology Center dedicated to the study of intelligence. The website and Youtube channel of the institute contains a good number of tutorials and educational materials that help anyone interested in topics related to neuroscience, machine intelligence, etc. This tutorial …

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Bivariate Gaussian Distribution

Hacking the Bivariate Gaussian Distribution

In one of our earlier posts, we have seen how we can visually relate the parts of the one-dimensional Gaussian distribution equation. In this post, we will follow the same strategy to understand the terms that comes up with a Multivariable Gaussian distribution. We will focus on the Bivariate Gaussian distribution as distributions of higher-order …

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Normal distribution visual explanation

Gaussian Distribution Explained Visually

Gaussian distribution appears in various parts of science and engineering. Apart from a distribution often appear in nature, it has got important properties such as its relation to Central Limit Theorem (CLT). The figure above shows one-dimensional Gaussian distributions of various mean and variance values. Libraries like NumPy provide functions that can return Gaussian distribution …

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TwoStateMDP

Coding a Simple Markov Decision Process

This is a quick tutorial on how to implement a two-state Markov Decision Process (MDP). It has only two states: state 1 and state 2. Applications MDPs often appear in machine learning and reinforcement learning. At any stage, the agent can take any of the two actions. The actions are to stay or switch. The …

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