# Implementation

## Approximating Sine Function in TinyML

Introduction TinyML is the field which involves deployment of machine learning models into resource constrained devices such as micro-controllers. Such devices called edge devices often have few Kilobytes of RAM and flash memory but consumes power in milli-watts range. This feature makes the technology anĀ  ideal choice for remote sensing applications, weather stations, tiny gadgets …

## User Interface Control Using Hand Gesture Recognition

User Interface is key for humans to interact with computers. The traditional way is to use keyboard and mouse to browse websites, use apps, or play video games. When it comes to specific applications such as infographics display in a mall or airport often the amount of content will be limitted and simple. In such …

## Writing Python Wrapper for Bioinformatics Tools

The use of computational tools for data preprocessing and analysis is daily activity for any bioinformatician. Having a interdisciplinary field, a plethora of tools exist in the domain, written by developers from various fields and languages. Many times there can be multiple candidate tools exists to accomplish any given task. Often it is the decision …

## Entropy Simplified

The field of information theory defines entropy as a measure of average information a random variable takes. As we know the random variable is one whose possible outcome states are not deterministic. We cannot predict its behavior exactly in an analytical form. In such cases, we can only look at the patterns of its outcome …

## Active Learning with Uncertainty Sampling from Scratch

This article is a tutorial on the algorithm called active Learning with uncertainty Sampling. 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 …

## 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 …

## Visualizing MinMax Scaling

This article explains the minmax scaling operation using visual examples. Normalization of vectors, an array of values, signals is often used as a preprocessing step before many algorithms. For example, in machine learning, some types of algorithms are prone to different inherent scales of features. In such situations normalization is done to give the same …

## 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 …

## Coding a Simple Markov Decision Process

A Markov Decision Process (MDP) is a mathematical framework used to model decision-making situations where the outcome of a decision depends on both the current state of the system and the actions taken by the decision maker. In an MDP, the decision maker is represented as an agent, and the system is represented as a …

## An Intuitive Explanation of Naive Bayes Classifier

Introduction In this post, let’s take a look at the intuition behind Naive Bayes Classifier used in machine learning. Naive Bayes classifier is one of the basic algorithms often encountered in machine learning applications. If linear regression was based on concepts from linear algebra and calculus, naive Bayes classifier mostly backed up by probability theory. …