Conceptual

Density Functional Theory

A Lighter Perspective to Density Functional Theory

The diverse properties of materials upon which material science researchers possess enormous enthusiasm are categorized into magnetic, transport, optical and superconducting properties. As we know, materials are made up of atoms comprised of electrons and nuclei and all these material properties originate from the interactions among electrons and atomic nuclei. Classical mechanics can describe the …

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Nanofabrication of MRAM

Nanofabrication deals with the fabrication of materials and structures at nanoscale. The essential requirements of a nanofabrication are a clean room which is devoid of particles and contaminations and also a specially designed equipment facility for thin film deposition, patterning and etching. We are all familiar with integrated chips (ICs). ICs are manufactured through nanofabrication …

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Feature Engineering to make the data elegant and interpretable for the machine learning model

If you have some raw data and want to convert in into meaningful insight, feature engineering in machine learning is one of the best tools to help you. Imagine a real-life situation where you want to know how frequently a customer order food from your restaurant in order to offer best customer service. In such …

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Quantum computing, a superb technology advancement to supercomputers

Supercomputers are a class of high-performance systems comprised of multiple central processing units (CPUs) in order to achieve high computational rates. They achieve fastest performance at a given time by using vector arithmetic which enables the system to operate on pairs of lists of numbers instead of simple pairs of numbers. For instance, if we …

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Binomial Distribution

Introduction Probability distributions are cornerstones in the theory of machine learning. The entire field of machine learning is often theoretically viewed or explained from a probabilistic perspective. Compared to the linear algebraic point of view, the probabilistic perspective gives formulations of machine learning algorithms that are more impressive. Understanding probability distributions and their behavior are …

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Frequentist and Bayesian: A Quick Comparison Note

Frequentist and Bayesian are two schools of thought in probability theory. Let us look at the difference between them. Some of them are discussed below. Frequentist is objective and is not influenced by personal feelings or opinions in considering and representing facts​. But Bayesian is subjective, based on or influenced by personal feelings, tastes, or …

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Spin Transfer Torque based Magnetic RAM: A stable promise to denser and faster memory

Dream for a computer storage device which performs as fast as static RAM (SRAM), delivers the cost effectiveness of dynamic RAM (DRAM), promises the non-volatility of flash memory, combined with added benefits of longevity, denser storage, durability and stability. Let’s discuss whether spin transfer torque based Random access Memory (STT-RAM) devices can help to realize …

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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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