The article discusses interview questions asked in many machine learning job roles Explain label encoding and one hot encoding, Explain how the dimensionality of a dataset is affected with these methods in machine learning Machine learning algorithms can understand only numbers. Often, the dataset consists of categorical and numerical features. We need to convert the …
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 …
A Lighter Perspective to Density Functional Theory Read More »
As an experimental physicist, I am always curious on whether I can extract accurate information in a situation where I have an enormous amount of experimental data whereas I left with apparently no direct theoretical/conceptual knowledge on it. The world around us is described with well formulated physics-based models. We can predict the orbit of …
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 …
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 …
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 …
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. Binomial distribution is one of the many …
Frequentist and Bayesian are two schools of thought in probability theory. Let us look at the difference between them. Bayesian and frequentist approaches are two major frameworks in statistics for analyzing data and making statistical inference. The frequentist approach is based on the idea of probability as the long-term frequency of an event in repeated …
Frequentist and Bayesian: A Quick Comparison Note Read More »
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 …
Spin Transfer Torque based Magnetic Memory Devices Read More »
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 …