Author name: Roshna S H

Dr. Roshna S H is a scientist by profession with more than five years of teaching experience at graduate and postgraduate levels. She has obtained a Ph.D. from IIT Madras after M.Sc., M. Phil in Physics. She has also cleared CSIR-NET and GATE with top ranks. She is a constant content creator through publications in peer-reviewed international journals and by writing concept based blog articles. She has research experiences in diverse areas spans from atmospheric science to optics as well as spintronics and magnetism. She has obtained awards and recognitions at various international platforms for her contributions as scientific articles and oral presentations. Dr. Roshna completed her Ph.D. with exposure at American physical society, Material research society, University of Oxford, etc. She focuses on communicating the concepts with utmost clarity in the simplest possible way.

Python Foundations-2: Beginner Probability and Statistics Problems for Machine Learning

Leave a Comment / Python Foundation for Machine Learning /

Building on the fundamental concepts covered in the initial article of the “Python Foundations” series, we are excited to continue our exploration with “Python Foundations-2: Beginner Probability and Statistics for Machine Learning.” In this article, we present 10 beginner-level problems, each accompanied by detailed explanations and Python code, aimed at reinforcing your understanding of probability […]

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Python Foundations for Machine learning-1: Exploring Fundamental Probability and Statistics

Leave a Comment / Python Foundation for Machine Learning /

In the ever-evolving landscape of machine learning, a profound understanding of probability and statistics forms the bedrock upon which intelligent algorithms thrive. In this inaugural article of the “Python Foundations for machine learning” series, we embark on a journey delving into the fundamental principles of probability and statistics, unravelling their significance in the context of

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The Subtle art of Not Giving a F*ck

Navigating Life’s Uncertainties: A Candid Exploration of “The Subtle Art of Not Giving a F*ck” by Mark Manson

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In a world saturated with motivational literature, Mark Manson’s “The Subtle Art of Not Giving a F*ck” stands out as a beacon of unfiltered wisdom. Released in 2016, this book provides a candid and pragmatic approach to navigating life’s complexities. Manson challenges traditional self-help narratives, encouraging readers to confront life’s struggles head-on and redefine their

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sot

Fundamental Concepts of Spin-Orbit torque based Magnetic RAM

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Spin orbit torque based magnetic random access memory (SOT-MRAM) are non-volatile memory devices which make use of spin based transport electronics named as spintronics. In such devices, capacitors in conventional memory devices are replaced by a trilayer structure called magnetic tunnel junction (MTJ). As spin is non-volatile, it overcomes the charge leakage associated with conventional

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rejection-proof

Rethink Rejection and Overcome Fear of Rejection: Book Review of Rejection Proof

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The book titled “Rejection Proof” is written by Jia Jiang. The author had great entrepreneurial aspirations but got stuck to achieve his dreams by the fear of rejection. The author decided to overcome the fear of rejection and in order to achieve it, he designed a plan to take up a challenge he named “100

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Statistical Testing Methods in Machine Learning

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Statistical testing methods are used in machine learning to ensure that a finding is statistically significant. Hypothesis testing most commonly termed statistical testing evaluates two mutually exclusive statements about a population and determines which among the two statements best describe the experimental/sample data. Hypothesis testing is a statistical method to determine whether two samples have

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Large Language Models in Deep Learning

Leave a Comment / Conceptual, Machine Learning Fundamentals /

Large language models (LLM) are general purpose language models in deep learning. The foremost word “Large” in LLM indicates a large training dataset which is often in the petabyte scale with an enormous number of parameters. General purpose means the models are capable of handling common problems in our day today life. The BERT model

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