Bindeshwar S. Kushwaha

Latex Workshop for Research Paper Writing

Latex /

Workshop Detail Presentation Resources Latex Tutorials 1-https://www.bu.edu/math/files/2013/08/ShortTeX3.pdf 2-https://cdn.overleaf.com/static/latex/learn/free-online-introduction-to-latex-part-1.pdf 3-https://tobi.oetiker.ch/lshort/lshort.pdf  Latex Diagrams  Tikz  Package 4-https://tug.ctan.org/info/visualtikz/VisualTikZ.pdf 5-https://www.bu.edu/math/files/2013/08/tikzpgfmanual.pdf   See Also: Beamer Latex Code to Explain Variance What is Research? How to Write a Research Paper

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Normal Distribution – Numerical Problems with Solutions

Artificial Intelligence, Machine Learning, Probability and Statistics /

Normal Distribution – Numerical Problems with Solutions Author: Bindeshwar Singh Kushwaha Platform: PostNetwork Academy 1. Definition of Normal Distribution A continuous random variable $X$ follows a Normal Distribution with mean $\mu$ and variance $\sigma^2$ if its probability density function (PDF) is: $$ f(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{ -\frac{(x – \mu)^2}{2\sigma^2} }, \quad -\infty < x

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Retrieval-Augmented Generation (RAG) with LLMs

Retrieval-Augmented Generation (RAG)Retrieval-Augmented Generation (RAG) Explained | LLMs and the Need for RAG

Agentic AI, Generative A. I., Large Language Models, RAG /

Retrieval-Augmented Generation (RAG) LLMs and the Need for RAG Author: Bindeshwar Singh Kushwaha Platform: PostNetwork Academy Let’s Start With a Question Suppose you ask a Large Language Model: “What is the medical history of my uncle?” Will the LLM know the answer? Answer: No. Why Can’t the LLM Answer? LLMs are trained on public Internet

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Python for A.I. (ML DL GENAI LLMS AGENTIC AI ) and ROBOTICS

Uncategorized /

No. Topic Complete (Post + Video + PDF + Quiz) 1 Python Programming Basics 1.1 Introduction to Python & Installation 🔗 1.2 Variables, Data Types & Operators 🔗 1.3 Conditional Statements 🔗 1.4 Loops (for, while) 🔗 1.5 Functions & Recursion 🔗 1.6 Lists, Tuples, Sets, Dictionaries 🔗 1.7 File Handling & Exception Handling 🔗

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Normal Distribution A Detailed Step-by-Step Explanation

Artificial Intelligence, Machine Learning, Machine Learning, Mathematics, Probability and Statistics, Research and Development, Statistics /

Normal Distribution A Detailed Step-by-Step Explanation By Bindeshwar Singh Kushwaha PostNetwork Academy Introduction: Random Variables A random variable (r.v.) is a function that assigns a numerical value to each outcome of a random experiment. There are two main types of random variables: Discrete Random Variable: Takes countable values (e.g., number of heads in 3 coin

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Negative Binomial Distribution | Simple Explanation #177 Data Sc. and A.I. Lect. Series

Probability and Statistics, Statistics /

Negative Binomial Distribution A Detailed Step-by-Step Explanation By Bindeshwar Singh Kushwaha PostNetwork Academy Introduction: Relation with Geometric Distribution The negative binomial distribution is a generalization of the geometric distribution. It describes the number of failures before the \( r^{th} \) success in a sequence of Bernoulli trials. When \( r = 1 \), it reduces

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Support Vector Machines Made Easy | SVM Explained with Example

Artificial Intelligence, Machine Learning, Machine Learning /

Support Vector Machine (SVM) A Simple Numerical Example – Detailed Explanation Author: Bindeshwar Singh Kushwaha PostNetwork Academy Introduction: Type and Purpose of SVM Type of Algorithm: Supervised Machine Learning Algorithm Used for Classification and Regression (SVR) Discriminative Model – finds decision boundaries Known as a Maximum-Margin Classifier Purpose: Find the optimal hyperplane that separates classes

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Geometric Distribution Made Simple | Stepwise Approach #176 Data Sc. and A.I. Lect. Series

Algebra, Artificial Intelligence, Machine Learning, Probability and Statistics /

  Geometric Distribution Made Simple | Stepwise Approach Bindeshwar Singh Kushwaha PostNetwork Academy  Geometric Distribution Let a sequence of Bernoulli trials be performed, each with constant probability \(p\) of success and \(q = 1 – p\) of failure. Trials are independent, and we continue performing them until the first success occurs. Let \(X\) be the

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