Research and Development

The Regression Revolution: How Sir Francis Galton’s Work Laid the Groundwork for Neural Networks

Research and Development /

  Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Sir Francis Galton: Biography and Contributions Quick Facts Name: Sir Francis Galton, FRS FRAI Born: 16 February 1822, Birmingham, England Died: 17 January 1911, Haslemere, Surrey, England (Aged 88) Resting Place: Claverdon, Warwickshire, England Education: King’s College London, Trinity College Cambridge Father: Samuel Tertius Galton Relatives: Charles […]

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Research

What is Research? How to Write a Research Paper

Research and Development /

What is Research? How to Write a Research Paper Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Outline What is Research? Benefits of Research Research Paper Sections Research Paper Examples Where to Publish Research Papers What is Peer Review? What is Impact Factor? Where to Start from? What is Research? Systematic investigation to discover new knowledge

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Moments of Binomial Distribution Video I Data Science and A.I. Lect. Series

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

  Moments of Binomial Distribution By Bindeshwar Singh Kushwaha — PostNetwork Academy Moment Definition Let \( X \sim B(n, p) \) be a binomial random variable. The \( r^\text{th} \) raw moment about origin: \( \mu_r’ = \mathbb{E}(X^r) = \sum_{x=0}^{n} x^r \cdot \mathbb{P}(X = x) \) First-order moment (mean): \( \mu_1′ = \mathbb{E}(X) \) Binomial

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But What A Neural Network Learns Actually: Neural Network Architecture for Iris Data Set

Artificial Intelligence, Machine Learning, PyTorch, Research and Development /

 Neural Network Architecture for Iris Dataset Author: Bindeshwar Singh Kushwaha Institution: PosstNetwork Academy  Outline Iris Dataset Overview Neural Network Architecture Mathematical Formulation Code Walkthrough Live Loss Plot Evaluation and Summary  Sample from Iris Dataset Sepal Length Sepal Width Petal Length Petal Width Class 5.1 3.5 1.4 0.2 Setosa 6.2 2.9 4.3 1.3 Versicolor 5.9 3.0

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Binomial Distribution Data Science and A.I. Lecture Series

Artificial Intelligence, Data Handling, Generative A. I., Mathematics, Maths, Probability and Statistics, Research and Development, Statistics /

  Binomial Distribution Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha | PostNetwork Academy Binomial Probability Function The binomial probability function is given by: \[ P(X = k) = \binom{n}{k} p^k (1 – p)^{n – k} \] where: \( n \) = total number of trials \( k \) = number of successes

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Bernoulli Distribution in Probability and Statistics

Artificial Intelligence, Data Handling, Machine Learning, Mathematics, Maths, Research and Development, Statistics /

Bernoulli Distribution Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha | PostNetwork Academy Introduction to Bernoulli Distribution A Bernoulli trial is an experiment with only two possible outcomes: Success (1) and Failure (0). If p is the probability of success, then q = 1 – p is the probability of failure. A random

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Addition, Multiplication Theorem of Expectation and Covariance

Artificial Intelligence, Calculus, Mathematics, Maths, Probability and Statistics, Programming, Python, Research and Development, Statistics /

Addition, Multiplication Theorem of Expectation and Covariance Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha PostNetwork Academy Outline Introduction Addition Theorem of Expectation Proof of Addition Theorem Multiplication Theorem of Expectation Proof of Multiplication Theorem Covariance Introduction Expectation (or expected value) is a fundamental concept in probability and statistics. It provides a measure

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Moments and Other Measures in Terms of Expectations

Artificial Intelligence, Data Handling, Machine Learning, Mathematics, Maths, Research and Development, Statistics /

  Moments and Other Measures in Terms of Expectations Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha – PostNetwork Academy Moments The \( r^{th} \) order moment about any point \( A \) of a variable \( X \) is given by: For discrete variables: \[ \mu_r’ = \sum_{i=1}^{n} p_i (x_i – A)^r

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