Object-Oriented Programming in Python By: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy What is Object-Oriented Programming (OOP)? OOP is a programming paradigm. It is based on the concept of “objects”. Helps model real-world entities like BankAccount, Student, Car. Makes code more organized, reusable, and easier to maintain. Class in OOP A class is a blueprint […]
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Transpose of a Matrix Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy What is the Transpose of a Matrix? The transpose of a matrix \( A \), denoted \( A^T \), is obtained by interchanging rows and columns. If \( A = \begin{bmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \end{bmatrix} \),
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Matrix Operations with PyTorch Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Matrix Addition and Scalar Multiplication Matrix Addition: We add corresponding elements of the same-sized matrices: \( A + B = [a_{ij} + b_{ij}] \) Scalar Multiplication: Multiply each element of the matrix by the scalar value: \( kA = [k \cdot a_{ij}] \) Example:
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Understanding PyTorch: Tensors, Vectors, and Matrices By: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy What is PyTorch? PyTorch is an open-source deep learning framework. It supports dynamic computation graphs. Designed to be Pythonic and flexible. Commonly used for research and production in AI/ML. Types of Tensors A scalar is a 0-dimensional tensor. A vector is a
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Building a Smart Indian Food Recommender Using TinyLlama, Ollama, and Python In this tutorial, we’ll build a simple yet smart food recommender system that suggests Indian dishes based on the current humidity and temperature values. The system uses a local language model TinyLlama running via Ollama, and Python’s random.randint() function to simulate real-time weather. Why
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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 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 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 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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Mathematical Expectation Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha – PostNetwork Academy Introduction This unit explores the expectation of a random variable. Expectation provides a measure of central tendency in probability distributions. Expectation is useful in both discrete and continuous probability distributions. Problems and examples help in understanding practical applications. Objectives Define
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