Machine Learning

Poisson Distribution | Data Sc. and A.I. Lect. Series

Artificial Intelligence, Data Handling, Machine Learning, Mathematics, Probability and Statistics /

📘 Understand Poisson Distribution 📌 Introduction In binomial distributions, events’ occurrences and non-occurrences are equally important. However, in real-life situations: Events do not occur as outcomes of fixed number of trials. Events occur randomly over time. Interest lies only in the number of occurrences. Examples: Number of printing mistakes per page in a book. Number […]

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Binomial Distribution Mean and Variance Related Problems| Data Sc. and A.I. Lect. Series

Artificial Intelligence, Machine Learning, Probability and Statistics /

Binomial Distribution: Mean and Variance Problem 1: Mean = 3, Variance = 4? Given: Is it possible a binomial distribution has a mean of 3 and a variance of 4? Solution: Mean: \( \mu = np \) Variance: \( \sigma^2 = npq \), where \( q = 1 – p \) Given: \( np =

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Moment Generating Function of Binomial Distribution

Artificial Intelligence, Generative A. I., Machine Learning, Probability and Statistics, Statistics /

Moment Generating Function of Binomial Distribution Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Definition of Moment Generating Function The moment generating function (m.g.f.) of a random variable \( X \) is defined as: \[ M_X(t) = E(e^{tX}) \] For a continuous random variable: \[ M_X(t) = \int_{-\infty}^{\infty} e^{tx} f(x) \, dx \] For a discrete

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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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Matrix Operations with PyTorch | Learn Linear Algebra with Code

Artificial Intelligence, Generative A. I., Linear Algebra, Machine Learning, Mathematics, Python, PyTorch /

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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Building a Smart Indian Food Recommender Using TinyLlama, Ollama, and Python

Artificial Intelligence, Data Handling, Deep Learning, Generative A. I., IoT, Machine Learning /

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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