Artificial Intelligence

IBM Research and Hugging Face Introduce SmolDocling: A Compact Vision-Language Model for Document Conversion

Artificial Intelligence /

IBM Research and Hugging Face Introduce SmolDocling: A Compact Vision-Language Model for Document Conversion IBM Research and Hugging Face have unveiled SmolDocling, an ultra-compact vision-language model designed for end-to-end document conversion. Unlike traditional models that rely on large foundational architectures or complex pipelines, SmolDocling offers a lightweight, efficient solution for processing entire documents while preserving […]

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Bivariate Discrete Cumulative Distribution Function

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

Bivariate Discrete Cumulative Distribution Function Data Science and A.I. Lecture Series Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Joint and Marginal Distribution Functions for Discrete Random Variables Two-Dimensional Joint Distribution Function The distribution function of the two-dimensional random variable \((X, Y)\) for all real \(x\) and \(y\) is defined as: \[ F(x,y) = P(X \leq

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Continuous Cumulative Distribution Function (CDF) | Probability & Statistics

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

  Definition: Continuous CDF A continuous random variable can take an infinite number of values in a given range. The Probability Density Function (PDF) \( f(x) \) describes the likelihood of \( X \) falling within a small interval. The Cumulative Distribution Function (CDF) is given by: \[ F(x) = P[X \leq x] = \int_{-\infty}^{x}

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Ordinary Least Squares (OLS) Regression: Step-by-Step Guide with Derivation & Visualization

Artificial Intelligence, Machine Learning /

Ordinary Least Squares (OLS) Regression Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Dataset of a Company X (Budget) Y (Sales) 1 2 2 2.8 3 3.6 4 4.5 5 5.1 Description: The dataset represents the relationship between advertising budget (\(X\)) and sales revenue (\(Y\)). The company wants to analyze how the budget affects sales using

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Tokens in Python

Artificial Intelligence, Programming, Python, Research and Development /

  Tokens in Python Introduction Tokens are the smallest individual units in a Python program. Everything in a Python program is built using tokens. Python has five types of tokens: Keywords: Reserved words in Python. Identifiers: Names given to variables, functions, and classes. Literals: Fixed values such as numbers, strings, and boolean values. Operators: Symbols

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Introduction to Vectors

Algebra, Artificial Intelligence, Linear Algebra /

Vectors in \(\mathbb{R}^n\) and \(\mathbb{C}^n\) Introduction to Vectors A vector is a mathematical object that has both magnitude and direction. Vectors are essential in physics, engineering, and mathematics. They can be represented in different dimensions, such as real number space \(\mathbb{R}^n\) and complex number space \(\mathbb{C}^n\). Visualization of Vectors in \(\mathbb{R}^3\) Consider a three-dimensional space

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What is Generative AI?

Artificial Intelligence, Generative A. I., Machine Learning /

  The Rise of Generative AI:  Overview Unlike traditional AI systems that rely on predefined rules, generative AI models use vast datasets and deep learning techniques to generate novel and contextually relevant outputs. This transformative capability is reshaping industries such as content creation, education, healthcare, and entertainment. How Generative AI Works At its core, generative

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Introduction to Machine Learning

Artificial Intelligence, Data Handling, Machine Learning /

Introduction to Machine Learning Definition and Types Welcome to this detailed introduction to Machine Learning. This post explores the fundamental definitions, types of machine learning, and their mathematical representations. What is Machine Learning? What is Machine Learning? What are the different types of Machine Learning? How can we mathematically define each type? Definition of Machine

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Some Questions Based on Continuous Probability Distributions | Data Science & AI Lecture Series

Artificial Intelligence /

Some Questions Based on Continuous Probability Distributions Question Compute the conditional probability: \[ P\left(X > \frac{3}{4} \mid X > \frac{1}{2}\right) \] Theory Behind Solution The conditional probability formula: \[ P(A | B) = \frac{P(A \cap B)}{P(B)} \] For continuous random variables, probability is computed using integration. Understanding Probability Density Functions A probability density function (p.d.f.)

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Central Limit Theorem (CLT) and Uniformly Minimum Variance Unbiased Estimator (UMVUE)

Artificial Intelligence, Data Handling, Research and Development, Statistics /

Central Limit Theorem (CLT) and Uniformly Minimum Variance Unbiased Estimator (UMVUE) By: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Question 1 Suppose \( X_1, X_2, \dots \) is an i.i.d. sequence of random variables with common variance \( \sigma^2 > 0 \). Define: \[ Y_n = \frac{1}{n} \sum_{i=1}^{n} X_{2i-1}, \quad Z_n = \frac{1}{n} \sum_{i=1}^{n} X_{2i} \]

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