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 […]
Linear Dependence and Independence Definition Let \( V \) be a vector space over a field \( K \). Vectors \( v_1, v_2, \ldots, v_m \) in \( V \) are linearly dependent if there exist scalars \( a_1, a_2, \ldots, a_m \) in \( K \), not all zero, such that: \[ a_1 v_1
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Vector Subspace in Linear Algebra Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Definition of a Subspace Let \( V \) be a vector space over a field \( K \) and let \( W \subseteq V \). Then \( W \) is a subspace of \( V \) if \( W \) is itself
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Vector and Vector Space in Linear Algebra by Bindeshwar Singh Kushwaha PostNetwork Academy Definition of ℝn The set of all n-tuples of real numbers is denoted by ℝn: u = (a1, a2, …, an) Each u is called a point or vector. The components ai are called coordinates, components, entries, or elements. The term scalar
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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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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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Some Questions of Linear Algebra: Linear Transformation Definition: Linear Transformation A linear transformation \(T: \mathbb{R}^n \to \mathbb{R}^m\) is a function that satisfies: Additivity: \(T(\mathbf{u} + \mathbf{v}) = T(\mathbf{u}) + T(\mathbf{v})\) for all \(\mathbf{u}, \mathbf{v} \in \mathbb{R}^n\). Homogeneity: \(T(c \mathbf{v}) = c T(\mathbf{v})\) for all \(\mathbf{v} \in \mathbb{R}^n\) and scalars \(c\). Numerical Example: Linear Transformation
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Addition Law of Probability and Examples Data Science and A.I. Lecture Series Introduction to Addition Law Statement: For any two events \( A \) and \( B \), the probability of their union is given by: \[ P(A \cup B) = P(A) + P(B) – P(A \cap B) \] This formula calculates the probability
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Set Operations and Important Laws Data Science and A.I. Lecture Series Set Operations Union Definition: \[ A \cup B = \{x : x \in A \text{ or } x \in B\} \] Examples: \( A = \{1, 2, 3\}, B = \{3, 4, 5\} \implies A \cup B = \{1, 2, 3, 4, 5\}
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Venn Diagrams – Data Science and AI Lecture Series Welcome to our Data Science and AI Lecture Series! In this post, we’ll dive into the world of Venn Diagrams, an essential tool in set theory that simplifies understanding the relationships between sets. Whether you’re studying mathematics, data science, or AI, mastering concepts like intersections, unions,