What is Data Preprocessing? Why It Matters in Machine Learning! Author: Bindeshwar Singh Kushwaha Real-World Data Challenges Missing or incomplete values Inconsistent formatting and typos Mixed data types (text, numeric, dates) Categorical variables needing encoding Scale variations and outliers What is Data Preprocessing? A set of techniques to clean and prepare raw data Essential for […]
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📘 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 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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Transfer Learning and Fine-Tuning Large Language Models In this post, we will explore the concept of Transfer Learning, its connection to fine-tuning large language models (LLMs), and step-by-step instructions to fine-tune DistilGPT-2. What is Transfer Learning? Transfer Learning is a powerful machine learning technique where a model trained on one task is reused as the
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Fitting Binomial Distribution Introduction Fitting a binomial distribution involves comparing observed frequencies with expected frequencies derived from the binomial probability formula. The recurrence relation simplifies the process of finding probabilities. This technique is useful for testing if a dataset follows a binomial distribution. Binomial Probability Function The binomial probability function is: $$p(x) = {n \choose
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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 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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Linear Combinations and Spanning Sets Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Introduction: In this post, we will explore the concepts of Linear Combinations and Spanning Sets in the context of vector spaces in Linear Algebra. Linear Combinations and Spanning Sets Let V be a vector space over a field K. A vector v in
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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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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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