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Articles by Naman Agrawal
Probabilistic Data Structures Decoded: Enhancing Performance in Modern Computing Original
Introduction What is a Probabilistic Data Structure?
Algorithmic Alchemy with The Fast Fourier Transform Original
The Ultimate Guide to Mechanics of the FFT Algorithm with Applications in Signal Processing Introduction What is the Fourier Transform?
Decoding the Symphony of Sound: Audio Signal Processing for Musical Engineering
The Ultimate Guide to Time and Frequency Domain Audio Feature Extraction using Python Introduction Time Domain Feature Extraction 2.1 Basics of Audio Signal Processing: Frame Size and Hop Length 2.2 Feature 1: Amplitude Envelope 2.3 Feature 2: Root Mean Square Energy 2.4 Feature 3: Crest Factor 2.5 Feature 4: Zero Crossing Rate Frequency Domain Feature Extraction 3.1 Feature 5: Band Energy Ratio 3.2 Feature 6: Spectral Centroid 3.3 Feature 7: Spectral Bandwidth 3.4 Feature 8: Spectral...
Audio Signal Processing for Musical Engineering
Introduction Time Domain Feature Extraction 2.1 Basics of Audio Signal Processing: Frame Size and Hop Length 2.2 Feature 1: Amplitude Envelope 2.3 Feature 2: Root Mean Square Energy 2.4 Feature 3: Crest Factor 2.5 Feature 4: Zero Crossing Rate Frequency Domain Feature Extraction 3.1 Feature 5: Band Energy Ratio 3.2 Feature 6: Spectral Centroid 3.3 Feature 7: Spectral Bandwidth 3.4 Feature 8: Spectral Flatness Conclusion References How does a computer read an audio signal What are time and...
The Epic Quest for the Optimal Stride
Introduction Method 1: Fixed Step Size Method 2: Exact Line Search Method 3: Backtracking Line Search Conclusion When training any machine learning model, Gradient Descent is one of the most commonly used techniques to optimize for the parameters. Gradient descent offers an efficient way of minimizing the loss function for the incoming data, especially in cases, where they may not be a closed-form solution to the problem.
Charting the Non-Parametric Odyssey: Statistical Frameworks for Distribution-Free Hypothesis Testing
Gauging the Mechanics of the Sign and Wilcoxon Signed Rank Tests Introduction Tool 1: One-Sample Sign Test Tool 2: Two-Sample Sign Test Limitations of the Sign Test Tool 3: One-Sample Wilcoxon Signed Rank Test Wilcoxon Signed Rank Test: Possible Complications Tool 4: Two-Sample Wilcoxon Signed Rank Test Conclusion Statistics is the corpus of instruments of knowledge that allow us to infer from data through tools including, but not limited to, estimation of parameters, construction of...
Statistical Frameworks for Distribution-Free Hypothesis...
Introduction Tool 1: One-Sample Sign Test Tool 2: Two-Sample Sign Test Limitations of the Sign Test Tool 3: One-Sample Wilcoxon Signed Rank Test Wilcoxon Signed Rank Test: Possible Complications Tool 4: Two-Sample Wilcoxon Signed Rank Test Conclusion Statistics is the corpus of instruments of knowledge that allow us to infer from data through tools including, but not limited to, estimation of parameters, construction of confidence intervals, and hypothesis testing to validate our assumptions.
QQ Plotting Your Way to Data Enlightenment: A Hitchhiker’s Guide to the Galaxies of Distribution
Is Your Data Normal? The Ultimate Guide to QQ Plots Using R Statistics is a mysterious field. Within its vast corpus of theories, formulations, and frameworks, lies knowledge that has profound applications across all areas of study. However, despite its tremendous success, it can be unfriendly too because a lot of its power comes from the assumptions it makes.
A Hitchhiker's Guide to the Galaxies of Distribution
Statistics is a mysterious field. Within its vast corpus of theories, formulations, and frameworks, lies knowledge that has profound applications across all areas of study. However, despite its tremendous success, it can be unfriendly too because a lot of its power comes from the assumptions it makes.
Coupon Collector’s Problem: A Probability Masterpiece
Unpacking the intricacies of a classic probability puzzle The world of mathematics is full of fascinating puzzles and paradoxes that challenge our understanding of the world around us. The coupon collector problem is one such puzzle, an age-old problem that has intrigued mathematicians and statisticians for generations. The coupon collector problem is as follows: Consider a contest involving collecting n unique coupons.
A Probability Masterpiece
The world of mathematics is full of fascinating puzzles and paradoxes that challenge our understanding of the world around us. The coupon collector problem is one such puzzle, an age-old problem that has intrigued mathematicians and statisticians for generations. The coupon collector problem is as follows: Consider a contest involving collecting n unique coupons. It is given that every cereal box, contains one coupon, with every coupon being equally likely to be present in a given box.
Gambler’s Ruin Problem: A Probability Antiquity
Understanding one of the most famous problems in probability theory Gambler’s ruin is one of the most essential concepts in Probability theory. Its first mention dates back to the 17th century in a letter exchange between Blaise Pascal and Pierre Fermat. The problem involves determining the probability that a gambler wins under the following conditions: They both start off with a fixed amount of money. The probability of either one of them winning is constant throughout the game.
Why is Smoking Injurious to Health? A Probabilistic View
There are lots of research papers, academic journals, and articles about the biological impact of smoking on a person's health. Some papers claim that smoking one cigarette reduces your lifespan by about 11 minutes, while others claim that the death rates of smokers are about twice that of a non-smoker. Lets' not dive into the specifics of how these numbers are obtained or how accurate they are.
Decoding Logistic Regression Using MLE
This article was published as a part of the Data Science Blogathon. Introduction Imight be familiar with the immense utility of logistic regressions to solve supervised classification problems. Some of the complex algorithms that we use today are largely a sophisticated version of logistic regression. How do we implement logistic regression for a model? For those of us with a background in using statistical software like R, it’s just a calculation done using 2e glm function in R).
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