Join me on a journey through the mathematical foundations of AI and the emotional rollercoaster of supporting Liverpool FC. A blog for those who calculate probabilities and those who believe in miracles.
I took a half day off work just to watch Dhurandhar 2 on the morning show. Couldn't wait. And MAN — it hit me. As someone from a family that has a martyr, this movie hit different. Here's what it felt like in the hall — not a breakdown of who stitched what, just me, the screen, and the tears.
A unique blend of rigorous academics and raw emotion. Here is the trailer of what you can expect.
From Boolean Logic to Fuzzy Systems. I break down complex mathematical concepts that power modern AI. No hand-waving, just pure understanding.
Unapologetic love for Liverpool FC. Why football is the greatest sport, the philosophy of Klopp, and why I chose the "Beautiful Game" over the "Lazy Game" (Cricket).
Reflections on my Bharatiya heritage, Swami Vivekananda's wisdom, and how ancient values shape modern technological thinking.
A comprehensive journey through nonparametric statistics, robust methods, fuzzy logic, and sampling theory. From Boolean algebra to complete audit blueprints.
Percentiles, quantiles, KDE, and distribution-free methods
MAD, adjusted boxplots, outlier detection, and safe ratios
T-norms, membership functions, and soft decision boundaries
Track your progress • 6 Modules • No prerequisites required
"Supporting Liverpool is not like supporting any ordinary club—it's a way of life."
Inspired by the passion of Jurgen Klopp and the leadership of Steven Gerrard, I explore why Football beats the "Lazy Game" (Cricket) every single time. It's about strategy, physical excellence, and the global unity of YNWA.
I took a half day off work just to watch Dhurandhar 2 on the morning show. Couldn't wait. And MAN — it hit me. As someone from a family that has a martyr, this movie hit different. Here's what it felt like in the hall — not a breakdown of who stitched what, just me, the screen, and the tears.
It's almost been an year since I met my wife (then my girlfriend), and here's a small note of gratitude, thanksgiving to God, parents and everyone involved to have made my life even ,ore beautiful than it was
Part 1 of a series exploring what the Atharva Veda says about marriage—from Pativedanam (securing a spouse) and Dampatya Santosha to Varchas, Veera Janana, Brahmajaya, and the householder's sacred fire.
A comprehensive mathematical summary mapping nonparametric statistics, robust measures, sampling theory, decision metrics, set operations, and fuzzy aggregation to their pipeline implementations.
Synthesize everything from quantile thresholds to strata to sample sizes. Learn to construct a complete stratified audit plan with cutoffs, sample sizes, and investigation workflows.
Understand how numeric coercion and NA handling affect data distributions. Learn the impact of different imputation strategies on mean, variance, and quantiles for threshold-based rule evaluation.
Understand how numeric coercion and NA handling affect data distributions. Learn the impact of different imputation strategies on mean, variance, and quantiles for threshold-based rule evaluation.
Master practical considerations for computing empirical quantiles. Understand how ties, discrete samples, and different interpolation schemes affect quantile estimates and threshold repeatability.
Compare min/max logic with product t-norm and Łukasiewicz variants. Understand t-norm families, boundary behaviors, and why min/max yields conservative idempotent aggregation for rule strength evaluation.
Learn to pair semantically complementary configuration sets like Premium/Standard and Verified/Unverified. Understand equivalence relations, pairing consistency, and how mapping functions ensure aligned parameters across pairs.
Learn to interpret priority tiers as prior beliefs or cost weights. Understand cost-sensitive thresholding, Bayes optimal decision rules, and how per-tier thresholds change labeling geometry through iso-cost analysis.
Learn to view event tagging as rule-based classification. Understand indicator functions, piecewise partitions, and priority-level conditioning—essential tools for mathematically partitioning events into Flagged and Passed categories.