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Deep dives on Deep Learning, Data Science, Marketing Analytics, and AI for builders — written with real analogies, worked examples, and zero hand-waving. By a Senior Data Scientist with 10+ years of experience.

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Deep Learning Data Science Marketing Analytics AI for Builders 10+ years · HPE · Empower
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Venkata Kiran
Senior Data Scientist
AI Builder · Bangalore
10+
Yrs DS & AI
44k
Instagram
4
Pillars
About me

I'm Venkata Kiran — a Senior Data Scientist with over a decade of experience across marketing analytics, machine learning, and AI product development. I've held roles at HPE and Empower Retirement, and I'm currently building auryd.io — an AI-powered ad spend analyzer for marketing agencies.

This blog exists because I got tired of explanations that skip the hard parts. Every article here comes with real math, worked numerical examples, and the intuition that only comes from applying these techniques professionally for years. Written for students getting serious and practitioners who want to go deeper.

I run @datamavericks on Instagram (44.5k followers) and share my build-in-public journey at @VenkataBuilds on X.

@datamavericks @VenkataBuilds LinkedIn GitHub
Published
Articles
1 live · more soon
Deep Learning
σ(x) = 1/(1+e⁻ˣ)
f(x) = max(0, x)
GELU(x) = x·Φ(x)
Deep Learning April 2026
Activation Functions in Deep Learning — A Complete Guide with Real-World Analogies
Step, Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax, GELU — every function explained with its formula, derivative, real-world analogy, worked numerical example, and when to use it. Seven deep dives, one article.
15 min read · 7 functions covered Read article →
Deep Learning
Backpropagation from Scratch
The chain rule, gradient flow, and why deep networks actually learn — with full numerical derivations.
Coming soon
Marketing Analytics
Attribution Modelling for Performance Marketers
Last-click, linear, time-decay, Shapley — which model is right for your budget.
Coming soon
AI for Builders
How to Build an AI Feature in a Weekend
From API call to production-ready feature — the full decision stack a solo builder needs.
Coming soon
Startups
What Cold Calling 100 Agencies Taught Me About PMF
Real rejection patterns and the three objections that reveal everything about fit.
Coming soon
Coverage
Topics
01
Deep Learning
Neural networks, backprop, activations, transformers — foundations to frontier.
1 article live
02
Data Science
Statistics, feature engineering, ML pipelines, and practical data thinking.
Coming soon
03
Marketing Analytics
Attribution, ROAS, ad spend analysis, and data-driven performance marketing.
Coming soon
04
AI for Builders
Building AI products, SaaS tools, and going from zero to first paying user.
Coming soon
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