Ml on Samarthya

AI on this blog: a reading map

Mon, 26 Oct 2026 18:50:00 -0400

I’ve written about AI and data on and off since 2023, one post at a time. Read in this order, they tell one story: from how machines learn, to how language models work, to building with them, to letting them act.

1. How machines learn
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Post	In one line
The Core Divide	Supervised vs unsupervised learning
K-Nearest Neighbors	Classify by the company you keep
Mastering TF-IDF	Turning text into numbers that mean something
Data Mining Essentials	Distances, clusters and the core vocabulary
Matrix Multiplication to GPUs	Why AI runs on graphics cards

2. How language models work
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Post	In one line
GenAI	What generative AI is, and its building blocks (2023)
Language Model	Transformers and the attention breakthrough
Two Students, Two Talents	Auto-regressive (GPT) vs auto-encoding (BERT) models
The Evolution of Language Models	From 2017’s transformer to today’s families
The Probability Engine	The maths of choosing the next token
Tokens and tokenomics	What a token is, context windows, and the bill

3. Building with models
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Post	In one line
Ollama	Running language models on your own machine
A minimal LangChain + Ollama demo	Prompt, model and chain, end to end
A practical RAG assistant	Answering from your own documents
From speech to text	Speech recognition in Python
Choosing the right framework	The AI stack, layer by layer
ML pipelines on Vertex AI	Train, evaluate and deploy, repeatably

4. Letting models act
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Post	In one line
From RAG to tools and agents	Function calling and the agent loop
MCP, the Model Context Protocol	One standard plug for tools and data
The Ghost Board	A story about agents nobody was watching

The data shelf
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Analytics and big-data posts that sit underneath much of the above:

AI in practice, part 2: ML pipelines on Google Vertex AI

Mon, 26 Oct 2026 18:10:00 -0400

A notebook that trains a good model once is an experiment. A pipeline that trains it again next week, on new data, and tells you whether it got better: that’s a system. Here’s how Google’s Vertex AI turns one into the other.

Why pipelines
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Training a model is never one step. Data is extracted, cleaned and split; a model is trained, evaluated and compared with the current one; if it’s better, it’s registered and deployed. Done by hand in a notebook, that sequence is easy to get subtly wrong and impossible to audit.

AI in practice, part 1: Choosing the right framework

Mon, 26 Oct 2026 18:00:00 -0400

“Which AI framework should I use?” has no single answer, because the names people throw around do completely different jobs. Put them on a map first, and the choice usually makes itself.

This series picks up where my earlier posts left off. Those covered the ideas: what generative AI is, how transformers pay attention, why GPUs matter, and two hands-on builds (LangChain with Ollama and a RAG assistant). This part is the map that connects them.

The Probability engine

Thu, 16 Apr 2026 00:00:00 +0000

From “what is autocomplete?” to the full mathematics powering Claude Sonnet 4.6; with history, intuition, worked examples and cheatsheet.

Two Students, Two Talents: The Story of How AI Learns Language

Sat, 31 Jan 2026 12:00:00 +0000

Remember how we compared a language model to an advanced autocomplete system? Well, here’s where the story gets interesting. Not all language models learn the same way. Imagine two students sitting in the same classroom, both brilliant, but each taught to master language through completely different methods. One becomes an incredible writer, the other becomes an exceptional reader. This is the tale of auto-regressive and auto-encoding models.

Mastering TF-IDF: A Gamified Journey!

Wed, 17 Dec 2025 12:00:00 +0000

Understanding how computers “read” and understand text is a fascinating field. One of the most fundamental techniques for identifying important keywords in a document, relative to a collection of documents, is TF-IDF (Term Frequency-Inverse Document Frequency).

k-Nearest Neighbors

Sat, 22 Nov 2025 12:00:00 +0000

Images: Courtesy GEMINI

KNN is the friendly, neighborhood algorithm that believes you are defined by the company you keep. It doesn’t try to learn a complex rule; it just looks around and goes with the consensus of its closest, most similar peers.

Data Mining Essentials

Sat, 08 Nov 2025 12:00:00 +0000

Whether you’re preparing for a quiz or just brushing up on fundamentals, this guide distills the key concepts from Data Mining into bite-sized, memorable chunks. Let’s dive in!

Matrix Multiplication to GPU’s

Thu, 16 Oct 2025 12:00:00 +0000

If you are like me, you must have stumbled on this question – Why is matrix multiplication so significant – especially in the AI/ML universe.

The Core Divide

Fri, 10 Oct 2025 12:00:00 +0000

At the heart of machine learning, algorithms learn from data. The key distinction between supervised and unsupervised learning is the type of data used for training.

Beyond the `DataFrame`: How Parquet and Arrow Turbocharge PySpark 🚀

Thu, 09 Oct 2025 12:00:00 +0000

In my last post, we explored the divide between Pandas (single machine) and PySpark (distributed computing).

The conclusion: for massive datasets, PySpark is the clear winner.

PySpark vs Pandas: Choosing Right! 🐍

Thu, 09 Oct 2025 12:00:00 +0000

As another project wraps up, bringing with it the bittersweet relief of an almost-closed chapter (and, yes, a few too many late-night coding sessions), I found myself reflecting on the tools that got us through.

Apache Spark

Sun, 14 Sep 2025 12:00:00 +0000

So as part of the academic curriculum we just started re-exploring Apache Spark. It’s been one of the technology that I had long lost touch with. Ironical but true, in this age of big data and speed Spark was left somewhere way behind in my past projects working for Pitney Bowes.

Model and Selection

Fri, 07 Feb 2025 12:00:00 +0000

What is a Model?
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In the context of forecasting and business analytics, a model is a simplified representation of a real-world situation or process. It’s like a blueprint or a miniature version that helps us understand, analyze, and make predictions about something complex.

GenAI

Tue, 03 Oct 2023 12:00:00 +0000

(thumbnail only — original image lost)

Artificial Intelligence (AI) has revolutionized many aspects of our lives, from virtual assistants like Siri and Alexa to self-driving cars and personalized content recommendations. One of the exciting and rapidly evolving subfields of AI is Generative AI, often referred to as GenAI. In this beginner-friendly blog post, I’ll explore the basics of Generative AI, its applications, and how it’s changing the way we interact with technology.