Rama Ramakrishnan

AI Demystified

Created with love for the curious. A technical background is NOT necessary.

Working with LLMs? Keep these things in mind. (September 2024)

Extract just what you need from long LLM responses (August 2024)

Using LLMs in business: A few guidelines (May 2024)

How to use LLMs as "helpers" to build and customize other LLMs (January 2024)

Get better answers from ChatGPT by using custom instructions (December 2023)

How ChatGPT works (a 20-minute explainer video) (September 2023)

How ChatGPT can answer complex questions using external tools (June 2023)

Practical AI

Is learning from experience data (rather than human-generated data) the key to superhuman AI? (April 2025)

Solving RAG's "lost context" problem (April 2025)

A Foundation Model for Tabular Data (March 2025)

Using LLMs to write code - Advice from Simon Willison (March 2025)

Getting payback from Generative AI (Sloan Management Review webinar) (March 2025)

Using GenAI to create no-code prototypes (December 2024)

Generative AI as a New Platform for Applications Development (September 2024)

Identifying AI opportunities in business - don't overlook the things NOT being done (April 2024)

The AI/ML/GenAI landscape (short videos) (March 2024)

Practical ML/Data Science

How big should your sample size be? A handy little formula that every data scientist should know (December 2022)

6 steps for leading successful data science teams (August 2021)

How to Learn Optimal Policies From Data: Part 1, Part 2, Part 3, Part 4 (June-August 2021)

Lessons from a Deep Learning Master (July 2020)

An Alternative to the Correlation Coefficient That Works For Numeric and Categorical Variables (June 2020)

Data Scientists, Ask Yourself Often: So What? (June 2020)

How to Use Causal Inference In Day-to-Day Analytical Work (Part 2 of 2) (June 2020)

How to Use Causal Inference In Day-to-Day Analytical Work (Part 1 of 2) (October 2019)

Create a Common-Sense Baseline First (January 2018)

I Have Data. I Need Insights. Where Do I Start? (July 2017)

Handy Command-Line One-liners for Starting Data Scientists (June 2017)

One More Reason to Prefer Simple Models (April 2016)

Three Ways to Analytic Impact (July 2011)

How Good Management Can Produce Bad Data (Nov 2010)

Practical Python

Useful Pythonic things that I will probably forget and have to re-learn if I don't write them down somewhere :-)
(each entry is written as a Jupyter/Google Colab notebook so that you can run the code and play with it directly)

How to add color to your boring text output (and build a token visualizer

while you are at it!)

Find long Python statements annoying? Here's how to break them up

Be careful when you pass lists, dicts or sets into a function

Easily process arguments for your command-line script with argparse

Random

Nabeel Qureshi's Life Principles - The ones that resonated with me (December 2024)

How to read without slipping into “check the box” mode (May 2022)

You can be - A book for little girls (by Anu Chitrapu) (July 2021)

A Peek into the Incomparable Mind of Isaac Asimov (May 2020)

Just for fun: A Sudoku Solver in Python (May 2019)

Building startups in an exit-friendly way (Jan 2017)

Can Animals Perceive Human Relationships? (Jan 2016)

What an Educated Person Should Know – Steven Pinker (Jan 2016)

The Taste Of This Meal Is Affected By The Room We Sit In (Jan 2016)

The User is Never Wrong (Dec 2015)

Three Supernovae Every Night! (Dec 2015)

Tiponomics: Analytics for Waiters (July 2010)

Farewell to Martin Gardner (May 2010)

The Ascent of Ranking Algorithms (April 2010)

Smarter Cruise Control With Analytics (March 2010)

Applying Behavioral Economics To Retail (March 2010)

Factoids, Stories and Insights (March 2010)

On Cuts and Clutters (my Ph.D. thesis from a long time ago :-))

Hi, I am Rama Ramakrishnan

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AI Demystified

Practical AI

Practical ML/Data Science

Practical Python

Random