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Linear regression is the simplest useful model in machine learning.

Cloudflare gives you three real ways to ship a full-stack CRUD app: Pages with Functions, Pages with a standalone Worker, and Workers with static assets.

Podcasts are a good first pass to get oriented on a topic.

Intro At serving time, a model only ever sees a feature’s most recent value.

There is a small blue blob living on my desktop.

I wanted to move my US stocks (securities) from INDMoney to Interactive Brokers.

Brushing up on my PyTorch skills every week.

Brushing up on my PyTorch skills every week.

Brushing up on my PyTorch skills every week.

I am building a comprehensive set of tools to do life logging.

I am building a comprehensive set of tools to do life logging.

This post is a quick summary of Lessons Learnt From Consolidating ML Models in a Large Scale Recommendation System.

How can you ensure that your contributions are also recognized?

Confidence Interval (CI) CI is an interval.

The Normal and lognormal distributions are fundamental concepts in statistics.

I spent the last two weeks of August in Ooty📍, A hill station in Tamil Nadu.

Have you ever needed to present the output of a GroupBy or Pivot Table?

Problem Statement I have a JSON column in my DataFrame.

In this post, I will describe my Google Sheets dashboard, where I track all fitness-related aspects.

Good intentions never work, you need good mechanisms to make anything happen - Jeff Bezos.

Entering Belagavi My first interaction in the city: ₹250 for an auto for 2 KMs.

Paper link: Monolith: Real Time Recommendation System With Collisionless Embedding Table Abstract Real-time RecSys are important when customer feedback is time sensitive (eg: TikTok short-video ranking).

Some people are born coaches.

Paper link: Deep Recurrent Neural Networks for OYO Hotels Recommendation Abstract A hybrid model with two parts: Embedding generation: generate implicit embeddings of properties.

Alternate title: k-Nearest Neighbours (kNN) in PySpark You can follow the story of what I wanted to do and how I did it.

📆 28th April ⌛ 10:00 PM Started for the bus boarding point.

I delivered this speech on Sunday (17th Apr) at a Toastmasters Meeting.

Today, I am going to discuss my second Toastmasters speech.

Last year in November, I joined Toastmasters (TM) to build my communication and leadership skills.

I created this introduction to Git for the Data Science team of Swiggy.

In the last two posts [1, 2], I had described the app I want for personal tracking and my progress on the app.

In the previous post, I had talked about the Quantified Self and how I started working on an app for that.

I’ve long since 2014 engaged in some form of Quantified Self.

A few months back, I was looking for some good Data Science podcasts, and the only sources I found were blog posts with reviews about a few podcasts.

I am reviewing the literature available on language identification for multilingual documents, focusing on Indic languages.

I am reviewing the literature available on language identification for multilingual documents, focusing on Indic languages.

I am reviewing the literature available on language identification for multilingual documents, focusing on Indic languages.

At the start of July 2020, I enrolled in a Coursera course on Design Thinking called Design Thinking for Innovation.

There are a lot of ways to maintain configuration in Python project.

At work, these days, I am building some new Python ML modules to be used within other projects.

After coding your flask app, when you do flask run, you get the following warning: WARNING: Do not use the development server in a production environment.

This will be my first post about cycling.

Recently, I realised that I have used majority of classical machine learning algorithms using scikit, but I haven’t actually implemented them myself.

This post is the next in series of my literature study of the Human Computation and Games With a Purpose field.

PyData (Bangalore) is a community of users and developers of all things data - Data Science, Data Engineering, Machine Learning, Deep Learning, Data Ethics, Visualization, etc.

I first read the terms Human Computation and Games with a Purpose while reading about crowd sourcing.

I’d like to verify using Gradient Descent, that given a perimeter value of a quadrilateral, square is the one with the maximum area.