blog

Machine Learning Cheat Sheet

ML

Preparing for ML Breadth and Depth for Interviews

Effect of Class Names on VLM Object Detection

ML

CV

VLM

Exploring the “language” part of VLM (Florence-2) for object detection

Object Detection Random Baseline

ML

CV

Compare your performance with a random baseline.

Object Detection - A how-to guide

ML

CV

Basic operations in object detection task

Download CPCB CAAQM locations

Data

Download CPCB CAAQM locations using Selenium

Download CPCB live data

Data

Download CPCB data with selenium

Fundamentals across ML domains

ML

Knowledge transfer between ML domains

Non-Gaussian Likelihoods for MLPs

ML

Non-Gaussian Likelihoods for MLPs

Pruning vs Uncertainty

ML

Pruning vs Uncertainty

Bayesian Basis Regression

ML

Bayesian Basis Regression

How numpy handles day-to-day algebra?

NumPy, Mathematics

A deep dive into basic operations of numpy

Can Rank 1 GPs represent all GPs?

ML, GP

Download low-cost data from OpenAQ

ML, GP

A guide to download low-cost sensor data from OpenAQ

Climate Modeling with GPs

ML

Exploring the use of GPs for climate modeling

Multi-class classification with Gaussian Processes

ML, GP

Multi-class GP classification with different strategies

Climate Modeling with SIRENs

ML

Exploring the use of SIRENs for climate modeling

GNNs and GPs

ML

Exploring similarities between GNNs and GPs

Graph Neural Networks for Regression

ML

Challenges in using GNNs for regression using various strategies

Basis functions

ML

Exploring basis functions

Conditional Neural Processes for Image Interpolation

ML

Extreme Image Interpolation with Conditional Neural processes

Passwordless SSH setup for MacOS Hosts

macOS

A tiny handbook to setup passwordless ssh in MacOS

Sine Combination Networks

ML

Challenges in fitting to a combination of sine waves

Neural Network Gaussian Process

GP

ML

Exploring NTK kernels + GPJax with toy datasets

Stochastic Variational Gaussian processes in JAX

GP

A practical implementation of Hensman et al. 2015 from scratch in JAX

Multi-Output Gaussian Processes

ML

Exploring MOGPs from scratch

Gaussian Processes - A no-skip-math version

ML

End-to-end math derivations for Gaussian process regression and classification

Train NN with KFAC-Laplace in JAX

ML

Exploring KFAC-Laplace approximation on simple problems in JAX

Conditional Neural Processes in JAX

ML

Implementing conditional neural processes from scratch in JAX

JAX Optimizers

ML

Pros and cons of several jax optimizers.

Get a list of contributors from a repo

GitHub

Get contributors’ list using GitHub API and pandas

Iteratively reweighted least squares (IRLS) logistic regression

ML

Implementation of IRLS from Probabilistic ML book of Dr. Kevin Murphy and its comparison with naive second order implementation.

Gcloud cheatsheet

Gcloud

Most used commands while working with gcloud

GitHub Contrubuting FAQs

GitHub

This is a collection of FAQs/road-blocks/queries/issues I had over the past 2 years of engagement with GitHub.

Torch essentials

ML

Practical and direct introduction to PyTorch

Probabilistic Machine Learning

ML

A video lecture series from Prof. Philipp Hennig

Uncertainty in Deep Learning

ML

Review of PhD thesis of Dr. Yarin Gal

PyTorch Tips

ML

PyTorch zen tips

Conference Presentation Tips

Academic

Conference Presentation Tips

Comparing Gaussian Process Regression Frameworks

ML

A basic comparison among GPy, GPyTorch and TinyGP

Zeel B Patel, Harsh Patel, Shivam Sahni

Query by Committee

ML

A programming introduction to QBC with Random Forest Classifier.

KL divergence v/s cross-entropy

ML

Understanding KL divergence

Why .py files are better than .ipynb files for ML codebase

Python

Where .py files are better than .ipynb files?

Anonymization tips for double-blind submission

Academic

A last-minute help list

Input Warped GPs - A failed idea

ML

An idea of input warping GPs

SparseGPs in Stheno

A simple demo of sparse regression in stheno with VFE and FITC methods.

Docker Cheatsheet

Docker

Most used command while working with Docker

How to apply constraint on parameters in various GP libraries

Apply constraints in GPy, GPFlow, GPyTorch

Understanding Kernels in Gaussian Processes

ML

An exploratory analysis of kernels in GPs

Programatically download OpenAQ data

Programatically download OpenAQ data

Active Learning with Bayesian Linear Regression

ML

A programming introduction to Active Learning with Bayesian Linear Regression.

Zeel B Patel, Nipun Batra