Pronod @Pronod4

@iitm_bs

🤖 Navigating #ML #DL environments? #VirtualEnvironmentsvs. #Containers: Which one suits you? Dive into the debate and discover the pros and cons of each approach! 📊🔍 #AI #DeepLearning #TechDebate #NvidiaEnroot #EnergyEfficiency #MLOp🚀data-intelligence.hashnode.dev/navigating-mac…

IITMadras and NSM Nodal Center are organizing an HPC Research Week. Six days of research talks on Aerospace, Biology, Chemical, Computers, Mathematical, and Mechanics. from November 20-25. <linkedin.com/feed/update/ur…>

@Cleartrip You can do the same with much cheaper linux based laptops and home made instant black coffee!!!

LAST CHANCE - Early Bird offer ends Sunday 🐦 Save a massive 25% on the fanless Byte Mk II today! Configure your #coreboot Mini PC Now: starlabs.systems/products/byte

Combining versatility and performance, the StarLite Mk V offers the best of both worlds in a single device. Featuring: 🔸#Intel N200 fanless processor 🔸16GB 4800MHz LPDDR5 RAM 🔸Detatchable keyboard with 6 layouts 🔸Powered by #coreboot 🔸0.9kg weight 🔎 starlabs.systems/products/starl…

Finding the Balance: Global Functions, the Global Keyword, and Energy-Efficiency in Python { by Pronod Bharatiya } from @hashnode data-intelligence.hashnode.dev/finding-the-ba…

How to use Python dict() in energy efficient way - Some useful tips { by Pronod Bharatiya } from @hashnode data-intelligence.hashnode.dev/how-to-use-pyt… #python #energyefficiency #Coding #carbonfootprint

Wrote what I learned about train-test-split for #ml pipeline - Are train-test splits for model evaluation in the ML pipeline must? { by Pronod Bharatiya } from @hashnode #artificialintelligence #machinelearning data-intelligence.hashnode.dev/are-train-test…

The Cathedral and the Bazaar (@ericsraymond ; 1997) explains the differences between #proprietary and #OpenSource development methodologies. Many of the concepts like #Agile development and #DevOps, we use today were accepted in 1997. en.wikipedia.org/wiki/The_Cathe…

#OpenML contains over 4k #AI ready datasets for reproducible #MachineLearning. Supported by @TUeindhoven , @NWONieuws and @AmazonScience website: openml.org #ArtificialIntelligence #Artificial_Intelligence #MachineLearning #ML

Even when you want to store results that you are not interested in, then use " _ =func() ". The output of the func() will be stored in the underscore variable. In python underscore ("_") variables are known as "garbage variables". #Python #Pythonprogramming #ML #MachineLearning

in #ArtificialIntelligence context -- #data to #Knowledge -- what #researchers are focusing on #Knowledge to #Action -- what #innovators are focusing on at last, it's not a zero-sum game for #researchers and #innovators and hence not a win-win situation for both of them

Convert a cell from markdown to cell → Select mode + "y" Selecting several cells → Select mode + Shift + arrow up/down

Add a cell below the current cell → Select mode + "b" Add a cell above the current cell → Select mode + "a" Delete a cell → Select mode + "dd" Cut/Copy/Paste → Select mode + "x"/"c"/"v" Convert a cell from code to markdown → Select mode + "m"

Simple Jupyter-Notebook Shortcuts Run one cell and go to the next one → Shift + Enter Run one cell and stay on the same cell → Ctrl + Enter Go from Edit mode (Green cell) to Select mode (Blue cell) → Escape Go from Select mode (Blue cell) to Edit mode (Green cell) → Enter

A paper I enjoyed reading recently: @julienmairal "Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning" lear.inrialpes.fr/people/mairal/… A very nice and clear exposition of Majorization-Minimization algorithms.

So I wrote a tutorial on dimension reductions in #rstats. It has actually turned out to be fairly comprehensive. It uses a fun example dataset on cereals (🎉 - not looking for at iris) I would love some feedback: rpubs.com/Saskia/520216