Pronod

Take fun seriously! Data is there for fun...

[Pronod @Pronod4]

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.
<https://t.co/kgWUVyXJrt>

[Pronod @Pronod4]

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

[Star Labs @starlabsltd]

LAST CHANCE - Early Bird offer ends Sunday 🐦

Save a massive 25% on the fanless Byte Mk II today!

Configure your #coreboot Mini PC Now: https://t.co/qoMT7nGAkR

[Star Labs @starlabsltd]

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
🔎 https://t.co/f9HzPkKIKn

[Pronod @Pronod4]

🤖 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🚀https://t.co/4KfbWMhbrP

[Pronod @Pronod4]

Finding the Balance: Global Functions, the Global Keyword, and Energy-Efficiency in Python
{ by Pronod Bharatiya } from @hashnode https://t.co/H99KDJjedd

[Pronod @Pronod4]

How to use Python dict() in energy efficient way - Some useful tips
{ by Pronod Bharatiya } from @hashnode https://t.co/a2YjMEBgC2
#python #energyefficiency #Coding #carbonfootprint

[Pronod @Pronod4]

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 https://t.co/kfhZtWf2oo

[Pronod @Pronod4]

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.
https://t.co/HIpQtW1gR9

[Pronod @Pronod4]

#OpenML contains over 4k #AI ready datasets for reproducible #MachineLearning. Supported by
@TUeindhoven
,
@NWONieuws
and
@AmazonScience

website: https://t.co/M8eQ5pkiUH

#ArtificialIntelligence #Artificial_Intelligence #MachineLearning #ML

[Pronod @Pronod4]

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

[Pronod @Pronod4]

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

[Pronod @Pronod4]

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

[Pronod @Pronod4]

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

[Pronod @Pronod4]

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"

[Gabriel Peyré @gabrielpeyre]

A paper I enjoyed reading recently: @julienmairal "Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning" https://t.co/HfCiJ6San6 A very nice and clear exposition of Majorization-Minimization algorithms.

[Saskia Freytag @SaskiaFreytag]

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: https://t.co/VsiHX2KYdT

[Kevin Markham @justmarkham]

🐼🤹‍♂️ pandas trick:

Want to explore a new dataset without too much work?

1. Pick one:
➡️ pip install pandas-profiling
➡️ conda install -c conda-forge pandas-profiling

2. import pandas_profiling
3. df.profile_report()
4. 🥳

See example 👇

#Python #DataScience #pandastricks

[Python Software Foundation @ThePSF]

Python Web Conf - August 23, 2019 - a virtual event that features international tech experts presenting on 24 topics such as microservices, identity management, machine learning & CI/CD. https://t.co/acyFQblP1k. Post-event recordings available exclusively to registered attendees.

[Daniel Holden @anorangeduck]

Results from our new paper on data-driven physics simulation for games. Produces 300 to 4000 times speedup over standard physics simulations by combining Machine Learning, subspace simulation, and lots of training data! https://t.co/1MeYXnLlUp