Rohit Pgarg @rohitpgarg

I tweet about Deep Learning/ML, and tech in general. Working as Data Scientist on recommendation systems at @glancescreen and @Roposolove. Tweets are my own. Joined April 2016

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1K
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548

Jay @thetrickytrade

6 months ago

One of the most underrated yet effective strategies for snagging premium cabin awards? Knowing EXACTLY when airlines release their seats. Let me show you how this works, using Singapore Airlines as an example... 🧵👇 🎁 DM for bonus content at the end.

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Rohit Pgarg @rohitpgarg

6 months ago

.@Swiggy @SwiggyCares You have stolen Rs 1532 for order 201939007131119. Did not give an option to cancel within 60 secs. Then customer support charged me the full amount for cancellation. Either revert the absurd cancellation charge, or give me my product back.

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Rohit Pgarg @rohitpgarg

6 months ago

.@ImagineApplePR Where's my order # 75050148411? It's been a week and no response from your side. Pathetic.

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Nick Dobos @NickADobos

9 months ago

Lmfao grok is worse at everything

AmebaGPT @amebagpt

9 months ago

Lmfao grok is worse at everything https://t.co/LieetqC5dH

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Rohit Pgarg @rohitpgarg

2 years ago

It’s been almost 24 hours, and still no response. Pathetic work, @zomato @zomatocare

Rohit Pgarg @rohitpgarg

2 years ago

It’s been almost 24 hours, and still no response. Pathetic work, @zomato @zomatocare

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Andrej Karpathy @karpathy

2 years ago

Random note on k-Nearest Neighbor lookups on embeddings: in my experience much better results can be obtained by training SVMs instead. Not too widely known. Short example: github.com/karpathy/rando… Works because SVM ranking considers the unique aspects of your query w.r.t. data.

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Rohit Pgarg @rohitpgarg

3 years ago

You haven’t learnt anything in your life.

Mark Tenenholtz @marktenenholtz

3 years ago

You haven’t learnt anything in your life.

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Radek Osmulski 🇺🇦 @radekosmulski

4 years ago

Curious about recommender models? Interested in endowing models from other domains with some of their superpowers? Please join me on a whirlwind tour of 6 recsys architectures! >> a thread 🧵 <<

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Rohit Pgarg @rohitpgarg

4 years ago

ImageNet is the new MNIST.

Aleksander Madry @aleks_madry

4 years ago

ImageNet is the new MNIST.

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muesli 🐧 @mueslix

4 years ago

The Alternative Big O notation: O(1) = O(yeah) O(log n) = O(nice) O(n) = O(k) O(n²) = O(my) O(2ⁿ) = O(no) O(n!) = O(mg!) O(n!*n) = O(rly?)

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Tanishq Mathew Abraham, Ph.D. @iScienceLuvr

4 years ago

Annotated PyTorch Paper Implementations by @labmlai is an AMAZING resource: • Deep learning papers explained in-depth with code side-by-side • Constantly updated with some of the latest papers! • 100% free and open-source! Check it out here → nn.labml.ai

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Rohit Pgarg @rohitpgarg

4 years ago

Hey @fernsnpetals, suffering from poor support. Order delivered partially with no rectification. No refund, no re-delivery. No response.

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Rohit Pgarg @rohitpgarg

4 years ago

Fourier features work in NLP too! Uber had a paper on DCT based features could replace lower layers of Resnet for image classification tasks. (limited) return of non-learned features!

Alexander Kolesnikov @kolesnikov

4 years ago

Fourier features work in NLP too! Uber had a paper on DCT based features could replace lower layers of Resnet for image classification tasks. (limited) return of non-learned features!

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Felice Antonio Merra @merrafelice

5 years ago

Very excited to share ELLIOT a Python Framework for #Reproducible #RecommenderSystems with more than 50 models, 36 metrics and many features to support easy-to-run experiments GitHub page: github.com/sisinflab/elli… #RecSys2021 #WSDM2021 #CIKM2021 #SIGIR202 #ECIR2021 (1/3)

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Eugene Yan @eugeneyan

5 years ago

How does real-time ML look in practice? We discuss real-time recommenders, drawing on my experience & industry papers: • When does it make sense? When does it not? • How have China & US companies implemented them? • How do we design & build an MVP? eugeneyan.com/writing/real-t…

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Rohit Pgarg @rohitpgarg

5 years ago

reuters.com/article/uk-int… "In June, it lost one of its veteran chip designers, Jim Keller, over a dispute on whether the company should outsource more of its production, sources said at the time." Interesting.

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Shreya Shankar @sh_reya

5 years ago

Unit testing for ML pipelines is challenging given changing data, features, models, etc. Changing I/O make it hard to have fixed unit tests. To hackily get around this, I liberally use assert statements in scheduled tasks. These have saved me so many times. Thread: (1/11)

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morgan @CasualEffects

5 years ago

@AmazingThew Something life changing I learned early at NVIDIA is that "putting something in hardware" (creating an application-specific integrated circuit, ASIC) usually does not make a program faster. (thread):