Ramsha Khan @geekyramsha
Curious mind into ML/DL | Mathematics | Exploring RL khan-ramsha.github.io Mumbai, Maharashtra Joined December 2023-
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Following149
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I'm in love with punjabi songs!! They don't play, they heal✨
About to do something impossible today- tryna complete entire semester work in one day!
My mind in a rickshaw stuck in bandra's heavy traffic: "i should definitely try patience = 2 for Reduce on LR Plateau scheduler and about a dozen other experiments at the same time." that urge to try things out when you don't have your laptop around :)
Matrix multiplication is now a multi-trillion dollar business.
every girl looks so pretty in their navratri fits, i'm obsessed with all the makeup looks popping up on my feed and also the garba dance!!!!💃
every girl looks so pretty in their navratri fits, i'm obsessed with all the makeup looks popping up on my feed and also the garba dance!!!!💃
i'm sometimes really good at prompting and sometimes really bad, depends on my mood :)
the sole purpose of life can’t just be getting a job, mannnnn. noooooo wayyyyyyyyy.
And this is where my fingers stopped scrolling and my eyes got stuck on the cuteness🥹
And this is where my fingers stopped scrolling and my eyes got stuck on the cuteness🥹
Traditional fits hits different. And then there's kurti with jhumkas 🥹 Haayee, desi look vich feel hogayi divine, Lagdi har scene vich mai heroine ✨✨
Traditional fits hits different. And then there's kurti with jhumkas 🥹 Haayee, desi look vich feel hogayi divine, Lagdi har scene vich mai heroine ✨✨
1.2MB & 0.26M params achieving SOTA is insane - most competitors need 8-16M parameters! This thing runs 592*592 imgs in 1 second on basic CPU while others need fancy GPUs. Code not open-sourced yet but I coded it up!!! 🔗github.com/Khan-Ramsha/Co…
1.2MB & 0.26M params achieving SOTA is insane - most competitors need 8-16M parameters! This thing runs 592*592 imgs in 1 second on basic CPU while others need fancy GPUs. Code not open-sourced yet but I coded it up!!! 🔗github.com/Khan-Ramsha/Co…
Compound Loss function -> Binary Cross Entropy + Matthews Correlation Coefficient (with weights). BCE focuses on pixel level details while MCC (using all 4 from confusion matrix) makes sure whole segmentation holds up and is super useful in handling class imbalance.
Compound Loss function -> Binary Cross Entropy + Matthews Correlation Coefficient (with weights). BCE focuses on pixel level details while MCC (using all 4 from confusion matrix) makes sure whole segmentation holds up and is super useful in handling class imbalance. https://t.co/NREOjeVkUL
"For you" section on X feels so useless, all i get to see are videos of people doing kalesh or some random entertainment! I'd rather pick instagram for better entertainment stuff equals to doomscrolling and time waste, helll nahhh!! not there either, I've learned to ignore insta.
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