Triplet loss is a way to train models so that similar things end up close together and different things end up far apart. You feed the model three examples at a time: - an anchor (the reference) - a positive (something that should be similar to the anchor) - a negative (something that should be different) The loss pushes the anchor and positive closer in the embedding space while pushing the anchor and negative farther apart, usually by at least some margin. It’s commonly used in: - face recognition - image retrieval - recommendation systems - any task where you care about similarity rather than exact class labels The basic idea is simple: don’t just learn to classify. Learn a space where distance means something useful.

Linkedin you sicko

X users in India. Please be careful. X is showing subscription amount in ₹ for Plus and in $ for Ultra. This is so devious. Someone might look at the number and choose ultra thinking it's cheaper. @X fix it.

Accepted cookies on a website to download free movies

How life was before I started moving my money from FD to SIPs #StockMarket

Financial stability

They thought 2048 bit encryption actually meant using the digits 2,0,4,8 as private keys 😭

No. of clicks: 3 Private key: 4 emudhra's global standards 😭

We can be friends if you know who he is and loved his lectures.

Dodged a JIRA ticket

HTC had a separate fan base for the kinds of Android phones it used to churn out. Especially the HTC desire series, it was outta this world. #Android