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@cs2smeshnyavka: чуток свалился #csmemes #мем #dogdrop #кс2 #ксмемы

cs2smeshnyavka
cs2smeshnyavka
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Thursday 17 September 2026 21:54:54 GMT
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learikon
learikon :
dogg
2026-09-18 02:20:42
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prokuren18
прокурен18 :
редко сочно упал
2026-09-17 22:05:36
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Cảnh báo nách có mùi bánh 🤣 #review #skincare #bodycare #viral #dove
Cảnh báo nách có mùi bánh 🤣 #review #skincare #bodycare #viral #dove
This interview question was actually asked during an interview for a data science position at Google. Although simple, it shows how important solid fundamentals are for ML/AI jobs nowadays. First, what is a confusion matrix? It’s a 4 cell matrix used for binary classification, with Prediction true/false as columns, and actual true/false as rows. We denote these as TP, FN, FP, and TN. Precision is the ratio of true positives to predicted positives (factoring in FP, TP + FP). We optimize for precision if false positives are costly (spam). Recall is the ratio of true positives to actual positives (factoring in FN, TP + FN). We optimize for recall when false negatives are costly. F1 Score is the harmonic mean of both. it’s = 2 · (Precision · Recall) / (Precision + Recall). It balances both. These metrics are more descriptive than accuracy, and are used as additional metric to prevent being misled. This is especially true for imbalanced datasets where a model can achieve high accuracy while still performing poorly on the class that matters most. Learn AI, simply. Join 9000+ Others in our Visual AI Newsletter. Get your weekly research breakdown (link in bio 🔗). #datascience #machinelearning #deeplearning #coding #math
This interview question was actually asked during an interview for a data science position at Google. Although simple, it shows how important solid fundamentals are for ML/AI jobs nowadays. First, what is a confusion matrix? It’s a 4 cell matrix used for binary classification, with Prediction true/false as columns, and actual true/false as rows. We denote these as TP, FN, FP, and TN. Precision is the ratio of true positives to predicted positives (factoring in FP, TP + FP). We optimize for precision if false positives are costly (spam). Recall is the ratio of true positives to actual positives (factoring in FN, TP + FN). We optimize for recall when false negatives are costly. F1 Score is the harmonic mean of both. it’s = 2 · (Precision · Recall) / (Precision + Recall). It balances both. These metrics are more descriptive than accuracy, and are used as additional metric to prevent being misled. This is especially true for imbalanced datasets where a model can achieve high accuracy while still performing poorly on the class that matters most. Learn AI, simply. Join 9000+ Others in our Visual AI Newsletter. Get your weekly research breakdown (link in bio 🔗). #datascience #machinelearning #deeplearning #coding #math
How to rely to “HOW ARE YOU?” 🇬🇧 #EduTok #edutoklanguage #edutokenglish #englishclass #englishtrick #englishtips #englishlesson #speakinenglish #eng
How to rely to “HOW ARE YOU?” 🇬🇧 #EduTok #edutoklanguage #edutokenglish #englishclass #englishtrick #englishtips #englishlesson #speakinenglish #eng


Replying to @gloriadiaz4492 Fui a trabajar enferma#yosoynemimar #nemimarortiz #GRACIAS #trabajo#comedia#😂😂😂😎
Replying to @gloriadiaz4492 Fui a trabajar enferma#yosoynemimar #nemimarortiz #GRACIAS #trabajo#comedia#😂😂😂😎
He deserves a raise 🫡❤️#foreignxprime #kickstreamer #ragebait #funny
He deserves a raise 🫡❤️#foreignxprime #kickstreamer #ragebait #funny

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