DeCoder

Bizda hammasi tayyor! Alloh manfaatli qilsin! Videoga olib keyin kanalga joylaymiz)

Workshopimiz 15:00’da boshlanadi! Barchani kutib qolaman. Sizlarni ajoyib sovg’alar ham kutadi(Bu hozircha sir😉)

Kelganlar darmonda, kelmaganlar armonda😁

#Environment #gdg Tashkiliy ishlar avjida

#ioextended #speakers And the final batch of Google I/O Extended Tashkent: Web Edition '22 speakers is here! Meet speakers from the Mobile, Design, and ML tracks! 📫 You can find the full agenda here: https://bit.ly/ioweb-agenda 👀 Date: May 29, 2022 🕙 Time: 09:00 📍 Venue: Inha University in Tashkent 🎟 Get your FREE ticket here: https://gdg.community.dev/events/details/google-gdg-tashkent-presents-io-extended-tashkent-web-edition-22/ See you at Google I/O Extended Tashkent: Web Edition '22! ⠀ @gdgtashkent

Reinforcement Learning for everyone mavzusida kichik masterclass bo’ladi hammani taklif qilaman😁 Qo’llab turasizlar degan umiddaman!

🔥 Google I/O Extended Tashkent: Web Edition '22 is here! The magic of I/O doesn’t end after the main event. That's why every year GDG Tashkent hosts the I/O Extended event, which brings together local developers to talk about their favorite announcements from I/O and all the new technologies. And this year, we are hosting the brand new Web Edition of Google I/O Extended! Join us this Sunday to meet dozens of developers, designers, founders of startups, and other representatives of the local IT community. We will have presentations and workshops on the topics of Angular, Firebase, React, NodeJS, Flutter, Dart, Kotlin, Swift, Git, UX Design, and others. Don't miss your chance to be part of one of the biggest tech events in Tashkent! 👀 Date: May 29, 2022 🕙 Time: 09:00 📍 Venue: Inha University in Tashkent 🎟 Get your FREE ticket here: https://gdg.community.dev/events/details/google-gdg-tashkent-presents-io-extended-tashkent-web-edition-22/ @gdgtashkent

Bir kuni Netflixdagi boshlig'im ofisimga kelib, meni oyligimni oshirishini aytdi. Men Netflixga endigina kirgan edim, shuning uchun oyligim oshishini kutmagan edim. "Yaxshi, ammo nega?", - deb savol berdim. Uning javobi nima uchun Netflixda ishlashni yoqtirishimga yaqqol misol bo'ldi. "Biz jamoaga yangi Senior dasturchi olib keldik va u sening oyligingdan ko'proq oylik so'radi. U bilan teng bo'lishing uchun maoshingni oshiryabmiz." Agar yangi ishchilar bilan shartnoma imzolash uchun ko'proq pul to'layotgan bo'lsangiz, demak bozor shuni talab qiladi va boshqa ishchilaringizni oyligini tenglashtirishingiz kerak. Sadoqatli xodimlarni maoshin bozordan ortda qolishiga yo'l qo'yib, siz bilan qolganligi uchun ularni jazolamang. P.S: Mana nima uchun maqasdlarimdan biri Netflixga ishga kirish. Sizning maqsadingizdagi kompaniya qaysi? @mabrur_dev

Shunqa holatlar ham bo’lib turadi😁

Shunaqa oynani ochib qo’yib mazza qilib o’tiradiganlar, sizlar TOPsizlar! 😅😅😅

Aslida ham shunaqa Juda ajoyib tasvirlashibdi.

https://t.me/AdamSaidov?livestream Biz boshladik!

💥Boooom Light weight va high accuracyni sevuvchilar uchun: State-of-the-art 2D and 3D Face Analysis Project InsightFace - PyTorch va MXNet asosida qurilgan face analysis toolbox Bu juda kuchli model hisoblanadi va juda ko’p pretrain modellar va tollarni birlashtira olgan open source project hisoblanadi. Developer Kitlar uchun yoki istalgan IoT’larda qo’llash uchun eng zo’r variant hisoblanadi. Robototexnika va IoTlar bilan shug’ullanayotganlar uchun tavsiya. Link: https://github.com/deepinsight/insightface

MTCNN Face Recognition modellar ichida men eng ko’p foydalanadigan model bu MTCNN hisoblanadi. Bundagi aniqlik darajasi yuqoriligi va tezligi menga doim yoqqan, juda ko’p projectlarda shuni ishlatilganiga guvoh bo’lganman va o’zim ham ishlataman. CPU va GPUda ham muammosiz ishlashi menga ko’proq yoqadi. Tensorflow asosida qurilgan versiyasini: https://github.com/ipazc/mtcnn Quyidagi link orqali sinab ko’rishingiz mumkin. Yaqinda bunga oid bir project ham bo’ladi(buyog’i hozircha sir😉)

Neural 3D Scene Reconstruction with the Manhattan-world Assumption Bu turli rasmlar yordamida rasmni 3D modelini yaratish hisoblanadi. Yaqinda shu bo’yicha NVIDIA kompaniyasining postini ko’rib qolgandim. Bu bo’yicha quyidagi datasetlar va pretrain modellar orqali tajriba qilib o’z modelingizni yaratishingiz mumkin. Paper: https://arxiv.org/pdf/2205.02836v1.pdf Datasets: ScanNet, 7-Scenes Project: https://github.com/zju3dv/manhattan_sdf P/s: Shunga o’xshash projectlarni commentda qoldirsangiz birga tahlil qilamiz)

Assalyamu aleykum everyone! Have great news, we are planning to do voice chat with Bexruz Nutfilloyev (@DeCoder_uz) and @maroonbells, who are really experienced engineers in AI. So the main topic of discussion will be how to start a path in AI and how to land your first job without experience. Date: 15 th of May at 6 pm (GMT+5) Voice chat will be in Uzbek!!! @AdamSaidov