Computer Science and Programming

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Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science

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Computer Science and Programming (@computer_science_and_programming) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 142 737 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 816-o'rinni va Italiya mintaqasida 87-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 142 737 obunachiga ega bo‘ldi.

14 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -1 292 ga, so‘nggi 24 soatda esa -44 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 6.29% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.82% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 8 976 marta ko‘riladi; birinchi sutkada odatda 2 595 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 17 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent sellerflash, github, developer, pricing, waybienad kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_sc...”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 15 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

142 737

Obunachilar

-4424 soatlar

-2007 kunlar

-1 29230 kunlar

8 976

Post ko'rishlar

~ 2 59524 soatlar

~ 3 64748 soatlar

6.29%

Muloqot nisbati

~ 1

Kuniga postlar

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beta

Postlar arxiv

142 711

Paper: http://proceedings.mlr.press/v78/dosovitskiy17a/dosovitskiy17a.pdf Code: https://github.com/carla-simulator/carla

142 711

CARLA: An Open Urban Driving Simulator Open-source simulator for autonomous driving

142 711

https://github.com/dsgiitr/graph_nets?fbclid=IwAR39aQbvMVhVxNCbQOoxkxdkYgnfd3tMuqzvWBqz048i8iE8Sjavcqbh56M

142 711

PyTorch Implementation and Explanation of Graph Representation Learning papers involving DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

142 711

Paper link, Blog and code are at their Github page: https://xbpeng.github.io/projects/DeepMimic/index.html?fbclid=IwAR3SaHAM-nii9UcpBJsWH-w7swuJudV9ouC7Ige8iuZDF0lbBy4ThchKhTo

142 711

DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills

142 711

Article: https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/ Github: https://github.com/microsoft/DeepSpeed

142 711

ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters

142 711

http://introtodeeplearning.com/

142 711

Course from MIT 6.S191 "Introduction to Deep Learning". Methods and applications in game play, medicine, language, art, computer vision, robotics and more

142 711

This video is synthetic and was created using deep learning

142 711

https://ai.facebook.com/blog/-introducing-pytorch3d-an-open-source-library-for-3d-deep-learning/

142 711

Introducing PyTorch3D: An open-source library for 3D deep learning. PyTorch3D: Faster, flexible 3D deep learning research

142 711

https://towardsdatascience.com/end-to-end-machine-learning-from-data-collection-to-deployment-ce74f51ca203 If you want to play with the app and see how it looks like: 💻Demo: https://www.reviews.ai2prod.com/ Code: https://github.com/MarwanDebbiche/post-tuto-deployment

142 711

End to End Machine Learning: From Data Collection to Deployment. - Collect and scrape data with Scrapy / Selenium - Train a deep character CNN for (English) sentiment analysis using PyTorch - Build an interactive web app with Dash to serve the model in real-time - Put everything in Docker Compose - Deploy to AWS on a custom domain name

142 711

More than 200 NLP datasets - this is gold (last update 21.01.202) https://quantumstat.com/dataset/dataset.html and also Google provided dataset search tool for publicly available datasets: https://datasetsearch.research.google.com/

142 711

Paper: https://arxiv.org/pdf/2001.05613.pdf Project page: http://www.ynl.t.u-tokyo.ac.jp/research/vmocap-syn/ Dataset will be available publicly soon

142 711

Synergetic Reconstruction from 2D Pose and 3D Motion for Wide-Space Multi-Person Video Motion Capture in the Wild

142 711

Paper pdf: https://arxiv.org/pdf/2001.05201.pdf Github: https://wywu.github.io/projects/EBT/EBT.html Youtube: https://youtu.be/tNPuAnvijQk

142 711

Everybody’s Talkin’: Let Me Talk as You Want This paper presents a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video.