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Computer Science and Programming

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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📈 Telegram kanali Computer Science and Programming analitikasi

Computer Science and Programming (@computer_science_and_programming) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 142 667 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 813-o'rinni va Italiya mintaqasida 86-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 142 667 obunachiga ega bo‘ldi.

15 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -1 289 ga, so‘nggi 24 soatda esa -46 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 6.44% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.85% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 9 197 marta ko‘riladi; birinchi sutkada odatda 2 646 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 16 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.

142 667
Obunachilar
-4624 soatlar
-2077 kunlar
-1 28930 kunlar
Postlar arxiv
Machine Learning Cheatsheet. Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.

Papers with codes, which published in top conferences and sorted by stars. Read the paper and play with code. This repository is continuous progress and weekly update

NLP_2018_Highlights.pdf2.96 MB

NLP 2018 Highlights By Elvis Saravia. Summary of all the biggest NLP stories, state-of-the-art results and new interesting research directions of the year coming from both academia and the industry

A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning

Wonderfully interactive, gentle, and well done introduction to probability and statistics. Walk through this with your favorite kid and give them a head-start in life on ML https://seeing-theory.brown.edu/basic-probability/index.html

Some important discussion and effective learning method from specialists. I'll highly recommend to read this greate article

The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)

MIT Deep Learning courses list from scholars and video tutorials, lectures series

Cheatsheets for each machine learning field and ultimate complition of concepts from Stanford CS. Updated (2018) and in pdf version

TensorSpace is a neural network 3D visualization framework Built on TensorFlow.js, Three.js and Tween.js. Better understandin
TensorSpace is a neural network 3D visualization framework Built on TensorFlow.js, Three.js and Tween.js. Better understanding and imagination of deep learning with visualization