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 142 667 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 813-o'rinni va Italiya mintaqasida 86-o'rinni egallagan.

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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.

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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.
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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

9 197

Post ko'rishlar

~ 2 64624 soatlar

~ 3 57748 soatlar

6.44%

Muloqot nisbati

~ 1

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Postlar arxiv

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https://medium.com/ibm-watson/watson-studio-desktop-is-now-free-for-academia-f8b50a118370

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Watson Studio Desktop is now free for academia. All products in your charge for free: * Watson Studio Cloud * Watson Studio Local * Watson studio Desktop Just visit, register as a student or faculty, varify your account, install and enjoy with service. You'll get detailed information in below medium link

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Main site: https://chokkan.github.io/deeplearning/ Github repo: https://github.com/chokkan/deeplearning

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Introduction to Deep learning with flavor of Natural Language Processing(NLP) Course (Tokyo Institue of Technology) materials, demos and implementations are available. Enjoy with DL. Happy learning

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computervisionnews-february2019.pdf3.13 MB

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Computer Vision news magazine RSIP vision. February 2019. CV Application, Challenges, Projects

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Rules of Machine Learning: Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google 👆

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rules_of_ml.pdf4.49 KB

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https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215?fbclid=IwAR0MVWaTWiPO_dcYvfj3Y73H4S7hwcgEBtUlyshKYyuaGklwP7YHOaMImj0

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You are deep learning enthusiast and Covolutions are unseperable part of your projects. In this tutorial given comprehensive guideline all about convolutions: -> Convolution v.s. Cross-correlation -> Convolution in Deep Learning (single channel version, multi-channel version) -> 3D Convolution -> 1 x 1 Convolution -> Convolution Arithmetic -> Transposed Convolution (Deconvolution, checkerboard artifacts) -> Dilated Convolution (Atrous Convolution) -> Separable Convolution (Spatially Separable Convolution, Depthwise Convolution) -> Flattened Convolution -> Grouped Convolution -> Shuffled Grouped Convolution -> Pointwise Grouped Convolution

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https://github.com/kmario23/deep-learning-drizzle?fbclid=IwAR0Q9BtyvfFUApwIIHoWZm6sYx1V78--aUXSpbheMAMO9Yab6vQWEiNzTCU

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Deep Learning Drizzle Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these selected and exciting lectures!! GitHub by Marimuthu Kalimuthu

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Impractical Python Project.pdf24.87 MB

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Github repository: https://github.com/rlvaugh/Impractical_Python_Projects

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Impractical PythonProjects by Lee Vaughan 2018

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https://mlwhiz.com/blog/2018/12/17/text_classification/?utm_source=telegram.me&utm_medium=social&utm_campaign=mdtrue-silenttruethe-best-of-gan-pape&utm_content=25847208

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What Kagglers are mostly using for Text Classification?

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https://paperswithcode.com/sota

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This is a super cool resource: Papers With Code now includes 950+ ML tasks, 500+ evaluation tables (including SOTA results) and 8500+ papers with code. Probably the largest collection of NLP tasks I've seen including 140+ tasks and 100 datasets.

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Prediction based algorithms in infographics. Type, name, description, advantages and disadvantages