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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Сеть:Computer Science and Programming Италия88 Технологии и приложения804...

📈 Аналитический обзор Telegram-канала Computer Science and Programming

Канал Computer Science and Programming (@computer_science_and_programming) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 140 479 подписчиков, занимая 804 место в категории Технологии и приложения и 88 место в регионе Италия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 140 479 подписчиков.

Согласно последним данным от 31 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -734, а за последние 24 часа — -18, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 7.96%. В первые 24 часа после публикации контент обычно набирает 1.94% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 11 177 просмотров. В течение первых суток публикация набирает 2 728 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 14.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как sellerflash, github, developer, pricing, waybienad.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“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...”

Благодаря высокой частоте обновлений (последние данные получены 01 сентября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

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140 479

Подписчики

-1824 часа

-2617 дней

-73430 день

11 177

Просмотры поста

~ 2 72824 часа

~ 3 75748 часов

7.96%

Коэффициент вовлеченности

Нет данных

Постов в день

Ads index

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Архив постов

140 452

DiffusionInst: Diffusion Model for Instance Segmentation * DiffusionInst is the first work of diffusion model for instance segmentation Github: https://github.com/chenhaoxing/DiffusionInst Paper: https://arxiv.org/abs/2212.02773v2 Getting started: https://github.com/chenhaoxing/DiffusionInst/blob/main/GETTING_STARTED.md Dataset: https://paperswithcode.com/dataset/lvis

140 452

Automatically find and fix errors in any ML datasets with cleanlab This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels during training. Github: https://github.com/cleanlab/cleanlab @computer_science_and_programming Docs: https://docs.cleanlab.ai/stable/index.html Examples: https://github.com/cleanlab/examples Paper: https://arxiv.org/abs/2211.13895v1

140 452

SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation The dataset consists of unlabeled patch triplets from 251079 locations across the globe, each patch covering 2640mx2640m and including 4 seasonal time stamps. Github: https://github.com/zhu-xlab/ssl4eo-s12 Paper: https://arxiv.org/abs/2211.07044v1 Dataset: https://mediatum.ub.tum.de/1660427 @computer_science_and_programming

140 452

You don't need to spend several $𝟭𝟬𝟬𝟬𝘀 to learn Data Science.❌ Stanford University, Harvard University & Massachusetts Institute of Technology is providing free courses.💥 Here's 8 free Courses that'll teach you better than the paid ones. 1. CS50’s Introduction to Artificial Intelligence with Python (Harvard) https://lnkd.in/d9CkkfGK 2. Data Science: Machine Learning (Harvard) https://lnkd.in/dQ7zkCv9 3. Artificial Intelligence (MIT) https://lnkd.in/dG5BCPen 4. Introduction to Computational Thinking and Data Science (MIT) https://lnkd.in/ddm5Ckk9 5. Machine Learning (MIT) https://lnkd.in/dJEjStCw 6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT) https://lnkd.in/dkpyt6qr 7. Statistical Learning (Stanford) https://lnkd.in/dymn4hbD 8. Mining Massive Data Sets (Stanford) 📍https://lnkd.in/d2uf-FkB

140 452

Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild Paper: https://arxiv.org/pdf/2207.10660.pdf Github: https://github.com/facebookresearch/omni3d Project page: https://garrickbrazil.com/omni3d/ @computer_science_and_programming

140 452

VToonify: Controllable High-Resolution Portrait Video Style Transfer Github: https://github.com/williamyang1991/vtoonify Colab code example https://colab.research.google.com/github/williamyang1991/VToonify/blob/master/notebooks/inference_playground.ipynb Paper: https://arxiv.org/pdf/2209.11224.pdf Dataset: https://paperswithcode.com/dataset/faceforensics-1 Video explanation: https://www.youtube.com/watch?v=0_OmVhDgYuY

140 452

VToonify: Controllable High-Resolution Portrait Video Style Transfer

140 452

Resources for performing deep learning on satellite imagery: - Techniques - Datasets - ML best Practice - Courses and more

140 452

Harvard CS109A #DataScience course materials — huge collection free & open! 1. Lecture notes 2. R code, #Python notebooks 3. Lab material 4. Advanced sections and more ... https://harvard-iacs.github.io/2019-CS109A/pages/materials.html @computer_science_and_programming

140 452

UFO: segmentation 140+ FPS 👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one! 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Unified framework for co-segmentation ✅Co-segmentation, co-saliency, saliency ✅Block for long-range dependencies ✅Able to reach for 140 FPS in inference ✅The new SOTA on multiple datasets Paper: https://arxiv.org/pdf/2203.04708v2.pdf Code: https://github.com/suyukun666/UFO

140 452

Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation. Github: https://github.com/164140757/scm Paper: https://arxiv.org/abs/2207.10447v1 Dataset: https://paperswithcode.com/dataset/cub-200-2011

140 452

🏎 Instance Shadow Detection with A Single-Stage Detector Deep framework, and an evaluation metric to approach this new task. Github: https://github.com/stevewongv/InstanceShadowDetection Instance Shadow Detection: https://github.com/stevewongv/SSIS Video: https://www.youtube.com/watch?v=p0b_2SsFypw Colab: https://colab.research.google.com/drive/1y9UpS5uA1YuoMyvYVzcKL4ltA_FDu_x0?usp=sharing Paper: https://arxiv.org/abs/2207.04614v1 Datasets: https://paperswithcode.com/dataset/soba @computer_science_and_programming

140 452

Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech IMS Toucan is a toolkit for teaching, training and using state-of-the-art Speech Synthesis models. Github: https://github.com/DigitalPhonetics/IMS-Toucan https://github.com/rballester/tntorch Pre-Generated Audios: https://multilingualtoucan.github.io/ Cloning prosody across speakers: https://toucanprosodycloningdemo.github.io/ Interactive Demo: https://huggingface.co/spaces/Flux9665/IMS-Toucan Paper: https://arxiv.org/abs/2206.12229v1 @computer_science_and_programming

140 452

CVPR 2022 open access https://openaccess.thecvf.com/CVPR2022?day=2022-06-21

140 452

MIT, Introduction to Deep Learning, 2022 Lecture series Website: http://introtodeeplearning.com/ Lecture: https://www.youtube.com/watch?v=7sB052Pz0sQ&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

140 452

Squeezeformer: An Efficient Transformer for Automatic Speech Recognition Github: https://github.com/kssteven418/squeezeformer Paper: https://arxiv.org/abs/2206.00888v1 Dataset: https://paperswithcode.com/dataset/librispeech

140 452

AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition Github: https://github.com/ShoufaChen/AdaptFormer Paper: https://arxiv.org/abs/2205.13535v1 Dataset: https://paperswithcode.com/dataset/something-something-v2

140 452

RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features (CVPR 2021) Paper: https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_RefineMask_Towards_High-Quality_Instance_Segmentation_With_Fine-Grained_Features_CVPR_2021_paper.pdf Source: https://github.com/zhanggang001/RefineMask @computer_science_and_programming

140 452

🧊 Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral) Github: https://github.com/dvlab-research/focalsconv Paper: https://arxiv.org/abs/2204.12463 Dataset: https://paperswithcode.com/dataset/nuscenes

140 452

NAFSSR: Stereo Image Super-Resolution Using NAFNet Github: https://github.com/megvii-research/NAFNet Paper: https://arxiv.org/abs/2204.08714v1 Demo: https://colab.research.google.com/drive/1dkO5AyktmBoWwxBwoKFUurIDn0m4qDXT?usp=sharing Dataset: https://paperswithcode.com/dataset/kitti