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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شبکه:Computer Science and Programming ايطاليا88 فناوری و برنامه‌ها804...

📈 تحلیل کانال تلگرام Computer Science and Programming

کانال Computer Science and Programming (@computer_science_and_programming) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 140 441 مشترک است و جایگاه 804 را در دسته فناوری و برنامه‌ها و رتبه 88 را در منطقه ايطاليا دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 140 441 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 31 اوت, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -734 و در ۲۴ ساعت گذشته برابر -18 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

وضعیت تأیید: تأیید نشده
نرخ تعامل (ER): میانگین تعامل مخاطب 7.96% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.94% واکنش نسبت به کل مشترکان کسب می‌کند.
دسترسی پست‌ها: هر پست به طور میانگین 11 177 بازدید دریافت می‌کند. در اولین روز معمولاً 2 728 بازدید جمع‌آوری می‌شود.
واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 14 است.
علایق موضوعی: محتوا بر موضوعات کلیدی مانند sellerflash, github, developer, pricing, waybienad تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
“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...”

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 01 سپتامبر, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامه‌ها تبدیل کرده‌اند.

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140 441

مشترکین

-1824 ساعت

-2617 روز

-73430 روز

11 177

نمایش های پست

~ 2 72824 ساعت

~ 3 75748 ساعت

7.96%

نرخ مشارکت

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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