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 ايطاليا87 فناوری و برنامه‌ها815...

📈 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 142 757 مشترک است و جایگاه 815 را در دسته فناوری و برنامه‌ها و رتبه 87 را در منطقه ايطاليا دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 142 757 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 13 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -1 316 و در ۲۴ ساعت گذشته برابر -26 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

وضعیت تأیید: تأیید نشده
نرخ تعامل (ER): میانگین تعامل مخاطب 6.13% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.79% واکنش نسبت به کل مشترکان کسب می‌کند.
دسترسی پست‌ها: هر پست به طور میانگین 8 753 بازدید دریافت می‌کند. در اولین روز معمولاً 2 559 بازدید جمع‌آوری می‌شود.
واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 17 است.
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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_sc...”

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 14 ژوئن, 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.

142 757

مشترکین

-2624 ساعت

-1847 روز

-1 31630 روز

8 753

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~ 2 55924 ساعت

~ 3 64348 ساعت

6.13%

نرخ مشارکت

~ 1

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142 757

🔗 Link:- https://apitester.org A fully free mobile API client for interacting with APIs straight from your phone. Doesn't it sound fantastic? API Tester allows you to connect to whatever type of API you're working with, including REST, gRPC, SOAP, and GraphQL. Constructing HTTP requests with parameters, auth details, and body data requires only a few steps with a simple and optimized UI. You can also create WebSocket connections, import collections, and use global variables. API Tester was developed by a team of enthusiasts who feel that powerful apps simplifying work is the key to progress. The app is constantly updated to ensure that you have all of the top-tier features. Try it out for yourself, rate and review on the App Store and Google Play.

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Cut and Learn for Unsupervised Object Detection and Instance Segmentation Cut-and-LEaRn (CutLER) is a simple approach for training object detection and instance segmentation models without human annotations. It outperforms previous SOTA by 2.7 times for AP50 and 2.6 times for AR on 11 benchmarks. Paper: https://arxiv.org/pdf/2301.11320.pdf Github: https://github.com/facebookresearch/CutLER Demo: https://colab.research.google.com/drive/1NgEyFHvOfuA2MZZnfNPWg1w5gSr3HOBb?usp=sharing 👉@computer_science_and_programming

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GLIGEN: Open-Set Grounded Text-to-Image Generation GLIGEN (Grounded-Language-to-Image Generation) a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs. Project page: https://gligen.github.io/ Paper: https://arxiv.org/abs/2301.07093 Github (coming soon): https://github.com/gligen/GLIGEN Demo: https://huggingface.co/spaces/gligen/demo 👉@computer_science_and_programming

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Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution BoxInstSeg is a toolbox that aims to provide state-of-the-art box-supervised instance segmentation algorithms. It supports instance segmentation with only box annotations. Github: https://github.com/LiWentomng/BoxInstSeg Paper: https://arxiv.org/pdf/2212.01579.pdf

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YOLOv8 is the newest state-of-the-art YOLO model that can be used for object detection, image classification, and instance segmentation tasks. YOLOv8 includes numerous architectural and developer experience changes and improvements over YOLOv5. Code: https://github.com/ultralytics/ultralytics What's New in YOLOv8 ? https://blog.roboflow.com/whats-new-in-yolov8/ Yolov8 Instance Segmentation (ONNX): https://github.com/ibaiGorordo/ONNX-YOLOv8-Instance-Segmentation

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MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications. 2023 lectures are starting in just one day, Jan 9th! http://introtodeeplearning.com MIT Introduction to Deep Learning The 2022 lectures can be found here: https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

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PACO: Parts and Attributes of Common Objects Sometimes object detection is not enough and you need more detail about object. Especially, when parts of objects is matters in your task. Then this dataset is for you from Facebook research team. PACO is a detection dataset that goes beyond traditional object boxes and masks and provides richer annotations such as part masks and attributes. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Ego4D) datasets. Paper: https://arxiv.org/pdf/2301.01795.pdf Github: https://github.com/facebookresearch/paco Visualization: https://github.com/facebookresearch/paco/tree/main/notebooks

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Happy New Year! In the new 2023 year, we wish a rapid increase in subscribers, high posts reach, high-quality active audience and, of course, happiness and health. A traditional present from us is a New Year card with your channel's this year results. See you in 2023, from TGSTAT team

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Accurate and Efficient Stereo Matching via Attention Concatenation Volume Stereo Depth Estimation Paper: https://arxiv.org/pdf/2209.12699.pdf Github: https://github.com/gangweiX/Fast-ACVNet Demo: https://www.youtube.com/watch?v=az4Z3dp72Zw ONNX: ONNX-FastACVNet-Stereo-Depth-Estimation @computer_science_and_programming

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DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients DeepLSD is a generic line detector that combines the robustness of deep learning with the accuracy of handcrafted detectors. It can be used to extract generic line segments from images in-the-wild, and is suitable for any task requiring high precision, such as homography estimation, visual localization, and 3D reconstruction. By predicting a line distance and angle fields, it can furthermore refine any existing line segments through an optimization Paper: https://arxiv.org/abs/2212.07766v1 Github: https://github.com/cvg/deeplsd Dataset: https://paperswithcode.com/dataset/hpatches

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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

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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

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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

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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

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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

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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

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VToonify: Controllable High-Resolution Portrait Video Style Transfer

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

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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