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فریب کلاهبرداران را نخورید! تل‌متریو این کانال‌ها را شناسایی و برچسب‌گذاری می‌کند 👉 برای مشاهده برچسب، اشتراک تهیه کنید

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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📈 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 138 848 مشترک است و جایگاه 817 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 138 848 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 01 نوامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -923 و در ۲۴ ساعت گذشته برابر -43 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 5.49% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.72% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 7 618 بازدید دریافت می‌کند. در اولین روز معمولاً 2 394 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 02 نوامبر, 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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What is Kafka? Kafka is an open-source, distributed event streaming platform that serves as the central nervous system for data in modern enterprises. It's designed to handle real-time data feeds, process them efficiently, and make them available for a variety of applications in real-time. 🛠 Use Cases: - Real-time Analytics - Log Aggregation - Event Sourcing - Data Integration - Machine Learning Pipelines

Wondering how C++, Java, Python Work? 🔵 C++ C++ is like the superhero of programming languages. It's a compiled language, meaning your code is transformed into machine code that your computer can understand before it runs. This compilation process is crucial for efficiency and performance. C++ gives you precise control over memory and hardware, making it a top choice for systems programming and game development. It's like wielding a finely-tuned instrument in the world of code! 🎸💻 🔴 Java Java, on the other hand, is the coffee of programming languages. It's a compiled language too but with a twist. Java code is compiled into bytecode, which runs on the Java Virtual Machine (JVM). This bytecode can run on any platform with a compatible JVM, making Java highly portable and platform-independent. It's a bit like sending your code to a virtual coffee machine that serves it up just the way you like it on any OS! ☕️💼 🐍 Python Python is the friendly neighborhood programming language. It's an interpreted language, which means there's no compilation step. Python code is executed line by line by the Python interpreter. This simplicity makes it great for beginners and rapid development. Python's extensive library ecosystem and easy syntax make it feel like you're scripting magic spells in a magical world! 🪄🐍 In the end, the choice of programming language depends on your project's needs and your personal preferences. Each language has its strengths and weaknesses, but they all share the goal of bringing your ideas to life through code. 🚀💡 So, whether you're crafting the perfect C++ masterpiece, brewing up Java applications, or scripting Python magic, remember that programming languages are the tools that empower us to create amazing things in the digital realm. Embrace the language that speaks to you and keep coding! 💻🌟

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𝗛𝗼𝘄 𝘁𝗼 𝘁𝗲𝘀𝘁 𝘆𝗼𝘂𝗿 𝗔𝗣𝗜𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗩𝗶𝘀𝘂𝗮𝗹 𝗦𝘁𝘂𝗱𝗶𝗼 𝗖𝗼𝗱𝗲? You can immediately do this from your Visual Studio Code, as Postman just released a VS Code extension that integrates API building and testing into your code editor. What you can do with the extension: 🔹𝗦𝗲𝗻𝗱 (𝗺𝘂𝗹𝘁𝗶𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹) 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 🔹𝗦𝗲𝗻𝗱 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂𝗿 𝗵𝗶𝘀𝘁𝗼𝗿𝘆 🔹𝗨𝘀𝗲 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀 🔹𝗨𝘀𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 🔹𝗩𝗶𝗲𝘄 𝗮𝗻𝗱 𝗲𝗱𝗶𝘁 𝗰𝗼𝗼𝗸𝗶𝗲𝘀 ➡️Check it here

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80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains 📌 Agricul
80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains 📌 Agriculture and Food 📌 Medical and Healthcare 📌 Satellite 📌 Security and Surveillance 📌 ADAS and Self Driving Cars 📌 Retail and E-Commerce 📌 Wildlife Classification library https://github.com/Tessellate-Imaging/monk_v1 Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo Detection and Segmentation Library https://github.com/Tessellate-Imaging/ Monk_Object_Detection Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

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🔭 GRES: Generalized Referring Expression Segmentation New benchmark (GRES), which extends the classic RES to allow expressio
🔭 GRES: Generalized Referring Expression Segmentation New benchmark (GRES), which extends the classic RES to allow expressions to refer to an arbitrary number of target objects. 🖥 Github: https://github.com/henghuiding/ReLA ⏩ Paper: https://arxiv.org/abs/2306.00968 🔎 Project: https://henghuiding.github.io/GRES/ 📌 New dataset: https://github.com/henghuiding/gRefCOCO 👉 @computer_science_and_programming

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Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold Paper: https://arxiv.org/abs/2305.10973 Github: https://github.com/XingangPan/DragGAN Project page: https://vcai.mpi-inf.mpg.de/projects/DragGAN/ 👉 @computer_science_and_programming

Test of Time: Instilling Video-Language Models with a Sense of Time GPT-5 will likely have video abilities, but will it have a sense of time? Here is answer to this question in #CVPR2023 paper by student of University of Amsterdam to learn how to instil time into video-language foundation models. Paper: https://arxiv.org/abs/2301.02074 Code: https://github.com/bpiyush/TestOfTime Project Page: https://bpiyush.github.io/testoftime-website/ 👉 @computer_science_and_programming

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ViperGPT: Visual Inference via Python Execution for Reasoning ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query. Github: https://github.com/cvlab-columbia/viper Paper: https://arxiv.org/pdf/2303.08128.pdf Project: https://paperswithcode.com/dataset/beat 👉@computer_science_and_programming

Multivariate Probabilistic Time Series Forecasting with Informer Efficient transformer-based model for LSTF. Method introduce
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Multivariate Probabilistic Time Series Forecasting with Informer Efficient transformer-based model for LSTF. Method introduces a Probabilistic Attention mechanism to select the “active” queries rather than the “lazy” queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention. 🤗Hugging face: https://huggingface.co/blog/informer ⏩ Paper: https://huggingface.co/docs/transformers/main/en/model_doc/informer ⭐️ Colab: https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multivariate_informer.ipynb 💨 Dataset: https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classes#datasets.Dataset.set_transform 👉@computer_science_and_programming

Efficient Teacher: Semi-Supervised Object Detection for YOLOv5 ✅ Efficient Teacher introduces semi-supervised object detectio
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Efficient Teacher: Semi-Supervised Object Detection for YOLOv5 ✅ Efficient Teacher introduces semi-supervised object detection into practical applications, enabling users to obtain a strong generalization capability with only a small amount of labeled data and large amount of unlabeled data. ✅ Efficient Teacher provides category and custom uniform sampling, which can quickly improve the network performance in actual business scenarios. Paper: https://arxiv.org/abs/2302.07577 Github: https://github.com/AlibabaResearch/efficientteacher 👉@computer_science_and_programming