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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Red:📈 Análisis del canal de Telegram Computer Science and Programming
El canal Computer Science and Programming (@computer_science_and_programming) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 138 878 suscriptores, ocupando la posición 816 en la categoría Tecnologías y Aplicaciones y el puesto 88 en la región Italia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 138 878 suscriptores.
Según los últimos datos del 31 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -883, y en las últimas 24 horas de -66, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.88%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.72% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 10 942 visualizaciones. En el primer día suele acumular 2 393 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 25.
- Intereses temáticos: El contenido se centra en temas clave como sellerflash, github, developer, pricing, waybienad.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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...”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 01 noviembre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
138 878
Suscriptores
-6624 horas
-4227 días
-88330 días
Archivo de publicaciones
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! 💻🌟
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
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
🔭 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
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
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
+1Multivariate 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
+2Efficient 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
