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

Red:Computer Science and Programming Italia87 Tecnologías y Aplicaciones816...

📈 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 142 737 suscriptores, ocupando la posición 816 en la categoría Tecnologías y Aplicaciones y el puesto 87 en la región Italia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 142 737 suscriptores.

Según los últimos datos del 14 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -1 292, y en las últimas 24 horas de -44, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.29%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.82% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 8 976 visualizaciones. En el primer día suele acumular 2 595 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 17.
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 15 junio, 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.

142 737

Suscriptores

-4424 horas

-2007 días

-1 29230 días

8 976

Visitas de la publicación

~ 2 59524 horas

~ 3 64748 horas

6.29%

Tasa de compromiso

~ 1

Mensajes por día

Ads index

beta

Archivo de publicaciones

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List of top 200 deep learning Github repositories sorted by the number of stars.

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Rotated Binary Neural Network Github (Pytorch implementation): https://github.com/lmbxmu/RBNN Paper: https://arxiv.org/abs/2009.13055

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Binary Neural Network (BNN) is best feet for reducing the complexity of deep neural networks. But, it suffers severe performance degradation. Rotation based training leads to around 50% weight flips which maximize the information gain and showed state-of-the-arts in benchmark datasets Rotated Binary Neural Network (RBNN)

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https://github.com/brinesoftwares/Flutter-AI-Rubik-cube-Solver?fbclid=IwAR0agon7_lmFDOoBeT3ru9skWvTer3syzLFz_vHDZy9isC3FzWTJJ3JufsE

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AI based Rubik's Cube Solver using Flutter and Python

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https://www.nature.com/articles/s41586-020-2649-2

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NumPy provides an easily readable, expressive, high-level API for array programming. It takes care of the underlying mechanics that make operations fast.

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https://dafriedman97.github.io/mlbook/content/table_of_contents.html And The list of Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources. (Last Update: Sept 9, 2020): https://www.marktechpost.com/free-resources/?fbclid=IwAR0hc2qkxPMXhQGzsg07ffgFecRr01tSCRqlhb_XMR6PjPt1KNdy68cLy9w

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Here is a new, and free book on Machine Learning from scratch. It includes the math and code examples. Solid reference.

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Organize the daily influx of ML content in meaningful ways without feeling overwhelmed, By Goku Mohandas et al. : https://madewithml.com/collections/

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Brian Huge, Antoine Savine : https://arxiv.org/abs/2005.02347 TF1 implementation notebook on Colab: https://colab.research.google.com/github/differential-machine-learning/notebooks/blob/master/DifferentialML.ipynb

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Differential Machine Learning

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FREE PDF download: https://d2l.ai/d2l-en.pdf Download the book in 'notebook' format to read and execute locally: https://d2l.ai/chapter_installation/index.html Website: https://d2l.ai Github: https://github.com/d2l-ai/d2l-en Article: https://www.amazon.science/latest-news/amazon-scientists-author-popular-deep-learning-book

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Dive Into Deep Learning August 2020 and FREE version!!! D2L is the 987-page book that Amazon scientists have compiled over the past two years and has finally been completed... an interactive and ' open source book ' with code, math and discussions. What makes this book unique is that it was created with Jupyter Notebook and with the idea of ′′ Learning with Practice "... that is, the book in its entirety consists of executable code with adaptations in PyTorch, TensorFlow and MXNet.

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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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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

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Blog: https://towardsdatascience.com/pp-yolo-surpasses-yolov4-object-detection-advances-1efc2692aa62 Github: https://github.com/PaddlePaddle/PaddleDetection

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Baidu publishes PP-YOLO and pushes the state of the art in object detection research.

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Tackled the problem of defining a perturbation set for real-world perturbations which cannot be easily described with a set of equations. Paper: https://arxiv.org/abs/2007.08450 Blog post: https://locuslab.github.io/2020-07-20-perturbation/ Code: https://github.com/locuslab/perturbation_learning

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Learning perturbation sets for robust machine learning