Computer Science and Programming
Открыть в Telegram
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
Больше
Сеть:📈 Аналитический обзор Telegram-канала Computer Science and Programming
Канал Computer Science and Programming (@computer_science_and_programming) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 140 441 подписчиков, занимая 804 место в категории Технологии и приложения и 88 место в регионе Италия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 140 441 подписчиков.
Согласно последним данным от 31 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -734, а за последние 24 часа — -18, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 7.96%. В первые 24 часа после публикации контент обычно набирает 1.94% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 11 177 просмотров. В течение первых суток публикация набирает 2 728 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 14.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как sellerflash, github, developer, pricing, waybienad.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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...”
Благодаря высокой частоте обновлений (последние данные получены 01 сентября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
140 441
Подписчики
-1824 часа
-2617 дней
-73430 день
Архив постов
💬 A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution
Github: https://github.com/mjq11302010044/tatt
Paper: https://arxiv.org/abs/2203.09388v2
Dataset: https://deepchecks.com/blog/
Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.
https://towardsdatascience.com/ai-papers-to-read-in-2022-c6edd4302247
A lightweight vision library for performing large scale object detection & instance segmentation
Github: https://github.com/obss/sahi
Paper: https://arxiv.org/abs/2202.06934v1
Kaggle notebook: https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx
Dataset: https://paperswithcode.com/dataset/xview
👉👉@computer_science_and_programming
323+ Open Source Pytorch Implementation Software Projects
Free and open source pytorch implementation code projects including engines, APIs, generators, and tools.
https://opensourcelibs.com/libs/pytorch-implementation
A curated list of tutorials, papers, projects, communities and more related to PyTorch:
https://www.ritchieng.com/the-incredible-pytorch/
https://github.com/ritchieng/the-incredible-pytorch
@computer_science_and_programming
✨ Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning
Github: https://github.com/sense-x/uniformer
Paper: https://arxiv.org/abs/2201.04676v1
Tasks: https://paperswithcode.com/dataset/kinetics-600
@computer_science_and_programming
An important collection of the 15 best machine learning cheat sheets.
1- Supervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
2- Unsupervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
3- Deep Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
4- Machine Learning Tips and Tricks
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
5- Probabilities and Statistics
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
6- Comprehensive Stanford Master Cheat Sheet
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf
7- Linear Algebra and Calculus
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
8- Data Science Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf
9- Keras Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf
10- Deep Learning with Keras Cheat Sheet
https://github.com/rstudio/cheatsheets/raw/master/keras.pdf
11- Visual Guide to Neural Network Infrastructures
http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png
12- Skicit-Learn Python Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
13- Scikit-learn Cheat Sheet: Choosing the Right Estimator
https://scikit-learn.org/stable/tutorial/machine_learning_map/
14- Tensorflow Cheat Sheet
https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf
15- Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
@computer_science_and_programming
Happy new year
Thank you for being with us
We appreciate your patience to science and always try to provide best content for subscribers
Page: https://d2l.ai/
PyTorch based: https://d2l.ai/d2l-en-pytorch.pdf
MXNET based: https://d2l.ai/d2l-en.pdf
Github: https://github.com/d2l-ai/d2l-en
👉👉@computer_science_and_programming
Dive into Deep Learning
Interactive deep learning book with code, math, and discussions
Implemented with NumPy/MXNet, PyTorch, and TensorFlow
Adopted at 300 universities from 55 countries
OBJECT-AWARE CROPPING FOR SELF-SUPERVISED LEARNING
Paper:
https://arxiv.org/pdf/2112.00319v1.pdf
Github:
https://github.com/shlokk/object-cropping-ssl
👉👉@computer_science_and_programming
Object-aware cropping, a simple, fast and highly effective data augmentation alternative to random scene cropping for SELF-SUPERVISED LEARNING
Another state-of-the-art archtecture for Vision tasks:
Github: https://github.com/sail-sg/poolformer
Paper: https://arxiv.org/abs/2111.11418
Datasets: ImageNet, COCO, Ade20k
Colab: https://colab.research.google.com/github/sail-sg/poolformer/blob/main/misc/poolformer_demo.ipynb
👉👉@computer_science_and_programming
ESPnet: end-to-end text-to-speech processing toolkit
ESPnet2-TTS: Extending the Edge of TTS Research
Github: https://github.com/espnet/espnet
Docs: https://espnet.github.io/espnet/
Paper: https://arxiv.org/abs/2110.07840v1
Dataset: https://paperswithcode.com/dataset/vctk
One of the best reference book is definately "Deep Learning with Python" (1st edition) by François Chollet (creator of Keras)
Deep Learning with Python (2nd edition) has been released with 500 pages of code examples, theory, context, practical tips...
Book:
https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras
For online reading:
https://livebook.manning.com/book/deep-learning-with-python-second-edition/chapter-1/
Jupyter notebooks on Github:
https://github.com/fchollet/deep-learning-with-python-notebooks
👉👉@computer_science_and_programming
Under review as a conference paper at ICLR 2022
8-BIT OPTIMIZERS VIA BLOCK-WISE QUANTIZATION
Paper:
https://arxiv.org/abs/2110.02861
Github:
https://github.com/facebookresearch/bitsandbytes
Video:
https://www.youtube.com/watch?v=IxrlHAJtqKE
Documentation:
https://bitsandbytes.readthedocs.io/en/latest/
👉@computer_science_and_programming
8-bit optimizers – a replacement for regular optimizers. 🚀, 75% less memory, same with upwards trend, no hyperparam tuning needed Input symbol for numbers: #Lightweight, #LessMemory
PASS: Pictures without humAns for Self-Supervised Pretraining
PASS is a large-scale image dataset that does not include any humans, human parts, or other personally identifiable information
Github
https://github.com/yukimasano/PASS
Paper
https://arxiv.org/abs/2109.13228v1
Dataset
https://paperswithcode.com/dataset/pass
Documentation
https://www.robots.ox.ac.uk/~vgg/research/pass/
