Computer Science and Programming
Open in 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
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Network:📈 Analytical overview of Telegram channel Computer Science and Programming
Channel Computer Science and Programming (@computer_science_and_programming) in the English language segment is an active participant. Currently, the community unites 138 848 subscribers, ranking 817 in the Technologies & Applications category and 88 in the Italy region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 138 848 subscribers.
According to the latest data from 01 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -923 over the last 30 days and by -43 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.49%. Within the first 24 hours after publication, content typically collects 1.72% reactions from the total number of subscribers.
- Post reach: On average, each post receives 7 618 views. Within the first day, a publication typically gains 2 394 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 14.
- Thematic interests: Content is focused on key topics such as sellerflash, github, developer, pricing, waybienad.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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...”
Thanks to the high frequency of updates (latest data received on 02 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
138 848
Subscribers
-4324 hours
-4127 days
-92330 days
Posts Archive
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/
