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

Network:Computer Science and Programming Italy88 Technologies & Applications804...

📈 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 140 441 subscribers, ranking 804 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 140 441 subscribers.

According to the latest data from 31 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -734 over the last 30 days and by -18 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.96%. Within the first 24 hours after publication, content typically collects 1.94% reactions from the total number of subscribers.
Post reach: On average, each post receives 11 177 views. Within the first day, a publication typically gains 2 728 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 01 September, 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.

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140 441

Subscribers

-1824 hours

-2617 days

-73430 days

11 177

Post views

~ 2 72824 hours

~ 3 75748 hours

7.96%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

140 441

💬 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/

140 441

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

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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

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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

140 441

✨ 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

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140 441

Happy new year Thank you for being with us We appreciate your patience to science and always try to provide best content for subscribers

140 441

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

140 441

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

140 441

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

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Object-aware cropping, a simple, fast and highly effective data augmentation alternative to random scene cropping for SELF-SUPERVISED LEARNING

140 441

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

140 441

PoolFormer: MetaFormer is Actually What You Need for Vision

140 441

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

140 441

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

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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

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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

140 441

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/

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Code: https://github.com/gist-ailab/uoais#unseen-object-amodal-instance-segmentation-uoais Paper: https://arxiv.org/abs/2109.11103 Dataset: https://paperswithcode.com/dataset/ocid Project page: https://sites.google.com/view/uoais 👉@computer_science_and_programming

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