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 Italy87 Technologies & Applications816...

📈 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 142 711 subscribers, ranking 816 in the Technologies & Applications category and 87 in the Italy region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 142 711 subscribers.

According to the latest data from 15 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -1 289 over the last 30 days and by -46 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.44%. Within the first 24 hours after publication, content typically collects 1.85% reactions from the total number of subscribers.
Post reach: On average, each post receives 9 197 views. Within the first day, a publication typically gains 2 646 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 17.
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 16 June, 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.

142 711

Subscribers

-4624 hours

-2077 days

-1 28930 days

9 197

Post views

~ 2 64624 hours

~ 3 57748 hours

6.44%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

142 676

https://github.com/rwightman/pytorch-image-models?fbclid=IwAR0QNx9Hui3Tucr04-yR5RlSXF9ApTNcXbMAilZrDnhFDiTy5QduNQQjgqA

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PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more

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Detailed explanation at paper: 👇 https://arxiv.org/pdf/1905.10498.pdf and it's implementation and some results by using pytorch: 👇 https://github.com/facebookresearch/qmnist

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MNIST reborn, restored and expanded. Now with an extra 50,000 training samples. If you used the original MNIST test set more than a few times, chances are your models overfit the test set. Time to test them on those extra samples. Now you will use #QMNIST instead of #MNIST

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https://www.newworldai.com/

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website, which will provide you all up-to-date and necessary information on Artificial Intelligence, Machine Learning, Deep Learning and some brain activities. You will also find TED Talks, Lectures and academic writings on these issues.

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Speech2Face: Learning the Face behind a Voice #CVPR2019

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ArXiv: 👇 https://arxiv.org/abs/1905.09773 Project: 👇 https://speech2face.github.io

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Paper👇: https://arxiv.org/pdf/1905.08233.pdf Youtube video👇: https://www.youtube.com/watch?v=p1b5aiTrGzY&feature=youtu.be&fbclid=IwAR2Z1DgoIh_SdTuweiylm4L1aZpdSo8LV9v32XdivMcc3Q02mr7Qz1yUfwg

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Samsung AI's latest work on #GAN to animate realistic head model sequences is setting the internet on fire

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https://medium.com/deep-systems/supervisely-end-to-end-web-platform-for-deep-learning-and-computer-vision-eb8d239826d6?fbclid=IwAR1IAMJHDjLMIJOkj2GwoRzTsOkZmFm4LF62MedHXkrXmigzwr_m95ITIrU

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Supervisely: end-to-end web-platform for Deep Learning and Computer Vision

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https://www.adhiraiyan.org/deeplearning/02.00-Linear-Algebra?fbclid=IwAR1roZFCx4aHYDQfsbS47-su7VCajlQ_M9Z7ZHEatSx6xJI3CRUUoScwp2w

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Linear Algebra topics you need to understand Deep Learning with practical examples in Tensorflow 2.0.

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Machine learning is the main trend in IT and technologies .The most actual information about big data , machine learning , neural networks on channel : @ai_machinelearning_big_data

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Detailed description and transcription of talk: http://blog.ezyang.com/

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Pytorch internals from Edward Z.Yang. PyTorch NYC meetup on May 2019. Concepts: * Tensors, * Storage, * Strides, * Layouts, * Device, * Dtype, * Autograd ...

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Just another advance of #GAN generations. From Face to pix2pix

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computer age statistical inference.pdf8.11 MB

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Computer Age Statistical Inference - Algorithms, Evidence, & Data Science Table of Content: Part I Classic Statistical Inference 1 Algorithms and Inference 2 Frequentist Inference 3 Bayesian Inference 4 Fisherian Inference and Maximum Likelihood Estimation 5 Parametric Models and Exponential Families Part II Early Computer-Age Methods 6 Empirical Bayes 7 James–Stein Estimation and Ridge Regression 8 Generalized Linear Models and Regression Trees 9 Survival Analysis and the EM Algorithm 10 The Jackknife and the Bootstrap 11 Bootstrap Confidence Intervals 12 Cross-Validation and Cp Estimates of Prediction Error 13 Objective Bayes Inference and MCMC 14 Postwar Statistical Inference and Methodology Part III Twenty-First-Century Topics 15 Large-Scale Hypothesis Testing and FDRs 16 Sparse Modeling and the Lasso 17 Random Forests and Boosting 18 Neural Networks and Deep Learning 19 Support-Vector Machines and Kernel Methods 20 Inference After Model Selection 21 Empirical Bayes Estimation Strategies