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 & Applications811...

📈 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 140 414 subscribers, ranking 811 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 140 414 subscribers.

According to the latest data from 02 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -753 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 8.00%. Within the first 24 hours after publication, content typically collects 2.00% reactions from the total number of subscribers.
Post reach: On average, each post receives 11 237 views. Within the first day, a publication typically gains 2 812 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 03 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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140 414

Subscribers

-124 hours

-2397 days

-75330 days

11 237

Post views

~ 2 81224 hours

~ 3 85648 hours

8.00%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

140 426

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

140 426

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

140 426

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

140 426

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

140 426

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

140 426

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

140 426

computer age statistical inference.pdf8.11 MB

140 426

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

140 426

https://sites.google.com/view/berkeley-cs294-158-sp19/home

140 426

Thrilled to be teaching a new course on Deep Unsupervised Learning with Peterxichen (ImprovedGAN, InfoGAN, PixelCNN++, VLAE, PixelSNAIL, Flow++), Hojonathanho (Flow++, GAIL), Aravind(Flow++): * Lectures * Homeworks

140 426

https://www.youtube.com/watch?v=lyRPyRKHO8M

140 426

Google IO 2019 keynote. Learn about the latest product and platform innovations at Google in a Keynote Several improvements are introduced this year in AI + Software + Hardware integration. 👇

140 426

https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ

140 426

Great Tensorflow tutorial Series from Hvass Laboratories, which one of the most dominating Deep Learning Framework with practical examples

140 426

https://sites.google.com/view/safeml-iclr2019/accepted-papers?fbclid=IwAR18gdqKNSE6Vao23HIc6J0zZ_ViACoHjr-zJ5OkpK1iIqR2154wly3RxPQ

140 426

SafeML ICLR 2019 Workshop accepted papers list. Read and explore new horizons of Machine Learning

140 426

Youtube Video Lectures: 👇 https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS Course Lecture Notes: 👇 http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/

140 426

I highly recommend the Cornell University's "Machine Learning for Intelligent Systems (CS4780/ CS5780)" course taught by Associate Professor Kilian Q. Weinberger.

140 426

https://bentoml.com/posts/2019-04-19-one-model/

140 426

"One Model to Rule Them All" Christoph Molnar. Some experienced toughts how to work effectively with your Machine Learning model(project)