Machinelearning

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Погружаемся в машинное обучение и Data Science Показываем как запускать любые LLm на пальцах. По всем вопросам - @haarrp @itchannels_telegram -🔥best channels Реестр РКН: clck.ru/3Fmqri

Network:Machinelearning Russia1 291 Technologies & Applications328...

📈 Analytical overview of Telegram channel Machinelearning

Channel Machinelearning (@ai_machinelearning_big_data) in the Russian language segment is an active participant. Currently, the community unites 292 747 subscribers, ranking 328 in the Technologies & Applications category and 1 291 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 747 subscribers.

According to the latest data from 07 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 317 over the last 30 days and by -209 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.45%. Within the first 24 hours after publication, content typically collects 5.46% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 817 views. Within the first day, a publication typically gains 15 977 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 160.
Thematic interests: Content is focused on key topics such as openai, claude, api, gemini, контекст.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Погружаемся в машинное обучение и Data Science Показываем как запускать любые LLm на пальцах. По всем вопросам - @haarrp @itchannels_telegram -🔥best channels Реестр РКН: clck.ru/3Fmqri”

Thanks to the high frequency of updates (latest data received on 08 July, 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.

292 747

Subscribers

-20924 hours

-1 3687 days

-6 31730 days

21 817

Post views

~ 15 97724 hours

~ 17 79548 hours

7.45%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

292 759

Deep Reinforcement Learning with TensorFlow 2.0 http://inoryy.com/post/tensorflow2-deep-reinforcement-learning/

292 759

A Visual Exploration of Gaussian Processes https://distill.pub/2019/visual-exploration-gaussian-processes/

292 759

Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI https://www.youtube.com/watch?v=XR8YSRcuVLE

292 759

New Google Brain Optimizer Reduces BERT Pre-Training Time From Days to Minutes https://medium.com/syncedreview/new-google-brain-optimizer-reduces-bert-pre-training-time-from-days-to-minutes-b454e54eda1d

292 759

A Gentle Introduction to Channels First and Channels Last Image Formats for Deep Learning https://machinelearningmastery.com/a-gentle-introduction-to-channels-first-and-channels-last-image-formats-for-deep-learning/

292 759

Who Will Win the Game of Thrones? Data visualization tool, had a little fun visualizing the probability of each main character ending up on the Iron Throne, calculated using the latest odds published on the betting site Bovada. https://towardsdatascience.com/who-will-win-the-game-of-thrones-fbde8434c94b

292 759

Build XGBoost / LightGBM models on large datasets — what are the possible solutions? https://towardsdatascience.com/build-xgboost-lightgbm-models-on-large-datasets-what-are-the-possible-solutions-bf882da2c27d

292 759

Capturing Special Video Moments with Google Photos http://ai.googleblog.com/2019/04/capturing-special-video-moments-with.html

292 759

Stanford CS230: Deep Learning lectures : https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb https://www.youtube.com/watch?v=PySo_6S4ZAg&list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb&index=2&t=44s

292 759

Using Deep Learning to Improve Usability on Mobile Devices http://ai.googleblog.com/2019/04/using-deep-learning-to-improve.html

292 759

Open-sourcing PyTorch-BigGraph for faster embeddings of extremely large graphs https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/ https://github.com/facebookresearch/PyTorch-BigGraph

292 759

Word2vec в картинках https://habr.com/ru/post/446530/

292 759

Beautiful Gooey Simulations, Now 10 Times Faster https://www.youtube.com/watch?v=-jL2o_15s1E

292 759

Protecting networks against adversial attacks https://medium.com/@lj94sm/protecting-networks-against-adversial-attacks-7fd3e2be15af

292 759

How to Load, Convert, and Save Images With the Keras API https://machinelearningmastery.com/how-to-load-convert-and-save-images-with-the-keras-api/

292 759

CS234: Reinforcement Learning Winter 2019 playlist : https://www.youtube.com/watch?v=FgzM3zpZ55o&list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u course: http://web.stanford.edu/class/cs234/index.html

292 759

NeuroSAT: An AI That Learned Solving Logic Problems https://www.youtube.com/watch?v=luwP75lPExo

292 759

Deep Learning and SAR Applications https://towardsdatascience.com/deep-learning-and-sar-applications-81ba1a319def

292 759

From Attention in Transformers to Dynamic Routing in Capsule Nets https://staff.fnwi.uva.nl/s.abnar/?p=108

292 759

Машинное обучение проникло во все сферы; теперь заказчики поголовно хотят использовать его возможности в своих проектах. Приходится срочно осваивать методы machine learning и обучение нейросетей, но самому разобраться в многообразии информации бывает сложно. Многие платные и бесплатные курсы страдают излишней академичностью и по месяцу рассказывают о теории, прежде чем перейти наконец к практике. В Skillfactory вместе с опытными data scientist’ами проводят курс “Практический Machine Learning” http://bit.ly/2GRmdyn , в котором студенты с первого занятия начинают обучать модели машинного обучения, от простого к более сложному, попутно разбираясь в теории. За 11 недель в интенсивном темпе участники решают все типовые задачи машинного обучения, осваивают методы оценки и оптимизации, участвуют в 2 соревнованиях на kaggle и знакомятся с основами нейросетей. Кстати, сегодня - последний день распродаж в Skillfactory, успейте забронировать место на курсе со скидкой 30%!