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

292 752

How to Calculate Precision, Recall, F1, and More for Deep Learning Models https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/

292 752

The Startups Disrupting Retail at The New Retail Conference by Sistema_VC Face recognition for retail, AI-driven windows and stocks, personalised offer for each customer in the store as if they were shopping online. Register to know how all these functions in the modern shops. Among speakers there are founders of successful startups from USA, Israel, UK. Place: Moscow, Tablica co-working, Novoslobodskaya 16. Date: April 3rd, 6 pm Register free: https://goo.gl/2zc5Nw

292 752

BMSG-GAN https://github.com/akanimax/BMSG-GAN

292 752

Unifying Physics and Deep Learning with TossingBot http://ai.googleblog.com/2019/03/unifying-physics-and-deep-learning-with.html

292 752

How to Evaluate Pixel Scaling Methods for Image Classification With Convolutional Neural Networks https://machinelearningmastery.com/how-to-evaluate-pixel-scaling-methods-for-image-classification/

292 752

Variational inference for Bayesian neural networks https://krasserm.github.io/2019/03/14/bayesian-neural-networks/

292 752

Simulated Policy Learning in Video Models http://ai.googleblog.com/2019/03/simulated-policy-learning-in-video.html

292 752

Deep Learning Explainability: Hints from Physics https://towardsdatascience.com/deep-learning-explainability-hints-from-physics-2f316dc07727

292 752

Machine Learning Mind Map https://www.thelearningmachine.ai/ml

292 752

Deep Learning Examples https://developer.nvidia.com/deep-learning-examples

292 752

6.883 Science of Deep Learning: Bridging Theory and Practice -- Spring 2018 https://people.csail.mit.edu/madry/6.883/

292 752

Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction and Logistics https://www.youtube.com/watch?v=PySo_6S4ZAg

292 752

MIT 6.S191: Visualization for Machine Learning (Google Brain) https://www.youtube.com/watch?v=ulLx2iPTIcs

292 752

Программа математики давно пройдена, но пробелы в знаниях все еще тормозят проф.рост? Пройдите обучение на курсе "Математика и статистика для Data Science" и получите возможность уверенно решать нетиповые задачи. Во время обучения вы на примере увидите, как знание математики и статистики работает в решении реальных жизненных задач в области анализа данных, прогнозирования и оптимизации. Забронируйте место на курсе сегодня и получите скидку 20% на обучение → http://bit.ly/2HF8ES0

292 752

How to Load and Manipulate Images for Deep Learning in Python With PIL/Pillow https://machinelearningmastery.com/how-to-load-and-manipulate-images-for-deep-learning-in-python-with-pil-pillow/

292 752

8 Excellent Pretrained Models to get you Started with Natural Language Processing (NLP) https://www.analyticsvidhya.com/blog/2019/03/pretrained-models-get-started-nlp/

292 752

Adaptive - and Cyclical Learning Rates using PyTorch The Learning Rate (LR) is one of the key parameters to tune. Using PyTorch, we’ll check how the common ones hold up against CLR! https://medium.com/@thomas_dehaene/adaptive-and-cyclical-learning-rates-using-pytorch-2bf904d18dee

292 752

Stanford Convolutional Neural Networks for Visual Recognition Course (Review) https://machinelearningmastery.com/stanford-convolutional-neural-networks-for-visual-recognition-course-review/

292 752

Measuring the Limits of Data Parallel Training for Neural Networks http://ai.googleblog.com/2019/03/measuring-limits-of-data-parallel.html

292 752

A Complete Exploratory Data Analysis and Visualization for Text Data https://towardsdatascience.com/a-complete-exploratory-data-analysis-and-visualization-for-text-data-29fb1b96fb6a