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 276 Technologies & Applications326...

📈 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 293 260 subscribers, ranking 326 in the Technologies & Applications category and 1 276 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 293 260 subscribers.

According to the latest data from 04 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 366 over the last 30 days and by -131 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.35%. Within the first 24 hours after publication, content typically collects 5.62% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 569 views. Within the first day, a publication typically gains 16 480 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 168.
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 05 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.

293 260

Subscribers

-13124 hours

-1 4647 days

-6 36630 days

21 569

Post views

~ 16 48024 hours

~ 18 29048 hours

7.35%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

293 272

Introducing Hypothesis GU Funcs, an Open Source Python Package for Unit Testing https://eng.uber.com/hypothesis-gu-funcs-unit-testing/ Hypothesis General Universal Function Documentation https://hypothesis-gufunc.readthedocs.io/en/latest/

293 272

RecSim: A Configurable Simulation Platform for Recommender Systems https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html article: https://arxiv.org/abs/1909.04847 github: https://github.com/google-research/recsim

293 272

DeepFovea: Using deep learning for foveated reconstruction in AR/VR https://ai.facebook.com/blog/deepfovea-using-deep-learning-for-foveated-reconstruction-in-ar-vr/ code: https://github.com/facebookresearch/DeepFovea full paper: https://research.fb.com/publications/deepfovea-neural-reconstruction-for-foveated-rendering-and-video-compression-using-learned-statistics-of-natural-videos/ @ai_machinelearning_big_data

293 272

What Does Stochastic Mean in Machine Learning? https://machinelearningmastery.com/stochastic-in-machine-learning/

293 272

Machine Learning with Python Cookbook — Chris Albon (en) 2018 @datascienceiot

293 272

Stacked Capsule Autoencoders https://github.com/google-research/google-research/tree/master/stacked_capsule_autoencoders paper : https://arxiv.org/abs/1906.06818 http://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.html

293 272

How to Connect Model Input Data With Predictions for Machine Learning https://machinelearningmastery.com/how-to-connect-model-input-data-with-predictions-for-machine-learning/

293 272

Sharing our Experience Upgrading OpenNMT to TensorFlow 2.0 https://blog.tensorflow.org/2019/11/our-experience-upgrading-OpenNMT-to-TensorFlow.html code: https://github.com/OpenNMT/OpenNMT-tf OpenNMT: http://opennmt.net/

293 272

Sberbank's subsidiary Cloud Technologies (provides cloud services under the SberCloud brand) showed the most powerful russian supercomputer Christofari. Power of the supercomputer is 6.67 penaflops (about 6.7 quadrillion operations per second). So Christofari be in the TOP-30 of the world rating.Access will be available for all AI Cloud subscribers. The cost of usage per min on a full power - 5750 RUB (about $90).

293 272

Research Guide: Pruning Techniques for Neural Networks https://heartbeat.fritz.ai/research-guide-pruning-techniques-for-neural-networks-d9b8440ab10d

293 272

Introducing the Next Generation of On-Device Vision Models: MobileNetV3 and MobileNetEdgeTPU http://ai.googleblog.com/2019/11/introducing-next-generation-on-device.html

293 272

Sparse Networks from Scratch: Faster Training without Losing Performance https://arxiv.org/abs/1907.04840 https://timdettmers.com/2019/07/11/sparse-networks-from-scratch/ Sparse Learning Library and Sparse Momentum Resources https://github.com/TimDettmers/sparse_learning

293 272

How to Save a NumPy Array to File for Machine Learning https://machinelearningmastery.com/how-to-save-a-numpy-array-to-file-for-machine-learning/

293 272

Green AI vs Red AI https://arxiv.org/abs/1907.10597 Tackling Climate Change with Machine Learning https://www.reddit.com/r/MachineLearning/comments/da30mv/r_tackling_climate_change_with_machine_learning/

293 272

A Multimodal Language Dataset for Understanding Humor article: https://arxiv.org/pdf/1904.06618.pdf dataset: https://github.com/ROC-HCI/UR-FUNNY

293 272

14 Different Types of Learning in Machine Learning https://machinelearningmastery.com/types-of-learning-in-machine-learning/

293 272

Освойте самую востребованную технологию искусственного интеллекта! Хотите в сжатые сроки получить практические навыки по программированию глубоких нейронных сетей? В SkillFactory в ноябре стартует курс онлайн-курс "Deep Learning и нейронные сети" https://clc.to/_dCORw (при поддержке NVIDIA Corporation). Здесь вы: попробуете свои силы в создании нейронной сети для распознавания рукописных цифр, обучении рекурентной сети задачам прогнозирования временных рядов, разработке нейросетевого чат-бота, создании модели для идентификации лиц и др. Курс основан на практике. Фокус и упор мы делаем не на математическом фундаменте, а именно на понимании задач и практическом применении решений. Узнайте больше о возможностях: https://clc.to/_dCORw

293 272

A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning https://machinelearningmastery.com/maximum-a-posteriori-estimation/

293 272

Numerical Computing with Python @datascienceiot

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This project is adapted from the original Dive Into Deep Learning book https://github.com/dsgiitr/d2l-pytorch