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 283 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 293 306 subscribers, ranking 326 in the Technologies & Applications category and 1 283 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 293 306 subscribers.

According to the latest data from 03 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 469 over the last 30 days and by -218 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.32%. Within the first 24 hours after publication, content typically collects 5.77% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 487 views. Within the first day, a publication typically gains 16 937 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 169.
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 04 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.

293 306

Subscribers

-21824 hours

-1 5287 days

-6 46930 days

21 487

Post views

~ 16 93724 hours

~ 18 77448 hours

7.32%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

293 289

A Gentle Introduction to the Bayes Optimal Classifier https://machinelearningmastery.com/bayes-optimal-classifier/ https://svivek.com/teaching/machine-learning/lectures/slides/prob-learning/bayes-optimal-classifier.pdf

293 289

AR-Net: A simple autoregressive neural network for time series https://ai.facebook.com/blog/ar-net-a-simple-autoregressive-neural-network-for-time-series/ full paper: https://arxiv.org/abs/1911.12436

293 289

Check the Data Science channel where you can find a lot of articles, links and commentaries on them. Join and learn hot topics of data science @opendatascience

293 289

📋 Introducing TensorBoard.dev: a new way to share your ML experiment results https://blog.tensorflow.org/2019/12/introducing-tensorboarddev-new-way-to.html article : https://arxiv.org/abs/1910.10683 example: https://tensorboard.dev/experiment/EvNO346lT0iYbmeaWmoNCQ/#scalars

293 289

nbdev: use Jupyter Notebooks for everything https://www.fast.ai//2019/12/02/nbdev/ github: https://github.com/fastai/nbdev/

293 289

A Gentle Introduction to Model Selection for Machine Learning https://machinelearningmastery.com/a-gentle-introduction-to-model-selection-for-machine-learning/

293 289

Python for Probability, Statistics, and Machine Learning @ai_machinelearning_big_data

293 289

Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares https://web.stanford.edu/~boyd/vmls/ @ai_machinelearning_big_data

293 289

High-Throughput, Contact-Free Detection of Atrial Fibrillation From Video With Deep Learning https://jamanetwork.com/journals/jamacardiology/fullarticle/2756246

293 289

Прими участие в бесплатном интенсиве по Data Science и освой Machine Learning с нуля. Регистрируйся прямо сейчас: 👉 https://clc.to/wLZYUQ. В результате ты: ✔️ познакомишься с основами программирования на Python; ✔️ научишься работать с библиотеками Matplotlib и Scikit-Learn; ✔️ узнаешь, как грамотно выгружать и визуализировать данные; ✔️ сможешь обучать модели; ✔️ создашь свой первый проект. 🎁 Все участники, которые дойдут до конца, получат премиум-доступ на месяц к приложению для изучения английского от партнёра EnglishDom!

293 289

Optimizing agent behavior over long time scales by transporting value https://github.com/deepmind/deepmind-research/tree/master/tvt https://github.com/deepmind/tvt article: https://arxiv.org/abs/1810.06721

293 289

How to Use an Empirical Distribution Function in Python https://machinelearningmastery.com/empirical-distribution-function-in-python/

293 289

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

293 289

Introducing LIGHT: A multiplayer text adventure game for dialogue research https://ai.facebook.com/blog/introducing-light-a-multiplayer-text-adventure-game-for-dialogue-research/ Learning in Interactive Games with Humans and Text https://parl.ai/projects/light/ ParlAI Quick-start http://parl.ai.s3-website.us-east-2.amazonaws.com/docs/tutorial_quick.html

293 289

Identifying Exoplanets with Neural Networks https://blog.tensorflow.org/2019/11/identifying-exoplanets-with-neural.html code: https://github.com/aedattilo/models_K2/tree/master/research/astronet paper: https://arxiv.org/pdf/1903.10507.pdf

293 289

Understanding the generalization of ‘lottery tickets’ in neural networks https://ai.facebook.com/blog/understanding-the-generalization-of-lottery-tickets-in-neural-networks/ One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers https://arxiv.org/pdf/1906.02773.pdf https://arxiv.org/pdf/1906.02768.pdf

293 289

🔥 Fire and smoke detection with Keras and Deep Learning https://www.pyimagesearch.com/2019/11/18/fire-and-smoke-detection-with-keras-and-deep-learning/

293 289

Continual Unsupervised Representation Learning Paper: https://arxiv.org/abs/1910.14481 Code: https://github.com/deepmind/deepmind-research/tree/master/curl

293 289

Handtrack.js: tracking hand interactions in the browser using Tensorflow.js and 3 lines of code https://blog.tensorflow.org/2019/11/handtrackjs-tracking-hand-interactions.html github: https://github.com/victordibia/handtrack.js/ dataset: http://vision.soic.indiana.edu/projects/egohands/

293 289

3 Ways to Encode Categorical Variables for Deep Learning https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python/