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 281 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 293 457 subscribers, ranking 326 in the Technologies & Applications category and 1 281 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 293 457 subscribers.

According to the latest data from 02 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 464 over the last 30 days and by -249 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.49%. Within the first 24 hours after publication, content typically collects 5.71% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 989 views. Within the first day, a publication typically gains 16 765 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 173.
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 03 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.

293 457

Subscribers

-24924 hours

-1 5267 days

-6 46430 days

21 989

Post views

~ 16 76524 hours

~ 18 67648 hours

7.49%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

293 421

PyTorch framework for cryptographically secure random number generation, torchcsprng, now available https://pytorch.org/blog/torchcsprng-release-blog/

293 421

Уже в этот четверг в 19:00 (27 августа)🗓 состоится митап, на котором вы погрузитесь в загадочный мир интересов продвинутых экспертов, помимо Data Science: 👉 О чём с детства мечтал Евгений Виноградов, руководитель отдела разработки хранилищ данных (Яндекс.Деньги)? 👉 Чем занимается в свободное от работы время datascientist и алгоритмический трейдер Алекс Леков? 🔥Регистрация на Meetup здесь

293 421

Building a Neural Network to Predict Loan Risk https://tymick.me/blog/loan-risk-neural-network Github: https://github.com/tywmick/loan-risk-neural-network @ai_machinelearning_big_data

293 421

Introducing Semantic Reactor: Explore NLP in Google Sheets https://blog.tensorflow.org/2020/08/introducing-semantic-reactor-explore-nlp-sheets.html Code sample: https://github.com/google/making_with_ml/blob/master/semantic_ml/use_sample.js @ai_machinelearning_big_data

293 421

Facebook research at ECCV 2020 https://ai.facebook.com/blog/facebook-research-at-eccv-2020/ @ai_machinelearning_big_data

293 421

@itlecture - канал с бесплатными обучающими видео-лекциями по IT и технологиям, а так же записями крупных конференций на различные IT тематики как для новичков, так и для опытных айтишников. Программирование, Искусственный Интеллект, DevOps, Clouds, Веб-Дизайн, Базы Данных и многое другое. ➡️ https://t.me/itlecture - Заходи и прокачай свои скиллы БЕСПЛАТНО с кучей крутых видосов.

293 421

📗 Forward from the 'Deep Learning for Coders' Book Post: https://www.fast.ai/2020/08/20/soumith-forward/ Free Book in Jupiter: https://github.com/fastai/fastbook/blob/master/01_intro.ipynb Github: https://github.com/fastai/fastbook @ai_machinelearning_big_data

293 421

Understanding Deep Learning on Controlled Noisy Labels https://ai.googleblog.com/2020/08/understanding-deep-learning-on.html Code: https://github.com/google-research/google-research/tree/master/mentormix Dataset: https://google.github.io/controlled-noisy-web-labels/index.html @ai_machinelearning_big_data

293 421

PyTorch 1.6 now includes Stochastic Weight Averaging https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ @ai_machinelearning_big_data

293 421

Content-Based Recommendation System using Word Embeddings https://www.kdnuggets.com/2020/08/content-based-recommendation-system-word-embeddings.html Code: https://github.com/sdhilip200/Content-Based-Recommendation---Good-Reads-data @ai_machinelearning_big_data

293 421

Efficient PyTorch I/O library for Large Datasets, Many Files, Many GPUs https://pytorch.org/blog/efficient-pytorch-io-library-for-large-datasets-many-files-many-gpus/ Github: https://github.com/tmbdev/webdataset Documentation: https://webdataset.readthedocs.io/en/latest/ @ai_machinelearning_big_data

293 421

🔥 Language Interpretability Tool Open-source platform for visualization and understanding of NLP models. Github: https://github.com/PAIR-code/lit Developer Guide: https://github.com/PAIR-code/lit/blob/main/docs/development.md Paper: https://arxiv.org/abs/2008.05122 Video: https://www.youtube.com/watch?v=j0OfBWFUqIE @ai_machinelearning_big_data

293 421

Guided Collaborative Training for Pixel-wise Semi-Supervised Learning Github: https://github.com/ZHKKKe/PixelSSL Paper: https://arxiv.org/abs/2008.05258 @ai_machinelearning_big_data

293 421

Plot a Decision Surface for Machine Learning Algorithms in Python https://machinelearningmastery.com/plot-a-decision-surface-for-machine-learning/ @ai_machinelearning_big_data

293 421

TensorFlow Model Optimization Toolkit — Weight Clustering API https://blog.tensorflow.org/2020/08/tensorflow-model-optimization-toolkit-weight-clustering-api.html @ai_machinelearning_big_data

293 421

5 Machine Learning Regression Algorithms You Need to Know https://towardsdatascience.com/5-regression-algorithms-you-need-to-know-theory-implementation-37993382122d habr ru: https://habr.com/ru/company/mailru/blog/513842/ @ai_machinelearning_big_data

293 421

LSTM in Machine Learning https://thecleverprogrammer.com/2020/08/11/lstm-in-machine-learning/ @ai_machinelearning_big_data

293 421

Layerwise learning for Quantum Neural Networks Training strategy that addresses vanishing gradients in quantum neural networks (QNNs). https://blog.tensorflow.org/2020/08/layerwise-learning-for-quantum-neural-networks.html Quirk: a drag-and-drop quantum circuit simulator with nice visualizations: https://algassert.com/quirk Paper: https://arxiv.org/abs/2003.02989 Quantum Intuition:https://www.youtube.com/channel/UC-2knDbf4kzT3uzWo7iTJyw @ai_machinelearning_big_data

293 421

Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets Github: https://github.com/facebookresearch/QA-Overlap Paper: https://arxiv.org/abs/2008.02637 @ai_machinelearning_big_data

293 421

DeText: A Deep Neural Text Understanding Framework DeText can be applied to many tasks, including search & recommendation ranking, multi-class classification and query understanding tasks. Github: https://github.com/linkedin/detext Paper: https://arxiv.org/abs/2008.02460v1 @ai_machinelearning_big_data