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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

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📈 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 293 167 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 293 167 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.

293 167
Subscribers
-13124 hours
-1 4647 days
-6 36630 days
Posts Archive
🔥AI For Everyone Free course from Andrew Ng In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate ethical and societal discussions surrounding AI https://www.coursera.org/learn/ai-for-everyone

Создай модель машинного обучения с нуля на бесплатном интенсиве! Ссылка для регистрации 🔜 https://clc.to/XaaVUQ ✔️ Настроим среду и проведем экспресс-введение в Python. ✔️ Построим модель от начала до конца и оценим ее качество. ✔️ Проведем ревью работ участников. Стань одним из лучших и получи грант на 30 000 рублей для обучения в Skillbox!

This is an attempt to modify Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook's code into PyTorch. https://github.com/dsgiitr/d2l-pytorch

Bi-Tempered Logistic Loss for Training Neural Nets with Noisy Data http://ai.googleblog.com/2019/08/bi-tempered-logistic-loss-for-training.html

TensorFlow with Apache Arrow Datasets Apache Arrow enables the means for high-performance data exchange with TensorFlow that is both standardized and optimized for analytics and machine learning. https://medium.com/tensorflow/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f Also TensorFlow 2.0 Release Candidate: https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0-rc0

Deep Learning for Content Creation Tutorial https://nvlabs.github.io/dl-for-content-creation/

Deep Learning Illustrated: Building Natural Language Processing Models https://blog.dominodatalab.com/deep-learning-illustrated-building-natural-language-processing-models/

Data Visualization Curriculum A data visualization curriculum of interactive notebooks, using Vega-Lite and Altair. https://github.com/uwdata/visualization-curriculum

Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. https://github.com/uber/ludwig

Turbo, An Improved Rainbow Colormap for Visualization http://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html

🔥New models in 17 and 100 languages XLM/mBERT pytorch LM supports multi-GPU and multi-node training https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models

A Gentle Introduction to StyleGAN the Style Generative Adversarial Network https://machinelearningmastery.com/introduction-to-style-generative-adversarial-network-stylegan/

Music Transformer: Generating Music with Long-Term Structure Code: https://github.com/jason9693/MusicTransformer-tensorflow2.0 Article: https://arxiv.org/abs/1809.04281

ai ,machine learning • 1146 leaderboards • 1223 tasks • 1105 datasets • 14779 papers with code https://paperswithcode.com/sota

Joint Speech Recognition and Speaker Diarization via Sequence Transduction http://ai.googleblog.com/2019/08/joint-speech-recognition-and-speaker.html