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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 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
Posts Archive
Задача по #DataScience от QIWI. Наверняка вы знаете, что такое Терминалы QIWI. Они позволяют совершить оплату в пользу более чем двух тысяч провайдеров. Сейчас пользователи мало платят через терминалы, не знают или не помнят, что их услугу можно оплатить на терминале. QIWI ищет команду, которая сможет доработать (уже реализованный) механизм, рекомендующий плательщику дополнительный платёж. Нужно научиться определять наиболее уместных провайдеров для данного терминала, исходя из его локации и частоты оплачиваемых провайдеров. На реализацию QIWI выделяет 3 млн.рублей и 5 месяцев. Задача подробно описана на сайте (видео + текст) — universe.qiwi.com Заявки от команд до 19 мая. Вопросы все можно задать в @QU_product_hub

How to Develop a Deep Convolutional Neural Network From Scratch for Fashion MNIST Clothing Classification https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-fashion-mnist-clothing-classification/

Digging Into Self-Supervised Monocular Depth Estimation Article.: https://arxiv.org/abs/1806.01260 Github: https://github.com/nianticlabs/monodepth2

Announcing Open Images V5 and the ICCV 2019 Open Images Challenge http://ai.googleblog.com/2019/05/announcing-open-images-v5-and-iccv-2019.html

Искусственный интеллект на примере простой игры. Часть 2 https://habr.com/ru/post/451070/

How to Develop a Convolutional Neural Network From Scratch for MNIST Handwritten Digit Classification https://machinelearningmastery.com/blog/

Огромный открытый датасет русской речи https://habr.com/ru/post/450760/

How to Visualize Filters and Feature Maps in Convolutional Neural Networks https://machinelearningmastery.com/how-to-visualize-filters-and-feature-maps-in-convolutional-neural-networks/

Billion-scale semi-supervised learning for image classification" Weakly-supervised pre-training + semi-supervised pre-training + distillation + transfer/fine-tuning =81.2% twith ResNet-50, 84.8% with ResNeXt-101-32x16, top-1 accuracy on ImageNet. article: https://arxiv.org/abs/1905.00546 announce : https://www.facebook.com/i.zeki.yalniz/posts/10157311492509962

Best Practices for Preparing and Augmenting Image Data for Convolutional Neural Networks https://machinelearningmastery.com/best-practices-for-preparing-and-augmenting-image-data-for-convolutional-neural-networks/

Announcing Google-Landmarks-v2: An Improved Dataset for Landmark Recognition & Retrieval http://ai.googleblog.com/2019/05/announcing-google-landmarks-v2-improved.html

BoTorch is a library for Bayesian Optimization built on PyTorch. Facebook open-sources Ax and BoTorch to simplify AI model optimization github: https://github.com/pytorch/botorch description: https://techcrunch.com/2019/05/01/facebook-open-sources-ax-and-botorch-to-simplify-ai-model-optimization/

Real-Time Patch-Based Stylization of Portraits Using Generative Adversarial Network http://dcgi.fel.cvut.cz/home/sykorad/facestyleGAN.html

Пошаговое руководство по созданию голосового помощника с Python https://habr.com/ru/post/450224/

A Gentle Introduction to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) https://machinelearningmastery.com/introduction-to-the-imagenet-large-scale-visual-recognition-challenge-ilsvrc/