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Machinelearning

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 291 898 subscribers, ranking 327 in the Technologies & Applications category and 1 297 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 291 898 subscribers.

According to the latest data from 11 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 089 over the last 30 days and by -172 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.14%. Within the first 24 hours after publication, content typically collects 5.61% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 20 847 views. Within the first day, a publication typically gains 16 386 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 153.
  • 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 12 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.

291 898
Subscribers
-17224 hours
-1 2407 days
-6 08930 days
Posts Archive
Интересное в мире DS/ML (в этот раз негусто): - Andrew Ng открыл новый курс на Курсере про Deep Learning - ссылка выше (было) - Facebook полностью перешел на нейросети для перевода - https://goo.gl/VhjA9H - Pytorch v0.2 - https://goo.gl/99QpB3 - может кому-то актуально - Люди из Salesforce тренируют LTSM сети для перевода с одного языка на другой, берут получившийся декодер (часть сети) и используют его чтобы скормить его выход другим нейросетям, которые делают более простые вещи - https://goo.gl/6Kt77o #data_science

Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors. https://arxiv.org/abs/1708.02002 #deeplearning #paper

В ИТМО через 10 дней пройдет летняя школа машинного обучения ЦРТ. Учеба бесплатная, организаторы оплачивают билеты и проживание 20 участникам. http://algorythm.tech/

#machinelearning in python for trading http://upflow.co/l/6E7H

Занятная статья - мысли профессора финансов про биткоины (несовместимые вещи) =) - https://goo.gl/DkgWfH #internet

вот тут хорошо разобрали почему так есть https://habrahabr.ru/company/mailru/blog/331696/