Available now! Telegram Research 2025 — the year's key insights 
Machinelearning
Open in Telegram
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
Show more📈 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 293 399 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 293 399 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.
293 399
Subscribers
-21824 hours
-1 5287 days
-6 46930 days
Posts Archive
293 392
NUBIA (NeUral Based Interchangeability Assessor) is a new SoTA evaluation metric for text generation
Methodology to build automatic evaluation metrics for text generation using only machine learning models as core components
https://wl-research.github.io/blog/
Github: https://github.com/wl-research/nubia
Paper: https://arxiv.org/abs/2004.14667v1
Colab: https://colab.research.google.com/drive/1_K8pOB8fRRnkBPwlcmvUNHgCr4ur8rFg
293 392
Как использовать новые возможности работы с данными для того, чтобы предлагать оптимальные решения для бизнеса?
6 мая в 19:00 (мск) в рамках шестимесячного онлайн-курса «Профессия: Аналитик», ребята из ProductStar проводят бесплатный вебинар «Быстрое погружение в ML».
👨🏫 Кто выступит?
Алексей Григорьев, Lead Data Scientist в OLX Group
🎓 О чем пойдет речь?
— Расскажем о процессе ведения проектов машинного обучения;
— Разберём основные этапы проектов с ML;
— Изучим метрики оценки результатов ML.
🔥 Два самых активных участника вебинара получат сертификат на бесплатное обучение в ProductStar.
Участие бесплатное, но регистрация обязательна.
Зарегистрироваться на вебинар 👉 @ProductStarAnalyticsBot
293 392
📈 Learning Convolutional Neural Networks with Interactive Visualization
Interactive visualization tool designed for non-experts to learn and examine convolutional neural networks (CNNs), a foundational deep learning model architecture.
Video: https://www.youtube.com/watch?v=HnWIHWFbuUQ&feature=youtu.be
Demo: https://poloclub.github.io/cnn-explainer/
Github: https://github.com/poloclub/cnn-explainer
Paper: https://arxiv.org/abs/2004.15004v1
293 392
Measuring Information Propagation in Literary Social Network
Annotated dataset of 100 works of fiction to support tasks in natural language processing and the computational humanities.
Code: https://github.com/dbamman/litbank
Paper: https://arxiv.org/pdf/2004.13980v1.pdf
293 392
Jukebox: a new generative model for audio from OpenAI.
Jukebox, a model that generates music with singing in the raw audio domain.
openai.com/blog/jukebox
Article: cdn.openai.com/papers/jukebox.pdf
Examples: https://jukebox.openai.com/
Code: https://github.com/openai/jukebox
293 392
🦑 Neuroevolution in squids
The versatility and adaptivity of the demonstrated neuroevolution framework makes it an interesting tool for many other applications, especially those focusing on artificial life.
https://jobtalle.com/neuroevolution_in_squids.html
Habr ru: https://habr.com/ru/post/499046/
https://jobtalle.com/neuroevolution_in_squids.html
293 392
Google’s Optimizing Multiple Loss Functions with Loss-Conditional Training
https://ai.googleblog.com/2020/04/optimizing-multiple-loss-functions-with.html
Demo: https://sites.google.com/view/stochss/adjusting-parameters
ADJUSTABLE REAL-TIME STYLE TRANSFER: https://sites.google.com/view/stochss/adjusting-parameters
293 392
NVIDIA Announce MONAI Open Source AI Framework for Healthcare Research
MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging.
https://blogs.nvidia.com/blog/2020/04/21/monai-open-source-framework-ai-healthcare/?ncid=so-twit-79443#cid=ix11_so-twit_en-us
Code: https://github.com/Project-MONAI/MONAI
Docs: https://monai.readthedocs.io/en/latest/
293 392
Как понять ценность продукта для пользователей? Самый эффективный показатель — продуктовые метрики.
⏰ В среду, 29 апреля в 19:00 (мск) ребята из ProductStar проводят бесплатный интенсив для начинающих аналитиков: «Простыми словами о метриках».
👩🏫 Кто выступит?
Михаил Морозов, Analytics Team Lead в Skyeng
👨🏫 О чем пойдет речь?
— На вебинаре вместе с Мишей поговорим, какие бывают метрики у продукта.
— Расскажем на пальцах, что такое распределение метрики и зачем его оценивать.
— Разберём, как прогнозировать метрики, когда никто ни в чём не уверен.
— На примере Skyeng покажем, как распределение метрик помогает аналитику сделать крутой прогноз и оценить влияние новых фич.
🔥 Два самых активных участника вебинара получат сертификат на бесплатное обучение в ProductStar.
Участие бесплатное, но регистрация обязательна.
Зарегистрироваться на вебинар 👉 @ProductStarAnalyticsBot
293 392
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
Easy and efficient method to extend existing sentence embedding models to new languages. This allows to create multilingual versions from previously monolingual models.
Code: https://github.com/UKPLab/sentence-transformers
Paper: https://arxiv.org/abs/2004.09813v1
293 392
Занимайте последние места на онлайн-курсе «Компьютерное зрение»!
Чтобы попасть на курс с скидкой -30% прямо сейчас, пройдите вступительный тест: https://otus.pw/HdMp/
Углубленный курс по Computer Vision для специалистов в машинном обучении, знакомых с нейронными сетями.
На курсе вы освоите принципы машинного обучения в области компьютерного зрения и сможете решать индустриальные задачи, используя открытые датасеты.
По ходу курса вы обучите нейросети для решения задач:
- классификации и сегментации изображений
- детекции объектов на изображениях
- отслеживания объектов на видео
- обработки трехмерных сцен
- порождения изображений и атаки на обученные модели нейронных сетей
Также вы научитесь пользоваться основными фреймворками для создания нейросетей: PyTorch, TensorFlow и Keras.
293 392
How I taught my computer to play Spot it! using OpenCV and Deep Learning
https://towardsdatascience.com/how-i-learned-my-computer-to-play-spot-it-using-opencv-and-deep-learning-ad1f017a3ec3
Habr ru: https://habr.com/ru/company/otus/blog/498800/
Code: https://github.com/henniedeharder/spotit/tree/master/DeepLearningSpotIt
293 392
Building a Real Time Emotion Detection with Python
https://morioh.com/p/801c509dda99
Code: https://github.com/Dhanush45/Realtime-emotion-detectionusing-python
293 392
Training with quantization noise for extreme model compression
Quant-Noise is a new technique to enable extreme compression of models that still deliver high performance when deployed in practical applications.
https://ai.facebook.com/blog/training-with-quantization-noise-for-extreme-model-compression/
Paper: https://arxiv.org/abs/2004.07320
GitHub: https://github.com/pytorch/fairseq/tree/master/examples/quant_noise
293 392
The Illustrated GPT-2 (Visualizing Transformer Language Models)
Visual explaining the inner-workings of transformers, and how they’ve evolved since the original paper
https://jalammar.github.io/illustrated-gpt2/
Habr ru: https://habr.com/ru/post/490842/
OpenAI Implementation: https://github.com/openai/gpt-2
293 392
How to Develop an Extra Trees Ensemble with Python
https://machinelearningmastery.com/extra-trees-ensemble-with-python/
293 392
ResNeSt: Split-Attention Networks
Split-Attention Network, A New ResNet Variant. It significantly boosts the performance of downstream models such as Mask R-CNN, Cascade R-CNN and DeepLabV3.
Github: https://github.com/zhanghang1989/ResNeSt#pretrained-models
Paper: https://arxiv.org/abs/2004.08955v1
293 392
Today, on April 22 is Earth day. It’s a right time to look at the climate issues in terms of data storage.
* 90% of all data was created in the last two years
* IoT, Big Data and AI are huge data creators
* 70% of all data stored is copy data2
* 70-80% of data is typically unstructured * 2018 HDD shipments = 869Eb
* 2023 HDD shipments = 2.6Zb
* In a normal DC, 1 watt of HDD consumption = 1 watt of cooling
What can everyone do for the ecology of our planet?
* Migrate suitable workloads to the cloud
* Collect, process and store less data; archive more to reduce carbon storage
* Use backup/archive instead of big data
* Leverage copy management tools
* If you must keep data for longer, use tape or cloud tape
Software solutions for backup, managing and recovering data help to move your data to the cloud and so you can take care of the environment. Commvault - leading experts in software-defined storage. Over 11 Exabytes of customer data are under Commvault management.
293 392
Transform and Tell: Entity-Aware News Image Captioning
End-to-end model which generates captions for images embedded in news articles.
Github: https://github.com/alasdairtran/transform-and-tell
Demo: https://transform-and-tell.ml/
Paper: https://arxiv.org/abs/2004.08070
