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 283 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 293 399 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

21 487

Post views

~ 16 93724 hours

~ 18 77448 hours

7.32%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

293 359

В 1962 году трое мужчин обманули охрану и смогли покинуть «Алькатрас» — тюрьму, из которой невозможно сбежать. Следователи решили, что мужчины утонули после бегства, однако спустя почти 60 лет нейросеть от компаний Identv и Rothco, проанализировав миллионы фото, «опознала» двоих преступников на снимке 1975 года. То, что оказалось неподвластным человеку, сделал искусственный интеллект. Какие еще возможности открывает Deep Learning, расскажут преподаватели SkillFactory на курсе по нейросетям. Осваивайте machine learning, Data Engineering и менеджмент, чтобы решать интересные задачи и расти профессионально. В течение 10 недель вы изучите фреймворки TensorFlow и Keras, научитесь работать со сверточными нейросетями и сможете их оптимизировать; в конце обучения проводится хакатон на реальных датасетах. ⚡️Давно откладывал обучение? Самое лучшее время – сейчас! Получи курс со скидкой: https://clc.to/skdwEA

293 359

Flows for simultaneous manifold learning and density estimation A new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold. Code: https://github.com/johannbrehmer/manifold-flow Paper: https://arxiv.org/abs/2003.13913

293 359

Improving Audio Quality in Duo with WaveNetEQ https://ai.googleblog.com/2020/04/improving-audio-quality-in-duo-with.html

293 359

Introducing the Model Garden for TensorFlow 2 Code examples for state-of-the-art models and reusable modeling libraries for TensorFlow 2. https://blog.tensorflow.org/2020/03/introducing-model-garden-for-tensorflow-2.html Model Garden repository: https://github.com/tensorflow/models/tree/master/official

293 359

🎲 Probabilistic Regression for Visual Tracking A general python framework for training and running visual object trackers, based on PyTorch. Code: https://github.com/visionml/pytracking Paper: https://arxiv.org/abs/2003.12565

293 359

Today is The World Backup Day! Don’t be an April Fool - protect your data, back up your files today! Share this reminder with your friends! Сегодня Всемирный день резервного копирования! Commvault защищает более 11 Эксабайт данных своих клиентов по всему миру, это 11534336 Терабайт. С заботой о данных мы присоединяемся к дню бэкапа и призываем вас сегодня сделать или проверить бэкапы ваших файлов! #WorldBackupDay #Commvault https://discover.commvault.com/World-Backup-Day.html

293 359

iTAML: An Incremental Task-Agnostic Meta-learning Approach iTAML hypothesizes that generalization is a key factor for continual learning Code is implemented using PyTorch and it includes code for running the incremental learning domain experiments Code: https://github.com/brjathu/iTAML Paper: https://arxiv.org/abs/2003.11652v1

293 359

New dataset from Google The Taskmaster-2 dataset consists of 17,289 dialogs https://research.google/tools/datasets/taskmaster-2/

293 359

MoCo: Momentum Contrast for Unsupervised Visual Representation Learning Github: https://github.com/facebookresearch/moco Paper: https://arxiv.org/abs/1911.05722

293 359

@yegor256news is an author's English-speaking channel by Yegor Bugayenko, a programmer, blogger, author of Elegant Objects, creator of Zerocracy platform and a regular speaker at many major IT-conferences; subscribe and follow his publications!

293 359

Improved Techniques for Training Single-Image GANs The latest convolutional layers are trained with a given learning rate, while previously existing convolutional layers are trained with a smaller learning rate https://www.tobiashinz.com/2020/03/24/improved-techniques-for-training-single-image-gans.html Code: https://github.com/tohinz/ConSinGAN Paper: https://arxiv.org/abs/2003.11512

293 359

Deep unfolding network for image super-resolution Deep unfolding network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods. Github: https://github.com/cszn/USRNet Paper: https://arxiv.org/pdf/2003.10428.pdf

293 359

NeRF: Neural Radiance Fields Algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction http://www.matthewtancik.com/nerf Tensorflow implementation: https://github.com/bmild/nerf Paper: https://arxiv.org/abs/2003.08934v1

293 359

PyTorch Tutorial: How to Develop Deep Learning Models with Python https://machinelearningmastery.com/pytorch-tutorial-develop-deep-learning-models/

293 359

High-Resolution Daytime Translation Without Domain Labels HiDT combines a generative image-to-image model and a new upsampling scheme that allows to apply image translation at high resolution. https://saic-mdal.github.io/HiDT/ Paper: https://arxiv.org/abs/2003.08791 Video: https://www.youtube.com/watch?v=DALQYKt-GJc&feature=youtu.be

293 359

Часто работаете с данными и почти наверняка только в Google Sheets или Excel? Да, для большинства задач этих инструментов вполне достаточно. Но если информации стало слишком много и гугл шитс не выдерживает, а источники данных хочется объединить в одно место для работы с ними — пора осваивать Python. 25 марта в 19:00 (мск) ребята из ProductStar проводят бесплатный онлайн-интенсив «Рассказываем простыми словами о Python». 👨‍🏫 Кто выступит? Андрей Пушвинцев, Product Analyst в Miro. 👩‍🏫 О чем пойдет речь? — Расскажем, как устроен и как работает Python, — Разберемся с базовым синтаксисом языка, научимся его читать и понимать. — Познакомимся с библиотеками для анализа данных. — Сделаем разведку данных и превратим грязные данные в красивые таблицы. Все участники получат именные электронные сертификаты, два самых активных — сертификат на бесплатное обучение в ProductStar. Участие бесплатное, но регистрация обязательна. Зарегистрироваться на вебинар 👉 @ProductStarAnalyticsBot

293 359

Scene Text Recognition via Transformer The method use a convolutional feature maps as word embedding input into transformer. Github: https://github.com/fengxinjie/Transformer-OCR Paper: https://arxiv.org/abs/2003.08077 The transformer source code:http://nlp.seas.harvard.edu/2018/04/03/attention.html

293 359

Get yourself stuffed with popcorn and cookies, tea and toilet paper (if you managed to buy some) and join our free practical web-event on Data Science from top IBM specialists! Key points: * ML in the Blue Giant - experience of world's top enterprise * IBM Watson's capabilities to automate and scale up your ML results * Grant program of $120k - your chance to shine The opportunity is tasty, we had to close up the registration on our previous event early. Join, while you can! Sign up now

293 359

Few-Shot Object Detection (FsDet) Detecting rare objects from a few examples is an emerging problem. In addition to the benchmarks we introduce new benchmarks on three datasets: PASCAL VOC, COCO, and LVIS. We sample multiple groups of few-shot training examples for multiple runs of the experiments and report evaluation results on both the base classes and the novel classes. Github: https://github.com/ucbdrive/few-shot-object-detection Paper: https://arxiv.org/abs/2003.06957

293 359

Introducing Dreamer: Scalable Reinforcement Learning Using World Models Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. https://ai.googleblog.com/2020/03/introducing-dreamer-scalable.html Paper: https://arxiv.org/abs/1912.01603 Blog: https://dreamrl.github.io/