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 278 Technologies & Applications328...

📈 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 292 964 subscribers, ranking 328 in the Technologies & Applications category and 1 278 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 292 964 subscribers.

According to the latest data from 06 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 314 over the last 30 days and by -187 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.37%. Within the first 24 hours after publication, content typically collects 5.45% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 579 views. Within the first day, a publication typically gains 15 979 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 159.
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 07 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.

292 964

Subscribers

-18724 hours

-1 3257 days

-6 31430 days

21 579

Post views

~ 15 97924 hours

~ 17 85348 hours

7.37%

Engagement rate

~ 6

Posts per day

Ads index

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Posts Archive

292 956

A General Decoupled Learning Framework for Parameterized Image Operators https://arxiv.org/abs/1907.05852

292 956

New fast.ai course: A Code-First Introduction to Natural Language Processing https://www.fast.ai/2019/07/08/fastai-nlp/ Github: https://github.com/fastai/course-nlp Videos: https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9

292 956

Postuf — продуктовая компания, которая занимается разработкой приложения, позволяющего узнать всю информацию о человеке из сети. Вводите имя, загружаете фото и получаете информацию об увлечениях, круге общения, последних совершённых действиях — всё, что только можно узнать о человеке из открытых источников. Сейчас они ищут разработчиков на Android, iOS и бэкенд (PHP, MySQL, Redis). Условия комфортные — офис в центре Москвы (ст.м. Чкаловская), Macbook Pro для работы, бесплатные обеды и ежегодный рост з/п на 20к. Подробнее о компании и вакансии: https://postuf.com/

292 956

How to Implement Wasserstein Loss for Generative Adversarial Networks https://machinelearningmastery.com/how-to-implement-wasserstein-loss-for-generative-adversarial-networks/

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Simple Deep Q Network w/Pytorch: https://youtu.be/UlJzzLYgYoE Reinforcement Learning Crash Course: https://youtu.be/sOiNMW8k4T0 Policy Gradients w/Tensorflow: https://youtu.be/UT9pQjVhcaU Deep Q Learning w/Tensorflow https://youtu.be/3Ggq_zoRGP4 Code Your Own RL Environments https://youtu.be/vmrqpHldAQ0 How to Spec a Deep Learning PC: https://youtu.be/xsnVlMWQj8o Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA Machine Learning Freelancing https://youtu.be/6M04ZTLE_O4 Code from video: https://github.com/philtabor/Youtube-Code-Repository

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Learning to learn with quantum neural networks via classical neural networks https://arxiv.org/abs/1907.05415

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An implementation of the BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer (Interspeech 2019) Article: https://arxiv.org/pdf/1907.03040.pdf Github: https://github.com/guanlinchao/bert-dst

292 956

How to Code the GAN Training Algorithm and Loss Functions https://machinelearningmastery.com/how-to-code-the-generative-adversarial-network-training-algorithm-and-loss-functions/

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Multilingual Universal Sentence Encoder for Semantic Retrieval http://ai.googleblog.com/2019/07/multilingual-universal-sentence-encoder.html

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TRFL a library of reinforcement learning building blocks By the Research Engineering team at DeepMind: https://github.com/deepmind/trfl

292 956

Advancing Semi-supervised Learning with Unsupervised Data Augmentation http://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html

292 956

A Tour of Generative Adversarial Network Models https://machinelearningmastery.com/tour-of-generative-adversarial-network-models/

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Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data Article: https://arxiv.org/abs/1907.04240 PDF: https://arxiv.org/pdf/1907.04240.pdf

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Predicting the Generalization Gap in Deep Neural Networks http://ai.googleblog.com/2019/07/predicting-generalization-gap-in-deep.html

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Neural Architecture Search at CVPR 2019 https://drsleep.github.io/NAS-at-CVPR-2019/

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How to Identify and Diagnose GAN Failure Modes https://machinelearningmastery.com/practical-guide-to-gan-failure-modes/

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Rank-consistent Ordinal Regression for Neural Networks Article: https://arxiv.org/abs/1901.07884 PyTorch: https://github.com/Raschka-research-group/coral-cnn

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Literature of Deep Learning for Graphs This is a paper list about deep learning for graphs. https://github.com/DeepGraphLearning/LiteratureDL4Graph

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Check the data science channel there you will find a lot of articles, links and advanced researches . Join and learn hot topics of data science @opendatascience

292 956

How to Develop a Conditional GAN (cGAN) From Scratch Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.