en
Feedback
Machinelearning

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 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
Posts Archive
DeepMind Made a Math Test For Neural Networks https://www.youtube.com/watch?v=f9z1I_81_Q4

Learning Perceptually-Aligned Representations via Adversarial Robustness Article: https://arxiv.org/abs/1906.00945 Github: https://github.com/MadryLab/robust_representations

Integrating TVM into PyTorch https://tvm.ai/2019/05/30/pytorch-frontend

InstaNAS: Instance-aware Neural Architecture Search https://hubert0527.github.io/InstaNAS/

A Gentle Introduction to Deep Learning for Face Recognition https://machinelearningmastery.com/introduction-to-deep-learning-for-face-recognition/

Multi-Sample Dropout for Accelerated Training and Better Generalization Link: https://arxiv.org/abs/1905.09788

EfficientNets EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks link: https://arxiv.org/abs/1905.11946.

How to Train an Object Detection Model to Find Kangaroos in Photographs (R-CNN with Keras) https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/

SimpleSelfAttention The purpose of this repository is two-fold: -demonstrate improvements brought by the use of a self-attention layer in an image -classification model. introduce a new layer which I call SimpleSelfAttention https://github.com/sdoria/SimpleSelfAttention

AlphaFold: Использование ИИ для научных открытий https://habr.com/ru/company/otus/blog/453848/

Arbitrary Style Transfer with Style-Attentional Networks https://dypark86.github.io/SANET/

How degenerate is the parametrization of neural networks with the ReLU activation function? https://arxiv.org/abs/1905.09803

illustrated Artificial Intelligence cheatsheets covering the content of the CS 221 class Link: https://stanford.edu/~shervine/teaching/cs-221/ Reflex-based models with Machine Learning: https://stanford.edu/~shervine/teaching/cs-221/cheatsheet-reflex-models

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration https://arxiv.org/abs/1905.09275

Torchvision 0.3: segmentation, detection models, new datasets https://pytorch.org/blog/torchvision03/