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 281 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 293 457 subscribers, ranking 326 in the Technologies & Applications category and 1 281 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 293 457 subscribers.

According to the latest data from 02 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -6 464 over the last 30 days and by -249 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.49%. Within the first 24 hours after publication, content typically collects 5.71% reactions from the total number of subscribers.
Post reach: On average, each post receives 21 989 views. Within the first day, a publication typically gains 16 765 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 173.
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 03 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.

293 457

Subscribers

-24924 hours

-1 5267 days

-6 46430 days

21 989

Post views

~ 16 76524 hours

~ 18 67648 hours

7.49%

Engagement rate

~ 6

Posts per day

Ads index

beta

Posts Archive

293 432

Хотите узнать какие подводные камни в работе с современной BigData? 12 октября пройдет демо-урок «Современные большие данные, анализ и оптимизация производительности распределенных приложений» Кирилл Султанов, расскажет, про подводные камни в работе с современной BigData: кастомизация, распределенное профилирование, контрибьют в open source. Все, что нужно - чтобы выйти в продакшн! Демо-урок является частью онлайн-курса «Промышленный ML на больших данных». Используйте эту возможность, чтобы получить ценные знания, оценить качество знаний и формат обучения. Для регистрации пройдите вступительный тест https://otus.pw/oWF5/

293 432

From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering Gitgub: https://github.com/HazyResearch/HypHC Paper: https://arxiv.org/abs/2010.00402

293 432

aLRP Loss: A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection. 💻 Github: https://github.com/kemaloksuz/aLRPLoss 📎 Dataset: https://cocodataset.org/#download 🗒 Paper: https://arxiv.org/abs/2009.13592v1 @ai_machinelearning_big_data

293 432

Rotated Binary Neural Network Pytorch implementation of RBNN. Github: https://github.com/lmbxmu/RBNN Paper: https://arxiv.org/abs/2009.13055 @ai_machinelearning_big_data

293 432

Transfer learning with TensorFlow Hub | TensorFlow Core https://www.tensorflow.org/tutorials/images/transfer_learning_with_hub TensorFlow Hub : https://tfhub.dev/ Github: https://github.com/tensorflow/docs/blob/master/site/en/tutorials/images/transfer_learning_with_hub.ipynb @tensorflowblog

293 432

Utterance-level Dialogue Understanding: An Empirical Study The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding. Github: https://github.com/declare-lab/conv-emotion Paper: https://arxiv.org/abs/2009.13902v1

293 432

Seeing Theory 🎲 A visual introduction to probability and statistics https://seeing-theory.brown.edu/index.html#4thPage 📗 Free book: https://seeing-theory.brown.edu/doc/seeing-theory.pdf

293 432

CaGNet: Context-aware Feature Generation for Zero-shot Semantic Segmentation. Github: https://github.com/bcmi/CaGNet-Zero-Shot-Semantic-Segmentation Paper: https://arxiv.org/abs/2009.12232v1 @ai_machinelearning_big_data

293 432

Graph Normalization Learning Graph Normalization for Graph Neural Networks Github: https://github.com/cyh1112/GraphNormalization Paper: https://arxiv.org/abs/2009.11746v1 @ai_machinelearning_big_data

293 432

🔋 The Most Complete Guide to PyTorch for Data Scientists https://www.kdnuggets.com/2020/09/most-complete-guide-pytorch-data-scientists.html Code: https://github.com/MLWhiz/data_science_blogs/tree/master/pytorch_guide @ai_machinelearning_big_data

293 432

Bringing the Mona Lisa Effect to Life with TensorFlow.js https://blog.tensorflow.org/2020/09/bringing-mona-lisa-effect-to-life-tensorflow-js.html Github: https://github.com/emilyxxie/mona_lisa_eyes Demo: https://monalisaeffect.com/ @ai_machinelearning_big_data

293 432

Facebook AI Releases ‘Dynabench’, A Dynamic Benchmark Testing Platform For Machine Learning Systems Articel: https://ai.facebook.com/blog/dynabench-rethinking-ai-benchmarking/ Project: https://dynabench.org/ @ai_machinelearning_big_data

293 432

📸 Old Photo Restoration (Official PyTorch Implementation) Restore old photos that suffer from severe degradation through a deep learning approace. http://raywzy.com/Old_Photo/ Github: https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life Paper: https://arxiv.org/pdf/2009.07047v1.pdf Colab: https://colab.research.google.com/drive/1NEm6AsybIiC5TwTU_4DqDkQO0nFRB-uA @ai_machinelearning_big_data

293 432

Advancing NLP with Efficient Projection-Based Model Architectures https://ai.googleblog.com/2020/09/advancing-nlp-with-efficient-projection.html Sequence Projection Models: https://github.com/tensorflow/models/tree/master/research/sequence_projection

293 432

Implementing a Deep Learning Library from Scratch in Python https://www.kdnuggets.com/2020/09/implementing-deep-learning-library-scratch-python.html

293 432

MEAL V2 Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks. Github: https://github.com/szq0214/MEAL-V2 Paper: https://arxiv.org/abs/2009.08453 ImageNet dataset: https://github.com/pytorch/examples/tree/master/imagenet#requirements. @ai_machinelearning_big_data

293 432

Dialog Ranking Pretrained Transformers It is a set of dialog response ranking models proposed by Microsoft Research NLP Group trained on millions of human feedback data. Github: https://github.com/golsun/DialogRPT Paper: https://arxiv.org/abs/2009.06978 Colab: https://colab.research.google.com/drive/1jQXzTYsgdZIQjJKrX4g3CP0_PGCeVU3C?usp=sharing @ai_machinelearning_big_data

293 432

Improving Sparse Training with RigL https://ai.googleblog.com/2020/09/improving-sparse-training-with-rigl.html Github: https://github.com/google-research/rigl Paper: https://arxiv.org/abs/1911.11134 @ai_machinelearning_big_data

293 432

Rule-Guided Graph Neural Networks for Recommender Systems Сombination of rule learning and GNNs achieves substantial improvement compared to methods only using either of them Github: https://github.com/nju-websoft/RGRec Paper: https://arxiv.org/abs/2009.04104v1

293 432

LaSOT Large-scale Single Object Tracking (LaSOT) aims to provide a dedicated platform for training data-hungry deep trackers as well as assessing long-term tracking performance. http://vision.cs.stonybrook.edu/~lasot/ Github: https://github.com/HengLan/LaSOT_Evaluation_Toolkit Dataset: http://vision.cs.stonybrook.edu/~lasot/download.html Paper: https://arxiv.org/abs/2009.03465 @ai_machinelearning_big_data