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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: MachinelearningRussia1 377Technologies & Applications336...

📈 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 275 988 subscribers, ranking 336 in the Technologies & Applications category and 1 377 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 275 988 subscribers.

According to the latest data from 07 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -2 499 over the last 30 days and by -157 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 8.01%. Within the first 24 hours after publication, content typically collects 6.41% reactions from the total number of subscribers.
Post reach: On average, each post receives 22 107 views. Within the first day, a publication typically gains 17 690 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 342.
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 08 November, 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.

275 988

Subscribers

-15724 hours

-8727 days

-2 49930 days

22 107

Post views

~ 17 69024 hours

~ 19 43548 hours

8.01%

Engagement rate

~ 6

Posts per day

Ads index

Posts Archive

275 991

📖 Physics-Based Deep Learning Book: https://www.physicsbaseddeeplearning.org/intro.html Github: https://github.com/thunil/Physics-Based-Deep-Learning Deep-Flow-Prediction: https://github.com/thunil/Deep-Flow-Prediction Paper: https://arxiv.org/abs/2109.05237v1 @ai_machinelearning_big_data

275 991

🔧 Neural Networks Block Movement Pruning Code: https://github.com/huggingface/nn_pruning Paper: https://arxiv.org/abs/2109.04838v1 Documentation: https://github.com/huggingface/nn_pruning/blob/main/docs/HOWTO.md @ai_machinelearning_big_data

275 991

✅ AliceMind: ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab Github: https://github.com/alibaba/AliceMind Paper: https://arxiv.org/abs/2109.05687v1 Dataset: https://paperswithcode.com/dataset/glue @ArtificialIntelligencedl

275 991

🔊 Textless NLP: Generating expressive speech from raw audio Facebook Ai: https://ai.facebook.com/blog/textless-nlp-generating-expressive-speech-from-raw-audio/ Examples: https://speechbot.github.io/pgslm/ Code: https://github.com/pytorch/fairseq/tree/master/examples/textless_nlp/gslm Paper: https://arxiv.org/abs/2102.01192 @ai_machinelearning_big_data

275 991

🧍‍♂ Texformer: 3D Human Texture Estimation from a Single Image with Transformers Github: https://github.com/xuxy09/texformer Paper: http://arxiv.org/abs/2109.02563 Meta data: https://www.dropbox.com/s/ekxn300cuw8bw6b/meta.zip @ai_machinelearning_big_data

275 991

🔝 Reinforcement Learning Lecture Series 2021 https://deepmind.com/learning-resources/reinforcement-learning-series-2021 Video: youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm @ai_machinelearning_big_data

275 991

☄️ nnFormer: Interleaved Transformer for Volumetric Segmentation Github: https://github.com/ziniuwan/maed Paper: https://arxiv.org/abs/2109.03201v1 Dataset: https://www.creatis.insa-lyon.fr/Challenge/acdc/ @ai_machinelearning_big_data

275 991

📹 CTRL-C: Camera calibration TRansformer with Line-Classification Github: https://github.com/jwlee-vcl/ctrl-c Paper: https://arxiv.org/abs/2109.02259v1 Dataset: https://drive.google.com/file/d/1opDR_j-9y4TY82ZscBb-xYAoMkTocAOp/view @ai_machinelearning_big_data$$$

275 991

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275 991

👨‍🎓 Best Resources to Learn Natural Language Processing in 2021 https://www.kdnuggets.com/2021/09/best-resources-learn-natural-language-processing-2021.html @ai_machinelearning_big_data

275 991

📁 Open-sourcing Common Objects in 3D, a large-scale data set for 3D reconstruction Github: https://github.com/facebookresearch/co3d Paper: https://arxiv.org/abs/2109.00512 Facebook blog: https://github.com/openvinotoolkit/training_extensions/tree/develop/misc Dataset: https://ai.facebook.com/datasets/co3d-downloads/ @ai_machinelearning_big_data

275 991

ℹ️ LIGAR: Lightweight General-purpose Action Recognition Github: https://github.com/openvinotoolkit/training_extensions Paper: https://arxiv.org/abs/2108.13153v1 Models: https://github.com/openvinotoolkit/training_extensions/tree/develop/misc @ai_machinelearning_big_data

275 991

🔎 Efficient Person Search: An Anchor-Free Approach. Github: https://github.com/daodaofr/AlignPS Paper: https://arxiv.org/abs/2109.00211v1 Dataset: https://paperswithcode.com/dataset/cuhk-sysu @ai_machinelearning_big_data

275 991

📶 ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation Github: https://github.com/segmentationblwx/sssegmentation Paper: https://arxiv.org/abs/2108.12382v1 Dataset: https://cs.stanford.edu/~roozbeh/pascal-context/ @ai_machinelearning_big_data

275 991

ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation Github: https://github.com/hanchaoleng/shapeconv Paper: https://arxiv.org/abs/2108.10528v1 @ai_machinelearning_big_data

275 991

💬 Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation Github: https://github.com/ofirpress/attention_with_linear_biases Paper: https://ofir.io/train_short_test_long.pdf Fairseq: https://github.com/pytorch/fairseq @ai_machinelearning_big_data

275 991

🧍 Fast and Flexible Human Pose Estimation with HyperPose Github: https://github.com/tensorlayer/hyperpose Paper: https://arxiv.org/abs/2108.11826v1 Docs: https://hyperpose.readthedocs.io/en/latest/markdown/quick_start/prediction.html#table-of-flags-for-hyperpose-cli @ai_machinelearning_big_data

275 991

💡Volunteer computing: статья про коллаборативное обучение нейросети Если вы хотите создавать большие модели, не имея суперкомпьютер за спиной, то вам понадобится технология, способная разделить вычисления между теми, кто готов предоставить вам мощности. В этом поможет технология DeDLOC, разработанная в Yandex Research, Hugging Face и University of Toronto. Хабр: https://habr.com/ru/company/yandex/blog/574466/ @ai_machinelearning_big_data

275 991

🕸 Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Github: https://github.com/VITA-Group/Deep_GCN_Benchmarking Paper: https://arxiv.org/abs/2108.10521v1 @ai_machinelearning_big_data

275 991

🔗 A Unified Objective for Novel Class Discovery Github: https://github.com/DonkeyShot21/UNO Paper: https://arxiv.org/abs/2108.08536v2 Dataset: https://paperswithcode.com/dataset/cifar-100 @ai_machinelearning_big_data