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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

Ko'proq ko'rsatish

Tarmoq:Machinelearning Rossiya1 276 Texnologiyalar & Aralashmalar327...

📈 Telegram kanali Machinelearning analitikasi

Machinelearning (@ai_machinelearning_big_data) Rus til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 293 687 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 327-o'rinni va Rossiya mintaqasida 1 276-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 293 687 obunachiga ega bo‘ldi.

01 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -6 444 ga, so‘nggi 24 soatda esa -235 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 7.55% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 5.55% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 22 202 marta ko‘riladi; birinchi sutkada odatda 16 311 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 172 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent openai, claude, api, gemini, контекст kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 02 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

293 687

Obunachilar

-23524 soatlar

-1 5517 kunlar

-6 44430 kunlar

22 202

Post ko'rishlar

~ 16 31124 soatlar

~ 18 21848 soatlar

7.55%

Muloqot nisbati

~ 6

Kuniga postlar

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Postlar arxiv

293 650

🔍 TOOD: Task-aligned One-stage Object Detection Github: https://github.com/fcjian/TOOD Paper: https://arxiv.org/abs/2108.07755v2 Dataset: https://paperswithcode.com/dataset/coco @ai_machinelearning_big_data

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🔍 TOOD: Task-aligned One-stage Object Detection Github: https://github.com/dvlab-research/panopticfcn Paper: https://arxiv.org/abs/2108.07755v2 Dataset: https://paperswithcode.com/dataset/coco @ai_machinelearning_big_data

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✔️ Fully Convolutional Networks for Panoptic Segmentation Github: https://github.com/dvlab-research/panopticfcn Paper: https://arxiv.org/pdf/2012.00720.pdf Methods: https://paperswithcode.com/method/fcn @ai_machinelearning_big_data

293 650

💡 X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics Github: https://github.com/yehli/xmodaler Paper: https://arxiv.org/abs/2108.08217v1 Project: https://xmodaler.readthedocs.io/en/latest/ @ai_machinelearning_big_data

293 650

👁 MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition Github: https://github.com/open-mmlab/mmocr Paper: https://arxiv.org/abs/2108.06543v1 Documentation: https://mmocr.readthedocs.io/en/latest/ @ai_machinelearning_big_data

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⏺ Towards Interpretable Deep Metric Learning with Structural Matching Github: https://github.com/wl-zhao/diml Paper: https://arxiv.org/abs/2108.05889v1 RevisitDML: https://github.com/wl-zhao/diml @ai_machinelearning_big_data

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Oriented R-CNN for Object Detection Github: https://github.com/jbwang1997/OBBDetection BboxToolkit: https://github.com/jbwang1997/BboxToolkit Paper: https://arxiv.org/abs/2104.11227 @ai_machinelearning_big_data

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🖊 Как внедряются элементы машинного обучения в службу поддержки: об этом рассказал Яндекс Go в новой статье на VC. Внутри можно узнать, каких результатов добилась компания и какие задачи решила благодаря SupportAI: VC: https://vc.ru/yandex.go/280385-yandeks-uluchshil-kachestvo-pismennoy-podderzhki-polzovateley-i-sokratil-rashody-bolee-chem-na-45 @ai_machinelearning_big_data

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Multiscale Vision Transformers: A hierarchical architecture for representing image and video information Facebook blog: https://ai.facebook.com/blog/multiscale-vision-transformers-an-architecture-for-modeling-visual-data/ Code: https://github.com/facebookresearch/SlowFast Pytorch video: https://github.com/facebookresearch/pytorchvideo Paper: https://arxiv.org/abs/2104.11227 @ai_machinelearning_big_data

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ESPnet: end-to-end speech processing toolkit Github: https://github.com/espnet/espnet Paper: https://arxiv.org/abs/2107.09011v2 Notebook: https://github.com/espnet/notebook @ai_machinelearning_big_data

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⚪ SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation (SIGGRAPH 2021) Github: https://github.com/liruihui/sp-gan Paper: https://arxiv.org/abs/2108.04476v1 Project: https://liruihui.github.io/publication/SP-GAN/ @ai_machinelearning_big_data

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🧹 Overview of data cleaning methods https://habr.com/ru/company/ru_mts/blog/571606/ @ai_machinelearning_big_data

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🎨 Paint Transformer: Feed Forward Neural Painting with Stroke Prediction Github: https://github.com/huage001/painttransformer Paper: https://arxiv.org/abs/2108.03798 Paddle Implementation: https://github.com/PaddlePaddle/PaddleGAN @ai_machinelearning_big_data

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🔍 Contrastive Sensor Fusion Github: https://github.com/descarteslabs/contrastive_sensor_fusion Paper: https://arxiv.org/abs/2108.05094v1 @ai_machinelearning_big_data

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📹 Internal Video Inpainting by Implicit Long-range Propagation Github: https://github.com/Tengfei-Wang/Implicit-Internal-Video-Inpainting Paper: https://arxiv.org/abs/2108.01912v1 4k Data: https://github.com/Tengfei-Wang/Annotated-4K-Videos Dataset: https://paperswithcode.com/dataset/videoremoval4k @ai_machinelearning_big_data

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👁 Improving Contrastive Learning by Visualizing Feature Transformation Github: https://github.com/DTennant/CL-Visualizing-Feature-Transformation Paper: https://arxiv.org/abs/2108.02982 @ai_machinelearning_big_data

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📸 Physics-based Noise Modeling for Extreme Low-light Photography Github: https://github.com/Vandermode/ELD Paper: https://arxiv.org/abs/2108.02158v1 Dataset: https://drive.google.com/drive/folders/1CT2Ny9W9ArdSQaHNxC5hGwav9lZHoqJa?usp=sharing @ai_machinelearning_big_data

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How to automate Hadoop administration without excruciating pain https://habr.com/ru/company/ru_mts/blog/569762/ @ai_machinelearning_big_data

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▶️ Deepminds's Perceiver: General Perception with Iterative Attention General architecture that works on many kinds of data https://deepmind.com/blog/article/building-architectures-that-can-handle-the-worlds-data Github: https://github.com/deepmind/deepmind-research/tree/master/perceiver Colab: https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/perceiver/colabs/masked_language_modelling.ipynb Paper: https://arxiv.org/abs/2103.03206 @ai_machinelearning_big_data

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