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
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📈 Telegram kanali Machinelearning analitikasi
Machinelearning (@ai_machinelearning_big_data) Rus til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 275 988 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 336-o'rinni va Rossiya mintaqasida 1 377-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 275 988 obunachiga ega bo‘ldi.
07 Noyabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -2 499 ga, so‘nggi 24 soatda esa -157 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 8.01% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 6.41% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 22 107 marta ko‘riladi; birinchi sutkada odatda 17 690 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 342 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 08 Noyabr, 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.
275 988
Obunachilar
-15724 soatlar
-8727 kun
-2 49930 kun
Postlar arxiv
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
👨🎓 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
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