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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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Tarmoq:Machinelearning Rossiya1 282 Texnologiyalar & Aralashmalar328...

📈 Telegram kanali Machinelearning analitikasi

Machinelearning (@ai_machinelearning_big_data) Rus til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 292 839 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 328-o'rinni va Rossiya mintaqasida 1 282-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 292 839 obunachiga ega bo‘ldi.

06 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -6 314 ga, so‘nggi 24 soatda esa -187 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 7.37% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 5.45% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 21 579 marta ko‘riladi; birinchi sutkada odatda 15 979 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 159 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 07 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.

292 839

Obunachilar

-18724 soatlar

-1 3257 kunlar

-6 31430 kunlar

21 579

Post ko'rishlar

~ 15 97924 soatlar

~ 17 85348 soatlar

7.37%

Muloqot nisbati

~ 6

Kuniga postlar

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Postlar arxiv

292 878

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

292 878

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

292 878

How to Perform Face Detection with Deep Learning in Keras https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/

292 878

How Computers SeeIntroduction to Convolutional Neural Networks

292 878

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

292 878

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

292 878

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

292 878

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

292 878

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

292 878

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/

292 878

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

292 878

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

292 878

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

292 878

Augmented Neural ODEs Github: https://github.com/EmilienDupont/augmented-neural-odes Article: https://arxiv.org/abs/1904.01681

292 878

Unsupervised Learning with Graph Neural Networks video: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule guide: http://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf

292 878

How to Perform Object Detection With YOLOv3 in Keras https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/

292 878

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

292 878

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

292 878

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

292 878

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