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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 292 Texnologiyalar & Aralashmalar328...

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Machinelearning (@ai_machinelearning_big_data) Rus til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 292 329 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 328-o'rinni va Rossiya mintaqasida 1 292-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 292 329 obunachiga ega bo‘ldi.

09 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -6 200 ga, so‘nggi 24 soatda esa -193 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 7.33% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 5.53% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 21 439 marta ko‘riladi; birinchi sutkada odatda 16 173 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 154 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 10 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.

292 329

Obunachilar

-19324 soatlar

-1 2987 kunlar

-6 20030 kunlar

21 439

Post ko'rishlar

~ 16 17324 soatlar

~ 17 79848 soatlar

7.33%

Muloqot nisbati

~ 6

Kuniga postlar

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Postlar arxiv

292 341

Introducing the Unrestricted Adversarial Examples Challenge https://ai.googleblog.com/2018/09/introducing-unrestricted-adversarial.html

292 341

GMM-UBM и алгоритма MAP adaptation https://habr.com/post/420515/

292 341

Highlights from the European Conference on Computer Vision 2018 https://blog.alookanalytics.com/2018/09/12/highlights-from-the-european-conference-on-computer-vision-2018/

292 341

Healthcare Dataset with #PySpark Back to some #BigData stuff: article about quick-and-dirty development of prediction algorithm using #Spark engine. Link: https://towardsdatascience.com/healthcare-dataset-with-spark-6bf48019892b

292 341

Simple introduction to the ALU for neural networks: explanation, physical meaning and implementation https://habr.com/post/422777/

292 341

Lite Intro into Reinforcement Learning https://towardsdatascience.com/lite-intro-into-reinforcement-learning-857ca5c924d9?gi=692d05ca6c3d

292 341

Conceptual Captions: A New Dataset and Challenge for Image Captioning https://ai.googleblog.com/2018/09/conceptual-captions-new-dataset-and.html

292 341

Analysis of the tonality of texts using convolutional neural networks https://habr.com/company/mailru/blog/417767/

292 341

Google launches new search engine to help scientists find the datasets they need https://www.theverge.com/2018/9/5/17822562/google-dataset-search-service-scholar-scientific-journal-open-data-access

292 341

Intel AI Interview Questions — Acing the AI Interview https://medium.com/acing-ai/intel-ai-interview-questions-acing-the-ai-interview-d4a62dbb593c

292 341

11-485/785 Introduction to Deep Learning Fall 2018 http://deeplearning.cs.cmu.edu/

292 341

Глагол «грокать», придуманный фантастом Робертом Хайнлайном более полувека назад, означает знать и понимать что‑либо так, что это стало частью тебя, частью твоей жизни. Разница между «грокать» и просто «знать» — как между владением родным языком и хорошо изученным иностранным. Рекомендуем отличный канал — @Groks о технологиях и аналитике. Там вы найдете отчёты, данные, графики, новости, подборки статей на русском и английском, и авторское описание последних событий по заданной теме. В общем, @Groks — канал для диджитал гроккеров, для тех, кто не смотрит на мир диджитал со стороны, а живёт в нём.

292 341

How and When to Use a Calibrated Classification Model with scikit-learn https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/

292 341

Neural nets in Android, Google ML Kit https://habr.com/post/422041/

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Introducing a New Framework for Flexible and Reproducible Reinforcement Learning Research https://ai.googleblog.com/2018/08/introducing-new-framework-for-flexible.html

292 341

A Style-Aware Content Loss for Real-time HD Style Transfer Source code for the ECCV18 paper: https://github.com/CompVis/adaptive-style-transfer

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What are the experts in data analysis really doing? Conclusions from 35 interviews https://habr.com/company/wirex/blog/421845/

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How and When to Use ROC Curves and Precision-Recall Curves for Classification in Python https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/

292 341

The neural network was trained to recognize depression by an arbitrary human speech without context https://habr.com/post/421775/

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HyperparameterHunter Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries https://github.com/HunterMcGushion/hyperparameter_hunter