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Machinelearning

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

Ko'proq ko'rsatish

📈 Telegram kanali Machinelearning analitikasi

Machinelearning (@ai_machinelearning_big_data) Rus til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 292 388 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 328-o'rinni va Rossiya mintaqasida 1 290-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 292 388 obunachiga ega bo‘ldi.

08 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -6 274 ga, so‘nggi 24 soatda esa -221 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 7.46% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 5.47% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 21 812 marta ko‘riladi; birinchi sutkada odatda 16 003 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 09 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.

292 388
Obunachilar
-22124 soatlar
-1 3547 kunlar
-6 27430 kunlar
Postlar arxiv
Dimensionality Reduction For Dummies — Part 2: Laying The Bricks https://towardsdatascience.com/data-science/home

Playing Mortal Kombat with TensorFlow.js. Transfer learning and data augmentation https://blog.mgechev.com/2018/10/20/transfer-learning-tensorflow-js-data-augmentation-mobile-net/

How to analyze “Learning”: Short tour of Computational Learning Theory https://towardsdatascience.com/how-to-analyze-learning-short-tour-of-computational-learning-theory-9d93b15fc3e5

Curiosity and Procrastination in Reinforcement Learning https://ai.googleblog.com/2018/10/curiosity-and-procrastination-in.html

Deep Learning and Reinforcement Learning Summer School, Toronto 2018 video: http://videolectures.net/DLRLsummerschool2018_toronto/

How linear algebra is applied in machine learning. When you study an abstract subject like linear algebra, you may wonder: why do you need all these vectors and matrices? Well, if you study it with the purpose of doing ML, this is the answer for you: http://amp.gs/vtWx

Digging into Airbnb data: reviews sentiments, superhosts, and prices prediction (part1) Example of #AirBnB data research Link: https://towardsdatascience.com/digging-into-airbnb-data-reviews-sentiments-superhosts-and-prices-prediction-part1-6c80ccb26c6a

mmdetection mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. Major features - Modular Design One can easily construct a customized object detection framework by combining different components. - Support of multiple frameworks out of box The toolbox directly supports popular detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc. - Efficient All basic bbox and mask operations run on GPUs now. The training speed is about 5% ~ 20% faster than Detectron for different models. - State of the art This was the codebase of the MMDet team, who won the COCO Detection 2018 challenge. https://github.com/open-mmlab/mmdetection

Google 2019 research internships https://t.co/rxmLEPLsir

SOTAWHAT - A script to keep track of state-of-the-art AI research https://huyenchip.com/2018/10/04/sotawhat.html https://github.com/chiphuyen/sotawhat.

Top AI Interview Questions & Answers — Acing the AI Interview https://medium.com/acing-ai/top-ai-interview-questions-answers-acing-the-ai-interview-61bf52ca34d4

Introduction to forecasting with FB Prophet https://www.interviewqs.com/ddi_code_snippets/prophet_intro