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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 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
Postlar arxiv
Mini Course in Deep Learning with PyTorch for AIMS https://github.com/Atcold/pytorch-Deep-Learning-Minicourse

MorphNet: Towards Faster and Smaller Neural Networks http://ai.googleblog.com/2019/04/morphnet-towards-faster-and-smaller.html

YoloV3 Implemented in TensorFlow 2.0 https://github.com/zzh8829/yolov3-tf2

Take Your Best Selfie Automatically, with Photobooth on Pixel 3 http://ai.googleblog.com/2019/04/take-your-best-selfie-automatically.html

Week 8 (part c) CS294-158 Deep Unsupervised Learning (4/3/19) -- Ilya Sutskever https://www.youtube.com/watch?v=X-B3nAN7YRM

Основы Natural Language Processing для текста https://habr.com/ru/company/Voximplant/blog/446738/

How to Use Test-Time Augmentation to Improve Model Performance for Image Classification https://machinelearningmastery.com/how-to-use-test-time-augmentation-to-improve-model-performance-for-image-classification/

How to Configure Image Data Augmentation When Training Deep Learning Neural Networks https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/

Review: Residual Attention Network — Attention-Aware Features (Image Classification) https://towardsdatascience.com/review-residual-attention-network-attention-aware-features-image-classification-7ae44c4f4b8

How to Load Large Datasets From Directories for Deep Learning with Keras https://machinelearningmastery.com/how-to-load-large-datasets-from-directories-for-deep-learning-with-keras/

Recurrent Neural Networks in Python https://www.youtube.com/watch?v=kZPRyeiaBnc

Make Money with Tensorflow 2.0 https://www.youtube.com/watch?v=WS9Nckd2kq0

Zero to Cohort Analysis in 60 Minutes https://data.valorep.com/posts/p1_zero_to_cohorts/

How to Get Started With Deep Learning for Computer Vision (7-Day Mini-Course) https://machinelearningmastery.com/how-to-get-started-with-deep-learning-for-computer-vision-7-day-mini-course/

How to Load and Visualize Standard Computer Vision Datasets With Keras https://machinelearningmastery.com/how-to-load-and-visualize-standard-computer-vision-datasets-with-keras/

Introduction to Tensorflow 2.0 | Tensorflow 2.0 Features and Changes https://www.youtube.com/watch?v=3O-5DuqKaRo