uz
Feedback
Machine learning books and papers

Machine learning books and papers

Kanalga Telegram’da o‘tish

📈 Telegram kanali Machine learning books and papers analitikasi

Machine learning books and papers (@machine_learn) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 509 obunachidan iborat bo'lib, Taʼlim toifasida 8 019-o'rinni va Eron mintaqasida 13 748-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 24 509 obunachiga ega bo‘ldi.

04 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -101 ga, so‘nggi 24 soatda esa 3 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 6.50% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.21% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 1 594 marta ko‘riladi; birinchi sutkada odatda 541 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 2 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent disorder, psy, مقاله, framework, graph kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

Yuqori yangilanish chastotasi (oxirgi ma’lumot 05 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

24 509
Obunachilar
+324 soatlar
-97 kunlar
-10130 kunlar
Postlar arxiv
Bayesian Reasoning and Machine Learning — D. Barber (en) 2012/2017. #book #beginner #theory @Machine_learn
Bayesian Reasoning and Machine Learning — D. Barber (en) 2012/2017. #book #beginner #theory @Machine_learn

#Frank Kane — Frank Kane's Taming Big Data with Apache Spark and Python (en) 2017 #book #python @Machine_learn

#Frank Kane — Frank Kane's Taming Big Data with Apache Spark and Python (en) 2017 #book #python @Machine_learn
#Frank Kane — Frank Kane's Taming Big Data with Apache Spark and Python (en) 2017 #book #python @Machine_learn

Data Science Fundamentals for Python and MongoDB — D. Paper (en) 2018 #book @Machine_learn

Data Science Fundamentals for Python and MongoDB — D. Paper (en) 2018 #book @Machine_learn
Data Science Fundamentals for Python and MongoDB — D. Paper (en) 2018 #book @Machine_learn

#Veracity of Big Data — V. Pendyala (en) 2018 #book #middle @Machine_learn

#Veracity of Big Data — V. Pendyala (en) 2018 #book #middle @Machine_learn
#Veracity of Big Data — V. Pendyala (en) 2018 #book #middle @Machine_learn

#Machinelearning for beginner s #page count=128 #Year=2017 #book @Machine_learn

#Machinelearning for beginner s #page count=128 #Year=2017 #book @Machine_learn
#Machinelearning for beginner s #page count=128 #Year=2017 #book @Machine_learn

#deeplearning #j.patterson and Adam #book #page count=523 @Machine_learn

#deeplearning #j.patterson and Adam #book #page count=523 @Machine_learn
#deeplearning #j.patterson and Adam #book #page count=523 @Machine_learn

# Smart Grid using Big Data Analytics: A #Random Matrix Theory Approach — R. C. Qiu, P. Antonik (en) 2017 #book @Machine_learn

#text analytics with python #book #Machine_learn

#Machine_learning and security #book @Machine_learn

#next-generation big data #big-data @Machine_learn

#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018 #book @Machine_learn

#Keras Deep Learning Cookbook: Over 80 Recipes for Implementing Deep Neural Networks in Python #book @Machine_learn

Python 3 Object oriented Programming #book @Machine_learn

#Deep learning with keras @Machine_learn