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Machine learning books and papers

Machine learning books and papers

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📈 Telegram kanali Machine learning books and papers analitikasi

Machine learning books and papers (@machine_learn) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 499 obunachidan iborat bo'lib, Taʼlim toifasida 8 053-o'rinni va Eron mintaqasida 13 774-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 24 499 obunachiga ega bo‘ldi.

30 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -131 ga, so‘nggi 24 soatda esa -4 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 7.24% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.98% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 1 773 marta ko‘riladi; birinchi sutkada odatda 484 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 1 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 01 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.

24 499
Obunachilar
-424 soatlar
-187 kunlar
-13130 kunlar
Postlar arxiv
The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry. This paper provides a comprehensive survey on this matter, categorizing more than 580 related articles into 22 well-defined fashion-related tasks. Such structured task-based multi-label classification of fashion research articles provides researchers with explicit research directions and facilitates their access to the related studies, improving the visibility of studies simultaneously. For each task, a time chart is provided to analyze the progress through the years. Furthermore, we provide a list of 86 public fashion datasets accompanied by a list of suggested applications and additional information for each. link: https://arxiv.org/abs/2111.00905 @Machine_learn

تفخیف 50% برای دوستان عزیز با زمان محدود. جهت خرید به ایدی بنده مراجعه کنین @Raminmousa

GoEmotions: A Dataset for Fine-Grained Emotion Classification http://ai.googleblog.com/2021/10/goemotions-dataset-for-fine-grained.html @Machine_learn

#RNN #Slide and #Survey @Machine_learn

Recurrent Neural Networks for Edge Intelligence: A Survey #Survey #RNN @Machine_learn

Survey on Recurrent Neural Network in Natural Language Processing #Survey #RNN @Machine_learn

Time Series Data Imputation: A Survey on Deep Learning Approaches #RNN #Survey @Machine_learn

A Critical Review of Recurrent Neural Networks for Sequence Learning #Survey #RNN @Machine_learn

Recurrent Neural Network TINGWU WANG, MACHINE LEARNING GROUP, UNIVERSITY OF TORONTO #Slide #RNN @Machine_learn

Computational Tutorial: An introduction to LSTMs in Tensorflow #Slide #RNN @Machine_learn

Introduction to RNNs! Arun Mallya! #RNN #Slide @Machine_learn

Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning Github: https://github.com/ethz-asl/Learn-to-Calibrate Paper: https://arxiv.org/abs/2109.14974v1 @Machine_learn

TensorFlow Model Optimization Toolkit — Collaborative Optimization API https://blog.tensorflow.org/2021/10/Collaborative-Optimizations.html @Machine_learn

تخفیف ۵۰٪ پکیچ تا پایان امشب @Raminmousa

Transfer Learning for Natural Language Processing #Book @Machine_learn

Discover the world of Machine Learning using Python algorithm analysis, ide and libraries. Projects focused on beginners. #Book @Machine_learn

Distributed Artificial Intelligence A Modern Approach Edited by Satya Prakash Yadav, Dharmendra Prasad Mahato, and Nguyen Thi Dieu Linh #Book @Machine_learn