Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Machine learning books and papers
Kanalga Telegram’da o‘tish
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Ko'proq ko'rsatish📈 Telegram kanali Machine learning books and papers analitikasi
Machine learning books and papers (@machine_learn) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 517 obunachidan iborat bo'lib, Taʼlim toifasida 8 031-o'rinni va Eron mintaqasida 13 728-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 24 517 obunachiga ega bo‘ldi.
26 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -162 ga, so‘nggi 24 soatda esa -2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 5.76% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.79% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 412 marta ko‘riladi; birinchi sutkada odatda 440 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 27 Iyun, 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.
24 517
Obunachilar
-224 soatlar
-337 kunlar
-16230 kunlar
Postlar arxiv
📌skscope: Fast Sparse-Constraint Optimization
🖥 Github: https://github.com/abess-team/skscope
📕 Paper: https://arxiv.org/abs/2403.18540v1
🔥Dataset: skscope.readthedocs.io
Topics
@Machine_learn
با عرض سلام به خاطر ماه مبارك رمضا دو پكيچ يادگيري ماشين و يادگيري عميق با تخفيف ٧٥٪ براي دوستان در نظر گرفتيم.
دوستاني كه نياز دارند به ايدي بنده پيام بدن.
@Raminmousa
🖼 One-Step Image Translation with Text-to-Image Models
CycleGAN-Turbo
▪Paper: https://arxiv.org/abs/2403.12036
▪Code: https://github.com/GaParmar/img2img-turbo
▪Demo: http://huggingface.co/spaces/gparmar/img2img-turbo-sketch
@Machine_learn
جهت استفاده از تخفیف این دو پکیچ یادگیری بنده می تونین با ایدیم در ارتباط باشین
@Raminmousa
The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages.
#interviews #datascience #python
https://t.me/DataScienceQ
با عرض سلام نياز به نفر دوم اين مقاله داريم.
ابتدا اركايو مقاله تا دو هفته ديگه فرستاده ميشه سپس براي knowledge based فرستاده ميشه. كسايي كه نياز دارن به بنده مراجعه كنن
@Raminmousa
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
🖥 Github: https://github.com/opengvlab/video-mamba-suite
📕 Paper: https://arxiv.org/abs/2403.09626v1
🔥Dataset: https://paperswithcode.com/dataset/egoschema
@Machine_learn
با عرض سلام دوستانی که مقاله برای Knowledge-based Systems می فرستن می تونن من رو به عنوان reviewer معرفی کنن تا مقالاتشون رو بررسی کنم.
https://www.sciencedirect.com/journal/knowledge-based-systems
@Machine_learn
TSMixer: An All-MLP Architecture for Time Series Forecasting
Time-series datasets in real-world scenarios are inherently multivariate and riddled with intricate dynamics. While recurrent or attention-based deep learning models have been the go-to solution to address these complexities, recent discoveries have shown that even basic univariate linear models can surpass them in performance on standard academic benchmarks. As an extension of this revelation, the paper introduces the Time-Series Mixer TSMixer. This innovative design, crafted by layering multi-layer perceptrons, hinges on mixing operations across both time and feature axes, ensuring an efficient extraction of data nuances.
Upon application, TSMixer has shown promising results. Not only does it hold its ground against specialized state-of-the-art models on well-known benchmarks, but it also trumps leading alternatives in the challenging M5 benchmark, a dataset that mirrors the intricacies of retail realities. The paper's outcomes emphasize the pivotal role of cross-variate and auxiliary data in refining time series forecasting.
Paper link:
https://arxiv.org/abs/2303.06053
Code link: https://github.com/google-research/google-research/tree/master/tsmixer
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-tsmixer
@Machine_learn
با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم این تخفیف اخرین سری از تخفیف های این دو پکیچ می باشد
1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)
جهت تهیه می تونین به ایدی بنده مراجعه کنین
@Raminmousa
ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions
🖥 Github: https://github.com/Traffic-X/ViT-CoMer
📕 Paper: https://arxiv.org/pdf/2403.07392.pdf
✨ Tasks: https://paperswithcode.com/task/object-detection
🔥Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn
