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Machine learning books and papers
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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Show more📈 Analytical overview of Telegram channel Machine learning books and papers
Channel Machine learning books and papers (@machine_learn) in the English language segment is an active participant. Currently, the community unites 24 506 subscribers, ranking 8 028 in the Education category and 13 775 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 506 subscribers.
According to the latest data from 02 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -109 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.29%. Within the first 24 hours after publication, content typically collects 2.04% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 541 views. Within the first day, a publication typically gains 500 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as disorder, psy, مقاله, framework, graph.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Thanks to the high frequency of updates (latest data received on 03 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
24 506
Subscribers
+524 hours
-147 days
-10930 days
Posts Archive
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
@Machine_learn
OpenCV Sudoku Solver and OCR
https://www.pyimagesearch.com/2020/08/10/opencv-sudoku-solver-and-ocr/
@Machine_learn
کانال صنایع و مدیریت qie با همکاری شرکت داده کاوی ترامین برگزار میکند:
💻 دوره آنـــلاین داده کاوی
(Data Mining)
🌟 با ارایه گواهینامه
🔬 آموزش با مطالعات موردی واقعی و پروژه
✅ مناسب برای تمام رشتههای مهندسی، مدیریت و علوم پایه
🚧 در خــــانه بمانید و آمـــــوزش ببینید.
🔗 جزئیات، مشــاوره رایگان و کسب اطلاعات بیشتر:
🤳 @qie_admin
📲 09226072331
📲 09360017325
A DomainSpecific Supercomputer for Training Deep Neural Networks
#paper
@Machine_learn
Using Flask to optimize performance with Mask R-CNN segmentation(with source code)
https://medium.com/medialesson/using-flask-to-optimize-performance-with-mask-r-cnn-segmentation-39752f153029
@Machine_learn
How to Build a Custom YOLOv4 Object Detector using TensorFlow
@Machine_learn
https://morioh.com/p/f9702a8223b2
CODE: https://github.com/theAIGuysCode/tensorflow-yolov4-tflite
COALESCE framework
#paper
@Machine_learn
https://arxiv.org/pdf/2008.01936.pdf
Neural Networks: A Visual Introduction for Beginners by Michael Taylor
#book
@Machine_learn
Data Analytics
Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life
#book
@Machine_learn
Israeli Geometric Deep Learning Workshop
Many cool presentations at the recent DGL workshop, including Yaron Lipman, Gal Chechik, Gal Chechik, and many other experienced people in this field. The video is on YouTube.
@Machine_learn
Netflix’s Polynote is a New Open Source Framework to Build Better Data Science Notebooks
https://www.kdnuggets.com/2020/08/netflix-polynote-open-source-framework-better-data-science-notebooks.html
Project page: https://polynote.org/
Github: https://github.com/polynote/polynote
@Machine_learn
🔸لیستی از برترین کانالهای آموزشی در زمینه های هوشمصنوعی, پایتون و یادگیری ماشین
❯ هوش مصنوعی:
1️⃣ @Ai_Tv
2⃣ @HomeAI
❯ یادگیری ماشین و یادگیری عمیق :
1️⃣ @Machine_learn
❯ علم داده
1⃣ @mr_ie
❯ تنسورفلو
1⃣ @cvision
❯ آموزش پایتون و برنامه نویسی :
1️⃣ @pythony
2⃣ @pythonchallenge
3⃣ @Programming4all_0to100
Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python - 2020 #python #DL #tensorflow
@Machine_learn
Tonic deep reinforcement learning library #Rl
@Machine_learn
https://github.com/fabiopardo/tonic
A survey on generative adversarial networks and their variants methods
#GAN
#paper
#survey
@Machine_learn
How to Use XGBoost for Time Series Forecasting
https://machinelearningmastery.com/xgboost-for-time-series-forecasting/
@Machine_learn
AI-based Surveying the Impact of Environmental, Climatic, Economic and Demographic Conditions on the Pandemic Outbreak Rate of COVID-19
May 2020
DOI: 10.13140/RG.2.2.22571.87842/1
License: CC BY-NC-ND 4.0
Project: AI-based Surveying the Impact of Environmental, Climatic, Economic and Demographic Conditions on the Epidemic Outbreak Rate of Covid-19
Ramin MousaMehryar MajdMehryar MajdArsalan Mousazadeh
https://www.researchgate.net/publication/343303838_AI-based_Surveying_the_Impact_of_Environmental_Climatic_Economic_and_Demographic_Conditions_on_the_Pandemic_Outbreak_Rate_of_COVID-19/stats
Ramin:@Raminmousa
Arsalan: @arsalan_m
Object Detection with RetinaNet
@Machine_learn
https://keras.io/examples/vision/retinanet/
🔸لیستی از برترین کانالهای آموزشی در زمینه های هوشمصنوعی, پایتون و یادگیری ماشین
❯ هوش مصنوعی:
1️⃣ @Ai_Tv
2⃣ @HomeAI
❯ یادگیری ماشین و یادگیری عمیق :
1️⃣ @Machine_learn
❯ علم داده
1⃣ @python4finance
❯ آموزش پایتون و برنامه نویسی :
1️⃣ @pythony
2⃣ @pythonchallenge
3⃣ @Programming4all_0to100
Whole-Body Human Pose Estimation in the Wild
@Machine_learn
Github: https://github.com/jin-s13/COCO-WholeBody
Paper: https://arxiv.org/abs/2007.11858v1
Dataset: https://cocodataset.org/#keypoints-2017
