Machine learning books and papers

Open in Telegram

Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

📈 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 24 502 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 24 502 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.

24 502

Subscribers

+524 hours

-147 days

-10930 days

1 541

Post views

~ 50024 hours

~ 61348 hours

6.29%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 501

📈 کارگاه Kernel Methods for Pattern Analysis 📅 تاریخ برگزاری: پنج شنبه ۲۷ آذر از ساعت ۱۳ الی ۱۶.۳۰ و جمعه ۲۸ آذر ۱۰ الی ۱۶.۳۰ 🖥 این دوره به صورت آنلاین برگزار خواهد شد. 🔖 ثبت نام زود هنگام این دوره را از دست ندهید! ⭕️ برای مشاهده جزئیات بیشتر و ثبت نام روی این لینک کلیک کنید. ❌ دوره دارای ظرفیت محدود می باشد. 🕙 مدت این دوره: 8 ساعت ➖➖➖➖➖➖➖➖ @LoopAcademy

24 501

Lipton, A., & de Prado, M. L. (2020). A closed-form solution for optimal mean-reverting trading strategies. #paper @Machine_learn

24 501

AN INTRODUCTION TO ALGORITHMIC TRADING Basic to Advanced Strategies #book @Machine_learn

24 501

Weight Agnostic Neural Networks link: https://weightagnostic.github.io/ @Machine_learn

24 501

Implementation of Adam and AMSGrad Optimizers Project: https://lab-ml.com/labml_nn/optimizers/adam.html AMSGrad: https://lab-ml.com/labml_nn/optimizers/amsgrad.html GitHub: https://github.com/lab-ml/nn Full list: https://lab-ml.com/labml_nn/index.html 👌 @Machine_learn

24 501

pixelNeRF: Neural Radiance Fields from One or Few Images. Github: https://github.com/sxyu/pixel-nerf Paper: http://arxiv.org/abs/2012.02190 @Machine_learn

24 501

Fully Convolutional Networks for Panoptic Segmentation. Github: https://github.com/yanwei-li/PanopticFCN Paper: https://arxiv.org/pdf/2012.00720.pdf @Machine_learn

24 501

Using AutoML for Time Series Forecasting http://ai.googleblog.com/2020/12/using-automl-for-time-series-forecasting.html @Machine_learn

24 501

This channel has launced to introduce books and educational videos in the field of artificial intelligence.

24 501

Animating Pictures with Eulerian Motion Fields New method for single image animation. Authors promised to release code soon! Website: https://eulerian.cs.washington.edu Paper: https://eulerian.cs.washington.edu/animating_pictures_2020.pdf ArXiV: https://arxiv.org/abs/2011.15128 YouTube: https://www.youtube.com/watch?v=4zKliOMilGY #DL #animation #WashingtonUni @Machine_learn

24 501

Using AutoML for Time Series Forecasting http://ai.googleblog.com/2020/12/using-automl-for-time-series-forecasting.html @Machine_learn

24 501

CraCking Codes with Python #book #python @Machine_learn

24 501

Text Extraction from a Table Image, using PyTesseract and OpenCV https://levelup.gitconnected.com/text-extraction-from-a-table-image-using-pytesseract-and-opencv-3342870691ae @Machine_learn

24 501

https://arxiv.org/pdf/2011.09055.pdf #GAN #Paper @Machine_learn

24 501

Tutorial on Generative Adversarial Networks (GANs) with Keras and TensorFlow Nice tutorial with enough theory to understand what you are doing and code to get it done. Link: https://www.pyimagesearch.com/2020/11/16/gans-with-keras-and-tensorflow/ #Keras #TensorFlow #tutorial #wheretostart #GAN @Machine_learn

24 501

Free Course Advanced Computer Vision with TensorFlow https://www.coursera.org/learn/advanced-computer-vision-with-tensorflow @Machine_learn

24 501

Learning Efficient GANs using Differentiable Masks and Co-Attention Distillation Github: https://github.com/SJLeo/DMAD Paper: https://arxiv.org/abs/2011.08382 @Machine_learn

24 501

Using GANs to Create Fantastical Creatures http://ai.googleblog.com/2020/11/using-gans-to-create-fantastical.html @Machine_learn

24 501

Edge AI Convergence of Edge Computing and Artificial Intelligence #book #AI @Machine_learn

24 501

Introduction to Deep Learning Using R A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R #book #DL @Machine_learn