Machine learning books and papers

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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

24 502

Applied Machine Learning with Python #book #ml Andrea Giussani (2020) @Machine_learn

24 502

PIFuHD: new state of the art high-quality 3D reconstruction of humans from a single image 🌐 github.com/facebookresearch/pifuhd 📝 arxiv.org/abs/2004.00452 📉 @Machine_learn

24 502

نسأل الله أن يتقبل منا ومنكم صالح الأعمال، عيدكم مبارك🤍. @Raminmousa

24 502

👶 BabyAI 1.1 ➡️@Machine_learn BabyAI is a platform used to study the sample efficiency of grounded language acquisitio Github: https://github.com/mila-iqia/babyai https://github.com/mila-iqia/babyai Paper: https://arxiv.org/abs/2007.12770v1 @ai_machinelearning_big_data

24 502

TensorFlow 2.3 is now officially released @Machine_learn https://blog.tensorflow.org/2020/07/whats-new-in-tensorflow-2-3.html

24 502

PyTorch 1.6.0 Release @Machine_learn https://github.com/pytorch/pytorch/releases/tag/v1.6.0

24 502

Deep Learning and the Game of Go #book #Dl @Machine_learn

24 502

Learning perturbation sets for robust machine learning ➡️@Machine_learn Git: https://locuslab.github.io/2020-07-20-perturbation/ Code: https://github.com/locuslab/perturbation_learning Paper: https://arxiv.org/abs/2007.08450

24 502

Data Analysis A Model Comparison Approach to Regression, ANOVA, and Beyond Third Edition #book @Machine_learn

24 502

Private prediction methods: A systematic study by Facebook Research ➡️@Machine_learn https://ai.facebook.com/blog/private-prediction-methods-a-systematic-study/ Github: https://github.com/facebookresearch/private_prediction Paper: https://arxiv.org/pdf/2007.05089.pdf @ai_machinelearning_big_data

24 502

Learn Data Analysis with Python Lessons in Coding #book #python @Machine_learn

24 502

(Re)Discovering Protein Structure and Function Through Language Modeling @Machine_learn Blog: https://blog.einstein.ai/provis/ Paper: https://arxiv.org/abs/2006.15222 Code: https://github.com/salesforce/provis #DL #NLU #proteinmodelling #bio #biolearning #insilico

24 502

🔸لیستی از برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی، علم داده , پایتون و یادگیری ماشین ‏❯ هوش مصنوعی: 1️⃣ @Ai_Tv 2⃣ @HomeAI ‏❯ پایگاه داده و تنسورفلو: 1️⃣ @cvision 2️⃣ @SQL_Server ‏❯علم داده 1⃣ @mr_ie ‏❯ یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn ‏❯ هوش تجاری : 1️⃣ @BIMining ‏❯ آموزش پایتون و برنامه نویسی : 1️⃣ @pythony 2⃣ @pythonchallenge 3⃣ @Programming4all_0to100

24 502

Fast and Accurate Neural CRF Constituency Parsing @Machine_learn Github: https://github.com/yzhangcs/parser Paper: https://www.ijcai.org/Proceedings/2020/560

24 502

📘 :Introduction To Graph Neural Network #book #Graph @Machine_learn

24 502

@Machine_learn Auto Rotate Images Using Deep Learning https://www.kdnuggets.com/2020/07/auto-rotate-images-deep-learning.html

24 502

Call For Chapter #Machine_learn @Machine_learn

24 502

Building Machine Learning Powered Applications Going from Idea to Product Emmanuel Ameisen #book #ML @Machine_learn

24 502

Jukebox: a new generative model for audio from OpenAI. @Machine_learn openai.com/blog/jukebox Article: cdn.openai.com/papers/jukebox.pdf Examples: https://jukebox.openai.com/ Code: https://github.com/openai/jukebox

24 502

🔸لیستی از برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی، علم داده , پایتون و یادگیری ماشین ‏❯ هوش مصنوعی: 1️⃣ @Ai_Tv 2⃣ @HomeAI ‏❯ علم داده: 1️⃣ @DataAnalysis 2️⃣ @python4finance 3⃣ @mr_ie ‏❯ یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn ‏❯ هوش تجاری : 1️⃣ @BIMining ‏❯ آموزش پایتون و برنامه نویسی : 1️⃣ @pythony 2⃣ @pythonchallenge 3⃣ @Programming4all_0to100