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Machine learning books and papers

Machine learning books and papers

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📈 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
Learning Automata Based Sentiment Analysis for Recommender System on Cloud #Paper #SA @Machine_learn

Semantic Bottleneck Layers: Quantifying and Improving Inspectability of Deep Representations #paper @Machin_learn

Adversarial NLI: A New Benchmark for Natural Language Understanding @Machine_learn Dataset: https://github.com/facebookresearch/anli Paper: https://arxiv.org/abs/1910.14599

Mastering Python Scripting for System Administrators - 2019 #book #python @Machine_learn

Object oriented python tutorial #Python #book @Machine_learn

Forecasting.pdf0.13 KB

کانال خوبی در مورد معرفی رویدادهای مرتبط با هوش مصنوعی @eventai
کانال خوبی در مورد معرفی رویدادهای مرتبط با هوش مصنوعی @eventai

@Machine_learn BentoML BentoML is an open-source platform for high-performance ML model serving. https://github.com/bentoml/BentoML bentoml/BentoML

Python for Signal Processing #book #python @Machine_learn

🔸لیستی از برترین کانال‌های آموزشی در زمینه‌های هوش‌مصنوعی، علم داده , پایتون و یادگیری ماشین ‏❯ هوش مصنوعی : 1️⃣ @Ai_Tv 2⃣ @HomeAI ‏❯ هوش تجاری : 1️⃣ @BIMining ‏❯ یادگیری ماشین : 1️⃣ @Machine_learn ‏❯ آموزش پایتون و برنامه نویسی : 1⃣ @Programming4all_0to100 2⃣ @pythonchallenge 3⃣ @raspberry_python 4⃣ @Koolac_Org

Hands-On Meta Learning with Python #tensorflow #ML #book @Machine_learn

Deep Learning with TensorFlow 2 and Keras Second Edition #book #keras #DL #tensorflow @Machine_learn

@Machine_learn VirTex: Learning Visual Representations from Textual Annotations https://kdexd.github.io/virtex/ Github: https://github.com/kdexd/virtex Paper: arxiv.org/abs/2006.06666