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 24 505 subscribers, ranking 8 028 in the Education category and 13 730 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 505 subscribers.

According to the latest data from 09 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -101 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.13%. Within the first 24 hours after publication, content typically collects 2.02% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 503 views. Within the first day, a publication typically gains 495 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
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 10 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.

24 505

Subscribers

No data24 hours

+17 days

-10130 days

1 503

Post views

~ 49524 hours

~ 61148 hours

6.13%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 505

Quantum Computing and Blockchain in Business (2020) #book #2020 #Blockchain @Machine_learn

24 505

Alternative data #book @Machine_learn

24 505

Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning http://ai.googleblog.com/2021/02/evaluating-design-trade-offs-in-visual.html @Machine_learn

24 505

TracIn — A Simple Method to Estimate Training Data Influence http://ai.googleblog.com/2021/02/tracin-simple-method-to-estimate.html @Machine_learn

24 505

Machine Learning for Computer Architecture http://ai.googleblog.com/2021/02/machine-learning-for-computer.html @Machine_learn

24 505

Practices of the Python Pro #book #python @Machine_learn

24 505

WeNet open source, production first and production ready end-to-end (E2E) speech recognition toolkit Github: https://github.com/mobvoi/wenet Paper: https://arxiv.org/abs/2102.01547v1 Tutorial: https://github.com/mobvoi/wenet/blob/main/docs/tutorial.md @Machine_learn

24 505

Open Datasets for Research During last week there were several news about newly open datasets for researchers. 1. Twitter opened “full history of public conversation” for academics (specifically, for academics): https://www.theverge.com/2021/1/26/22250203/twitter-academic-research-public-tweet-archive-free-access We can happily conduct researches about social networks graphs, users behavior and fake news (especially fake news🙃) without fighting with Twitter API. 2. Papers with code are now also Papers with Datasets: https://www.paperswithcode.com/datasets Not for only NLP, but for all fields structured for easy search and download. @Machine_learn

24 505

Feature Engineering for Machine Learning Principles and Techniques for Data Scientists #book @Machine_learn

24 505

Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE #paper @Machine_learn

24 505

#Pandas #python @Machine_learn

24 505

Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning http://ai.googleblog.com/2021/02/evaluating-design-trade-offs-in-visual.html @Machin_learn

24 505

Improving Mobile App Accessibility with Icon Detection http://ai.googleblog.com/2021/01/improving-mobile-app-accessibility-with.html @Machine_learn

24 505

TIN: Transferable Interactiveness Network Github: https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network HOI-Learning-List: https://github.com/DirtyHarryLYL/HOI-Learning-List Paper: https://arxiv.org/abs/2101.10292v1 @Machine_learn

24 505

🔸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 2⃣ @cvision ‏❯ علم داده: 1⃣ @mr_ie ‏❯ آموزش پایتون و برنامه نویسی : 1⃣ @pythony 2⃣ @pythonchallenge 3⃣ @Programming4all_0to100

24 505

Stabilizing Live Speech Translation in Google Translate http://ai.googleblog.com/2021/01/stabilizing-live-speech-translation-in.html @Machine_learn

24 505

Self-Adaptive Training Github: https://github.com/LayneH/self-adaptive-training Paper: https://arxiv.org/abs/2101.08732v1 @Machine_learn

24 505

سلام از دوستان كسي هست كه به #رايانش_تكاملي مسلط باشه ممنون ميشم بهم پيام بده @Raminmousa

24 505

A Visual Intro to NumPy and Data Representation . Link : https://jalammar.github.io/visual-numpy/ @Machine_learn

24 505

👉Lecture Notes for Linear Algebra Featuring Python . GitHub link : https://github.com/MacroAnalyst/Linear_Algebra_With_Python @Machine_learn