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 24 508 subscribers, ranking 8 019 in the Education category and 13 748 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 508 subscribers.

According to the latest data from 04 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 3 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.50%. Within the first 24 hours after publication, content typically collects 2.21% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 594 views. Within the first day, a publication typically gains 541 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 05 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.

24 508

Subscribers

+324 hours

-97 days

-10130 days

1 594

Post views

~ 54124 hours

~ 66448 hours

6.50%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 508

#Chest Radiograph Pathology Categorization via Transfer Learning #Chapter13 @Machine_learn

24 508

#Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning #Chapter11 @Machine_learn

24 508

#Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching #Chapter9 @Machine_learn

24 508

#Deep Learning Tissue Segmentation in Cardiac Histopathology Images #Chapter8 @Machine_learn

24 508

#Deep Voting and Structured Regression for Microscopy Image Analysis #Chapter7 @Machine_learn

24 508

#Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images #Chapter6 @Machine_learn

24 508

#Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks #Chapter5 @Machine_learn

24 508

#Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition #Chapter4 @Machine_learn

24 508

#An Introduction to Deep Convolutional Neural Nets for Computer Vision #Chapter2 @Machine_learn

24 508

#An Introduction to Neural Networks and Deep Learning #Chapter1 @Machine_learn

24 508

#deep learning adaptive computation #book @Machine_learn

24 508

#learning predictive analytics with python #book #Machine_learn

24 508

#Datascience #MachineLearning #Artificialintelligence #Statistics

24 508

p.y.b: Here is a list of what I believe are the 10 Practical Steps for #DataScience: 1. Programming a. Python - https://lnkd.in/gGQ7cuv b. R - https://lnkd.in/giMGbph c. SQL - https://lnkd.in/gM8nMNP d. Command Line - https://lnkd.in/e3EQuis 2. Stats/Prob/Math a. Coursera's Statistics w/ R - https://lnkd.in/gGT9NEf b. edX's Probability - https://lnkd.in/gpUyC3P c. Khan Academy Linear Algebra - https://lnkd.in/gMshbX4 3. Data Viz a. Python Matplotlib- https://lnkd.in/gr3ifNt b. R ggplot2 - https://lnkd.in/eThJXNr 4. Data Manipulation a. Python Pandas - https://lnkd.in/g9kfpX4 b. R dplyr - https://lnkd.in/gAWusih 5. #MachineLearning a. Google Crash Course - https://lnkd.in/gSgkVcT b. Stanford Coursera - https://lnkd.in/g8ZG557 c. ISLR Book - https://lnkd.in/gk8GPZC 6. Experimental Design a. Udacity A/B Testing - https://lnkd.in/gCerh4f 7. Business Sense a. Metrics - https://lnkd.in/gZAG7bS 8. Communication a. Storytelling - https://lnkd.in/gwjxVUu 9. Profile Building a. GitHub - https://lnkd.in/g4r9naJ b. LinkedIn - https://lnkd.in/g-KHHEC c. Kaggle - https://lnkd.in/gBC77Hu d. DS Resume - https://lnkd.in/gU8WVAF 🏅 10. Job Search a. Daily Expert Tips & Advice - https://lnkd.in/g8z-xXD --- Hope this helps! 👍 Updated on my site - http://www.claoudml.co/

24 508

#Adrian_Rosebrock #deep_Learning #book @Machine_learn

24 508

#Reinforcement Learning Textbook - Sutton #book @Machine_learn

24 508

سلام از دوستان اگر کسی پایان نامش مرتبط با موضوع«بهبود استخراج قوانین انجمني با استفاده از روش های تکاملي» هستش لطفا جهت همکاری به این ایدی پیام بدن. با تشکر @mahdi7_7_7

24 508

#LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS - ICLR 2019 @Machine_learn

24 508

#deep learning and convolutional #book @Machine_learn

24 508

#deep learning adaptive comoutation #book @Machine_learn