Machine learning books and papers

Open in Telegram

Admin: @Raminmousa1 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 24 442 subscribers, ranking 8 034 in the Education category and 14 033 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 442 subscribers.

According to the latest data from 25 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -28 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 7.81%. 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 1 910 views. Within the first day, a publication typically gains 494 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
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: @Raminmousa1 ID: @Machine_learn link: https://t.me/Machine_learn”

Thanks to the high frequency of updates (latest data received on 26 August, 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.

24 442

Subscribers

-524 hours

-87 days

-2830 days

1 910

Post views

~ 49424 hours

~ 67048 hours

7.81%

Engagement rate

No data

Posts per day

Ads index

beta

Data loading in progress...

Similar Channels

77.3K

Data Science & Machine Learning

Best Offers - Courses, Books etc

2.4K

علوم آماری و محاسباتی

More channels

Incoming and Outgoing Mentions

---

Attracting Subscribers

August '26

+68

in 1 channels

July '26

+83

in 1 channels

Get PRO

June '26

+44

in 1 channels

Get PRO

May '26

+45

in 2 channels

Get PRO

April '26

+45

in 0 channels

Get PRO

March '26

in 0 channels

Get PRO

February '26

+14

in 1 channels

Get PRO

January '26

+10

in 1 channels

Get PRO

December '25

+251

in 2 channels

Get PRO

November '25

+522

in 11 channels

Get PRO

October '25

+1 065

in 2 channels

Get PRO

September '25

+794

in 2 channels

Get PRO

August '25

+941

in 11 channels

Get PRO

July '25

+835

in 3 channels

Get PRO

June '25

+734

in 3 channels

Get PRO

May '25

+956

in 2 channels

Get PRO

April '25

+1 898

in 4 channels

Get PRO

March '25

+658

in 7 channels

Get PRO

February '25

+44

in 4 channels

Get PRO

January '25

+92

in 8 channels

Get PRO

December '24

+134

in 5 channels

Get PRO

November '24

+394

in 13 channels

Get PRO

October '24

+463

in 6 channels

Get PRO

September '24

+249

in 4 channels

Get PRO

August '24

+387

in 3 channels

Get PRO

July '24

+220

in 1 channels

Get PRO

June '24

+228

in 11 channels

Get PRO

May '24

+422

in 2 channels

Get PRO

April '24

+611

in 11 channels

Get PRO

March '24

+731

in 4 channels

Get PRO

February '24

+1 021

in 1 channels

Get PRO

January '24

+1 100

in 13 channels

Get PRO

December '23

+628

in 0 channels

Get PRO

November '23

+409

in 11 channels

Get PRO

October '23

+296

in 9 channels

Get PRO

September '23

+365

in 0 channels

Get PRO

August '23

+467

in 0 channels

Get PRO

July '23

+715

in 0 channels

Get PRO

June '23

+268

in 0 channels

Get PRO

May '23

+107

in 0 channels

Get PRO

April '23

+107

in 0 channels

Get PRO

March '23

+249

in 0 channels

Get PRO

February '23

+110

in 0 channels

Get PRO

January '23

+163

in 0 channels

Get PRO

December '22

+118

in 0 channels

Get PRO

November '22

+165

in 0 channels

Get PRO

October '22

+101

in 0 channels

Get PRO

September '22

+177

in 0 channels

Get PRO

August '22

+220

in 0 channels

Get PRO

July '22

+229

in 0 channels

Get PRO

June '22

+56

in 0 channels

Get PRO

May '22

+296

in 0 channels

Get PRO

April '22

+411

in 0 channels

Get PRO

March '22

+47

in 0 channels

Get PRO

February '22

+34

in 0 channels

Get PRO

January '22

+44

in 0 channels

Get PRO

December '21

+59

in 0 channels

Get PRO

November '21

+59

in 0 channels

Get PRO

October '21

+109

in 0 channels

Get PRO

September '21

+454

in 0 channels

Get PRO

August '21

+251

in 0 channels

Get PRO

July '21

+84

in 0 channels

Get PRO

June '21

+183

in 0 channels

Get PRO

May '21

+126

in 0 channels

Get PRO

April '21

+116

in 0 channels

Get PRO

March '21

+101

in 0 channels

Get PRO

February '21

+601

in 0 channels

Get PRO

January '21

+167

in 0 channels

Get PRO

December '20

+8 834

in 0 channels

Date	Subscriber Growth	Mentions	Channels
26 August	0
25 August	0
24 August	0
23 August	0
22 August	+7
21 August	0
20 August	+2
19 August	+7
18 August	0
17 August	+3
16 August	+2
15 August	+9
14 August	+1
13 August	0
12 August	0
11 August	+1
10 August	+3
09 August	0
08 August	+10
07 August	+3
06 August	0
05 August	0
04 August	0
03 August	0
02 August	+2
01 August	+18

Channel Posts

🔖 ML algorithms in visualizations A useful repository that helps you understand how machine learning algorithms work – through interactive diagrams and step-by-step explanations. You can run it in your browser or locally using Docker. ⛓ Link to GitHub https://github.com/gavinkhung/machine-learning-visualized @Machine_learn

2	"The Mathematics of Bitcoin" is a concise work that analyzes Bitcoin from a mathematical perspective. 📊 It utilizes probability theory, stochastic processes, martingales, combinatorics, and special functions to explore the mechanisms of the Bitcoin protocol. 🧮 In particular, the authors examine the probability of double-spending, the profitability of mining, block generation, miner strategies, and the resilience of the protocol. ⛏️ If you want to delve deeper, I also recommend "Bitcoin and Cryptocurrency Technologies" from Princeton University. This is a much broader introduction to cryptographic hash functions, digital signatures, consensus, Proof of Work, mining, transactions, anonymity, security, and the incentive system in cryptocurrencies. 🎓 The Mathematics of Bitcoin: https://arxiv.org/pdf/2003.00001 Bitcoin and Cryptocurrency Technologies: https://d28rh4a8wq0iu5.cloudfront.net/bitcointech/readings/princeton_bitcoin_book.pdf @Machine_learn	1 033
3	🔥 8 skills = 8 free certifications >>> AI (Microsoft) - https://learn.microsoft.com/en-us/training/paths/get-started-artificial-intelligence/ Deep learning (NVIDIA) - https://learn.nvidia.com/en-us/training/self-paced-courses Data science (IBM) - https://skillsbuild.org/students/course-catalog/data-science Data Analyst (Microsoft) - https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/ Python (Microsoft) - https://learn.microsoft.com/en-us/shows/intro-to-python-development/ SQL (Infosys) - https://www.coursejoiner.com/freeonlinecourses/infosys-free-certification-course-9/ Java (Infosys) - https://www.coursejoiner.com/uncategorized/infosys-launched-free-java-certification-course/ Cloud computing (AWS) - https://explore.skillbuilder.aws/learn/course/134/aws-cloud-practitioner-essentials @Machine_learn	1 686
4	اخرین زمان سابمیت این مقاله امشب...! @Raminmousa1	1 870
5	با عرض سلام در مقاله زیر جهت سابمیت نیاز به نفر دوم داریم Title: FFChurn: Fusion Former for Customer Churn Classification Based on Transformer, FEDformer, and Informer Abstract: Customer churn prediction is a key issue in customer relationship management that directly impacts organizational profitability and has created challenges for researchers and organizations. Machine learning (ML), Ensemble Learning (EL), and Deep Learning (DL) models have achieved comparable results on this problem. In this study, Fusion Former was introduced, integrating the FEDformer, Informer, and Transformer architectures to simultaneously extract local features and long-term dependencies. The pipeline for this model includes denoising with a wavelet transform, Min-Max normalization, and hybrid adaptive feature selection based on mutual information (MI), recursive feature elimination (RFE), and the Boruta algorithm. Four different versions of the model, including binary and ternary object fusion, were evaluated on two datasets. The results showed that the Fusion Former (FED+INF+Transformer) model, with F1 scores of 0.9876 on the Dataset 1 and 0.8887 on the Dataset 2, outperformed classical machine learning models, multilayer neural networks, and other binary combinations. Also, the sensitivity analysis of hyperparameters, which included changes in the cost function, batch size, and dropout size, methods for dealing with data imbalance, which included Smote, TMG-GAN, Ib-gan, T-SMOTE approaches, and the effect of feature selection, which included four methods: MI, RFE, Boruta, and Adaptive FS (Boruta+MI+RFE), confirmed the superiority and relative stability of the proposed model. Price:250$ @Raminmousa1 @Machine_learn	2 024
6	report2 (1).pdf	2 049
7	🔖 Learning Data Science through interactive examples One of the most useful repositories for those who want to better understand machine learning. It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results. ⛓ Link to GitHub https://github.com/GeostatsGuy/DataScienceInteractivePython @Machine_learn	2 258
8	Attention Heatmap vs Token Pruning 🔍✂️ 🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms #AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM @Machine_learn	1 918
9	با عرض سلام سه موضوع زیر جهت نگارش مقالات مدنظر داریم. که در هر سه مقاله به دو جایگاه نیاز داریم. مقالات کاملا مشارکتی هست و علاوه بر تقبل هزینه کار نیز باید انجام بشه. 1: Survey on knowledge graph and large language models _ auth2: 300$ _auth3:200$ 2: Survey on challenges of large language models _ auth2: 300$ _auth3:200$ 3: New learning model for skin cancer detection _ auth2: 300$ _auth3:200$ جهت مشارکت میتونین با ایدی بنده در ارتباط باشین. زمان شروع هر مقاله یک هفته بعد از تشکیل تیم. @Raminmousa1 @Machine_learn	2 154
10	🔖 One of the most useful books on Agentic AI This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents. It's an excellent option to get a holistic picture and understand which topics deserve deeper study. ⛓️ Link to the book https://arxiv.org/abs/2606.24937 @Machine_learn	1 721
11	Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers 🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars. 📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context. 📖 It contains 20 chapters: * Vectors, matrices, calculus * Statistics and probability * Machine learning and deep learning * NLP, computer vision, audio/speech * Multimodal learning and autonomous systems * GNN, OS, algorithms * Production engineering, GPU/SIMD * AI inference, ML systems design, and applied AI 💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI. 🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium @Machine_learn	1 591
12	10 GitHub repositories that are worth checking out for an AI engineer 🤖 1. Hands-On AI Engineering 🛠️ A collection of AI applications and agent systems with practical use cases of LLM. 👉 https://github.com/Sumanth077/Hands-On-AI-Engineering 2. Hands-On Large Language Models 📘 👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models 3. AI Agents for Beginners 🎓 👉 https://github.com/microsoft/ai-agents-for-beginners 4. GenAI Agents 🤖 👉 https://github.com/NirDiamant/GenAI_Agents 5. Made With ML 🚀 👉 https://github.com/GokuMohandas/Made-With-ML 6. Learn Harness Engineering ⚙️ 👉 https://github.com/walkinglabs/learn-harness-engineering 7. AutoResearch 🔬 👉 https://github.com/karpathy/autoresearch 8. Designing Machine Learning Systems 📚 👉 https://github.com/chiphuyen/dmls-book 9. Awesome LLM Inference ⚡ 👉 https://github.com/xlite-dev/Awesome-LLM-Inference 10. LLM Course 🗺️ 👉 https://github.com/mlabonne/llm-course	2 247
13	🔖 Comprehensive Practical Course on Reinforcement Learning We've found a repository that will help you learn Reinforcement Learning, from basic concepts to advanced algorithms. The author supports the theory with practical examples using TensorFlow, making the material ideal for self-study. ⛓️ Link to GitHub https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow @Machine_learn	1 922
14	+1 با عرض سلام مقاله زیر جهت واگذاری اسامی در نظر گرفته شده است Title: A Multi-Task Framework Unifying Classification and Regression forMicrogrid Power (kWh) Forecasting: Modified FEDformer Journal: IEEE transaction on soft computing Price: 2: 500$ 3: 350$ @Raminmousa1	387
15	Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models Read @Machine_learn	2 630
16	🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨ We found this interactive website that shows you visually how transformer models work. 🌐📊 Transformer Explainer: https://poloclub.github.io/transformer-explainer/ @Machine_learn	2 235
17	https://t.me/a_dust_seeking_the_sun	634
18	با عرض سلام اکانت @Raminmousa به دلیل جابجایی در زمان لاگین تلگرام تعلیق شده و در تلاش برای رفع این مشکلیم. دوستانی که باهام پروژه دارن لطفا با این ایدیم در ارتباط باشن @Raminmousa1	3 033
19	Game Theory: http://arxiv.org/abs/1512.06808 ————— #GameTheory #Gamification #Mathematics #Statistics #Probability @Machine_learn	3 490
20	🔥 MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management 💡 The paper introduces MemGUI-Agent, a mobile GUI agent designed to address the limitations of existing agents on long-horizon tasks. Current agents struggle with retaining intermediate facts across many steps and app transitions, leading to unreliable performance. This limitation is attributed to the ReAct-style prompting approach, which passively accumulates per-step records, causing prompt explosion and dilution of critical cross-app facts. To address this issue, the authors propose MemGUI-Agent, which uses proactive context management through Context-as-Action, or ConAct. ConAct casts context management as first-class actions emitted by the same policy that selects UI actions. This approach maintains three structured context fields: folded action history, folded UI state, and recent step record, preserving critical UI facts while keeping context compact. The authors also introduce MemGUI-3K, a dataset with 2,956 trajectories and full ConAct annotations for supervised training and offline analysis. Training an 8B model on MemGUI-3K results in MemGUI-8B-SFT, an 8B MemGUI-Agent that achieves the best open-data 8B performance on MemGUI-Bench and generalizes to the out-of-distribution MobileWorld benchmark. The contributions of the paper are threefold. Firstly, it identifies the limitations of existing mobile GUI agents on long-horizon tasks and attributes them to the ReAct-style prompting approach. Secondly, it proposes MemGUI-Agent with proactive context management through ConAct, which addresses the limitations of existing agents. Finally, it introduces MemGUI-3K, a dataset for supervised training and offline analysis, and demonstrates the effectiveness of MemGUI-8B-SFT, an 8B MemGUI-Agent trained on this dataset. The code, data, and trained models will be released to facilitate further research and development. 📅 Published on Jun 18 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2606.19926 • PDF: https://arxiv.org/pdf/2606.19926 • Project Page: https://memgui-agent.github.io/ 🤖 Models citing this paper: • https://huggingface.co/lgy0404/MemGUI-8B-SFT 📊 Datasets citing this paper: • https://huggingface.co/datasets/lgy0404/MemGUI-3K ━━━━━━━━━━━━━━━━━━━━━━━━ @Machine_learn	3 075

View all posts