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 24 518 subscribers, ranking 8 056 in the Education category and 13 757 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 518 subscribers.

According to the latest data from 24 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -165 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 6.78%. Within the first 24 hours after publication, content typically collects 1.90% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 663 views. Within the first day, a publication typically gains 465 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 25 June, 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.

24 518

Subscribers

-324 hours

-477 days

-16530 days

1 663

Post views

~ 46524 hours

~ 57948 hours

6.78%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 518

دوستان خروجي اين كار ٣ تا مقاله خواهد بود...!

24 518

Repost from Papers

با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم روي حوزه ي LLM مدل ها كار كنيم. این کار تحت نظر استاد Rex (Zhitao) Ying انجام میشه. link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en ۲نفر براي همکاری نياز داريم. BioPars: a pre-trained biomedical large language model for persian biomedical text mining. ١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...) ٢- پيش پردازش متن ها و تميز كردن متن ها ٣- اموزش ترنسفورمرها ي مورد نظر ٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...) دوستاني كه مايل به مشاركت هستن مي تونين بهم اطلاع بدن. هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. هزينه به ترتيب براي نفرات علاوه بر انجام تسك ها به صورت زير مي باشد. 🔹نفر سوم 500 دلار 🔺نفر چهارم 400 دلار @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

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Repost from Github LLMs

🌟 LLaMA-Mesh: 🟡Arxiv 🖥GitHub https://t.me/deep_learning_proj

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🌟 AlphaFold 3 🟡Paper 🟡Demo 🖥GitHub @Machine_learn

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Building Blocks for Theoretical Computer Science 🎓 Link @Machine_learn

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📑 Application of graph theory in liver research: A review 📎 Study paper @Machine_learn

24 518

Repost from Papers

با عرض سلام مقاله زیر در مرحله major revision می‌باشد. نفر ۴ ام از این مقاله قابل اضافه کردن است. Abstract Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality rates. The utilization of ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has exhibited commendable segmentation outcomes through the application of UNet and UNet++ networks. Nevertheless, a notable drawback of these models resides in their inattention towards the temporal aspects embedded within the images. This research endeavors to enrich the UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit temporal characteristics for segmentation purposes. Furthermore, the incorporation of a Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++. Through the amalgamation of our proposed methodology with data augmentation on the BUSI with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%, sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These findings demonstrate competitiveness with cutting-edge techniques outlined in existing literature. Keywords: Attention mechanisms, BUSI dataset, Deep Learning, Feature Extraction, Multi-Scale features دوستانی که نیاز دارن به ایدی بنده پیام بدن. #Unet++ #Segmentation @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

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Probability, Random Processes, and Statistical Analysis Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance 📕 Book @Machine_learn

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Book: The Art of Data Science Authors: Roger D. Peng & Elizabeth Matsui @Machine_learn

24 518

01. Time Series Visualization from Raw Data to Insights.pdf11.68 MB

24 518

New o3 OpenAI model is changing the game! For a long time, ARC was seen as proof that AI models “can’t think.” The argument went: if they truly could, why do they perform so poorly on this benchmark? Well, those days are over. The o3 model demonstrates not only the ability to think but also the capability to tackle tasks once considered out of reach. 👀 Check out the full breakdown of this breakthrough: https://arcprize.org/blog/oai-o3-pub-breakthrough It might be time to rethink what AI can achieve. Looking forward to the release! @Machine_learn

24 518

با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم از ١ ام دي ماه روي حوزه ي LLM مدل ها كار كنيم. این کار تحت نظر استاد Rex (Zhitao) Ying انجام میشه. link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en 1نفر براي همکاری نياز داريم. BioPars: a pre-trained biomedical large language model for persian biomedical text mining. ١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...) ٢- پيش پردازش متن ها و تميز كردن متن ها ٣- اموزش ترنسفورمرها ي مورد نظر ٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...) دوستاني كه مايل به مشاركت هستن مي تونين تا ١ دي بهم اطلاع بدن. هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. هزينه به ترتيب براي نفرات علاوه بر انجام تسك ها به صورت زير مي باشد. 🔹نفر چهارم 500 دلار @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0 🔹🔹شروع کار از امشب🔹🔹

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Gemini API Cookbook 📚 Github @Machine_learn

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مرکز تخصصی خریدوفروش مقاله +کتاب +ثبت اختراع و سایتیشن مقالات https://t.me/online24_24 https://t.me/online24_24

24 518

Perfect Roadmap To Learn Data Science In 2024 📖 Book @Machine_learn

24 518

دو‌ روز تا سابمیت نهایی این مقاله...!

24 518

Repost from Papers

با عرض سلام نفر سوم براي مقاله زير رو خالي داريم. Title: Alzheimer’s disease (AD) classification using swin transformer wavelet and Improved Gray Wolf Optimization (IGWO) Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification. 💠journal: https://www.sciencedirect.com/journal/expert-systems-with-applications 🔺if:7.5 هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد. امکان co-author نیز برای این کار هستش. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

24 518

🌟 SmolLM2 ⏩SmolLM2-1.7B🟢SmolLM2-1.7B-Instruct🟢Instruct GGUF ⏩SmolLM2-360M🟠SmolLM2-360M-Instruct 🟠Instruct GGUF ⏩SmolLM2-135M 🟠SmolLM2-135M-Instruct 🟠Instruct GGUF от комьюнити ▶️SmolLM2-1.7B :

from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-1.7B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

📌Apache 2.0 License. 🟡Demo SmolLM2 1.7B @Machine_learn

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Practitioner Guide for Creating Effective Prompts in Large Language Models 🔗 Paper @Machine_learn

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تنها نفر ۴ ام از این کار مشترک باقی مونده شروع کار ۱ دی ماه هستش. جهت همکاری به ایدی بنده پیام بدین. @Raminmousa