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 24 504 subscribers, ranking 8 031 in the Education category and 13 740 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 504 subscribers.

According to the latest data from 29 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -131 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.01%. Within the first 24 hours after publication, content typically collects 1.97% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 718 views. Within the first day, a publication typically gains 484 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 30 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.

24 504

Subscribers

-124 hours

-277 days

-13130 days

1 718

Post views

~ 48424 hours

~ 59648 hours

7.01%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

24 504

نفر اول رزور شد...!

24 504

Repost from Papers

با عرض سلام مقاله زیر در مرحله ی اولیه ارسال می باشد. نفرات ۱ تا ۳ جایگاه ها خالی می باشد. دوستانی که نیاز دارند می تونن به ایدی بنده پیام بدن. 💠💠 Title: Automated Concrete Crack Detection and Geometry Measurement Using YOLOv8 Description: This paper presents a comprehensive approach for automatic detection and quantification of concrete cracks using the YOLOv8 deep learning model. By leveraging advanced object detection capabilities, our system identifies concrete cracks in real-time with high accuracy, addressing challenges of complex backgrounds and varying crack patterns. Following crack detection, we employ image processing techniques to measure key geometric parameters such as width, length, and area. This integrated system enables rapid, precise analysis of structural integrity, offering a scalable solution for infrastructure monitoring and maintenance. Target Journal: Nature, Scientific Reports @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

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This repository contains a collection of resources in the form of eBooks related to Data Science, Machine Learning, and similar topics. 📖 book 💠@Machine_learn

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🔸برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی, پایتون و یادگیری ماشین ‏❯ هوش مصنوعی: 1️⃣ @Ai_Tv 2⃣ @HomeAI 3⃣ @ai_in_research 4⃣ @eventai 5⃣ @Ai_NewsTv ‏❯ علم داده : 1️⃣ @DataPlusScience ‏❯ یادگیری ماشین : 1️⃣ @Machine_learn ‏❯ آموزش پایتون و یادگیری ماشین: 1⃣ @Python4all_pro ‏❯ منابع و کتابهای پایتون ، علم داده و یادگیری ماشین : 1⃣ @programmers_street

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📃 Plant-based anti-cancer drug discovery using computational approaches 📎 Study the paper @Machine_learn

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Constrained Diffusion Implicit Models! We use diffusion models to solve noisy inverse problems like inpainting, sparse-recovery, and colorization. 10-50x faster than previous methods! Paper: arxiv.org/pdf/2411.00359 Demo: https://t.co/m6o9GLnnZF @Machine_learn

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Smol TTS models are here! OuteTTS-0.1-350M - Zero shot voice cloning, built on LLaMa architecture, CC-BY license! 🔥 > Pure language modeling approach to TTS > Zero-shot voice cloning > LLaMa architecture w/ Audio tokens (WavTokenizer) > BONUS: Works on-device w/ llama.cpp ⚡ Three-step approach to TTS: > Audio tokenization using WavTokenizer (75 tok per second). > CTC forced alignment for word-to-audio token mapping. > Structured prompt creation w/ transcription, duration, audio tokens. https://huggingface.co/OuteAI/OuteTTS-0.1-350M @Machine_learn

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📕 Machine Learning for Absolute Beginners ▪️Link @Machine_learn

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Machine Learning with PyTorch and Scikit-Learn Book 📚 book @Machine_learn

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AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent 🖥 Github: https://github.com/thudm/autowebglm 📕 Paper: https://arxiv.org/abs/2404.03648v1 🔥Dataset: https://paperswithcode.com/dataset/mind2web @Machine_learn

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❤️ اکستنشن ChatGPT Search برای مرورگرهای کرومیوم منتشر شد از طریق این لینک میتونید این افزونه رو دانلود کنید @Machine_learn

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Conformal prediction under ambiguous ground truth Paper: https://arxiv.org/pdf/2307.09302v2.pdf Codes: https://github.com/google-deepmind/uncertain_ground_truth https://github.com/alaalab/webcp Dataset: Dermatology ddx dataset @Machine_learn

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فقط جایگاه دوم از این مقاله باقی مونده

24 504

Repost from Papers

الحمدالله تو اين بازه ٣ ماه تونستيم مقالات مشاركتي رو تحت وظايف زير انجام بديم: 🔹ثبت ٤ مقاله در حوزه Multi-modal wond classification 🔹ارائه ی دو مقاله در حوزه ی breast cancer segmentation 🔹 ارائه ی سه مقاله در حوزه ی cancer detection که ۸۰٪ مراحل این مقالات هم تموم شده. به زودی پس از اتمام این مقالات لیستی از مقالات مشارکتی رو خواهیم داشت . https://t.me/+SP9l58Ta_zZmYmY0

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👩‍💻 Python Notes for Professionals book 🔗 Book @Machine_learn

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Repost from Github LLMs

📖 LLM-Agent-Paper-List is a repository of papers on the topic of agents based on large language models (LLM)! The papers are divided into categories such as LLM agent architectures, autonomous LLM agents, reinforcement learning (RL), natural language processing methods, multimodal approaches and tools for developing LLM agents, and more. 🖥 Github https://t.me/deep_learning_proj

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💠Title:BERTCaps: BERT Capsule for persian Multi-domain Sentiment Analysis. 🔺Abstract: Sentiment classification is widely known as a domain-dependent problem. In order to learn an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are expensive and time-consuming to annotate. Multi-domain sentiment analysis based on multi-task learning can leverage labeled samples in each single domain, which can alleviate the need for large amount of labeled data in all domains. In this article, the purpose is BERTCaps to provide a multi-domain classifier. In this model, BERT was used for Instance Representation and Capsule was used for instance learning. In the evaluation dataset, the model was able to achieve an accuracy of 0.9712 in polarity classification and an accuracy of 0.8509 in domain classification. journal: https://www.sciencedirect.com/journal/array If:2.3 جايگاه ٢ و ٤ اين مقاله رو نياز داريم. دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن. @Raminmousa @Paper4money @Machine_learn

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Data Pipelines with Apache Airflow 📘 book @Machine_learn

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📑A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models 📎 Study the paper @Machine_learn

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Ms - SmolLM2 1.7B - beats Qwen 2.5 1.5B & Llama 3.21B, Apache 2.0 licensed, trained on 11 Trillion tokens 🔥 > 135M, 360M, 1.7B parameter model > Trained on FineWeb-Edu, DCLM, The Stack, along w/ new mathematics and coding datasets > Specialises in Text rewriting, Summarization & Function Calling > Integrated with transformers & model on the hub! You can run the 1.7B in less than 2GB VRAM on a Q4 👑 Fine-tune, run inference, test, train, repeat - intelligence is just 5 lines of code away! https://huggingface.co/collections/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9 @Machine_learn