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Machine learning books and papers
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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Show more📈 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 24 522 subscribers, ranking 8 070 in the Education category and 13 771 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 522 subscribers.
According to the latest data from 22 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -150 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 7.45%. 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 1 829 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 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: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Thanks to the high frequency of updates (latest data received on 23 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.
24 522
Subscribers
-524 hours
-417 days
-15030 days
Posts Archive
با عرض سلام
در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفر۲ این موضوع رو می تونن شرکت کنن.
✅زمان شروع ۲۰ فروردین.
Journal: scientific reports https://www.nature.com/srep/
🔥🔥🔥🔥
Price:
2: ٢٥ میلیون
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
Carnegie Mellon University's "Advanced Algorithms" course notes
📄 Book
@Machine_learn
Llama 3.2 From Scratch
This repository contains a from-scratch, educational PyTorch implementation of Llama 3.2 text models with minimal code dependencies. The implementation is optimized for readability and intended for learning and research purposes.
📌 Guide
@Machine_learn
با عرض سلام فقط نفر ۲ از این پروژه باقی مانده است....!
@Raminmousa
با عرض سلام
در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفرات ۲ و ٣ این موضوع رو می تونن شرکت کنن.
✅زمان شروع ۲۰ فروردین.
Journal: scientific reports https://www.nature.com/srep/
🔥🔥🔥🔥
Price:
2: ٢٥ میلیون
3: ٢٠ ميليون
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
📄Multimodal deep learning approaches for precision oncology: a comprehensive review
📎 Study the paper
@Machine_learn
تنها ٤ روز براي شروع اين مقاله باقي مونده. دوستاني كه مي خوان مشاركت كنن لطفا به من اطلاع بدن
@Raminmousa
Repost from Github LLMs
Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models
🖥 Github: https://github.com/devoallen/awesome-reasoning-economy-papers
📕 Paper: https://arxiv.org/abs/2503.24377v1
@Machine_learn
📽 Current and Future Integrations of Genomics and AI
🎞 Watch
@Raminmousa
Repost from Papers
با عرض سلام
در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفرات ۲ و ٣ این موضوع رو می تونن شرکت کنن.
✅زمان شروع ۲۰ فروردین.
Journal: scientific reports https://www.nature.com/srep/
🔥🔥🔥🔥
Price:
2: ٢٥ میلیون
3: ٢٠ ميليون
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds
Animatable 3D human reconstruction from a single image is a challenging problem due to the ambiguity in decoupling geometry, appearance, and deformation. Recent advances in 3D human reconstruction mainly focus on static human modeling, and the reliance of using synthetic 3D scans for training limits their generalization ability. Conversely, optimization-based video methods achieve higher fidelity but demand controlled capture conditions and computationally intensive refinement processes. Motivated by the emergence of large reconstruction models for efficient static reconstruction, we propose LHM (Large Animatable Human Reconstruction Model) to infer high-fidelity avatars represented as 3D Gaussian splatting in a feed-forward pass. Our model leverages a multimodal transformer architecture to effectively encode the human body positional features and image features with attention mechanism, enabling detailed preservation of clothing geometry and texture. To further boost the face identity preservation and fine detail recovery, we propose a head feature pyramid encoding scheme to aggregate multi-scale features of the head regions. Extensive experiments demonstrate that our LHM generates plausible animatable human in seconds without post-processing for face and hands, outperforming existing methods in both reconstruction accuracy and generalization ability.
Paper: https://arxiv.org/pdf/2503.10625v1.pdf
Code: https://github.com/aigc3d/LHM
@Machine_learn
📃 A Comprehensive Guide to Validating Bioinformatics Findings: From In Silico to In Vitro
📎 Study the paper
@Machine_learn
InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
20 Mar 2025 · Liming Jiang, Qing Yan, Yumin Jia, Zichuan Liu, Hao Kang, Xin Lu ·
Achieving flexible and high-fidelity identity-preserved image generation remains formidable, particularly with advanced Diffusion Transformers (DiTs) like FLUX. We introduce InfiniteYou (InfU), one of the earliest robust frameworks leveraging DiTs for this task. InfU addresses significant issues of existing methods, such as insufficient identity similarity, poor text-image alignment, and low generation quality and aesthetics. Central to InfU is InfuseNet, a component that injects identity features into the DiT base model via residual connections, enhancing identity similarity while maintaining generation capabilities. A multi-stage training strategy, including pretraining and supervised fine-tuning (SFT) with synthetic single-person-multiple-sample (SPMS) data, further improves text-image alignment, ameliorates image quality, and alleviates face copy-pasting. Extensive experiments demonstrate that InfU achieves state-of-the-art performance, surpassing existing baselines. In addition, the plug-and-play design of InfU ensures compatibility with various existing methods, offering a valuable contribution to the broader community.
Paper: https://arxiv.org/pdf/2503.16418v1.pdf
Code: https://github.com/bytedance/infiniteyou
Dataset: 10,000 People - Human Pose Recognition Data
@Machine_learn
با عرض سلام
در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفرات ۲ و ٣ این موضوع رو می تونن شرکت کنن.
✅زمان شروع ۲۰ فروردین.
Journal: scientific reports https://www.nature.com/srep/
🔥🔥🔥🔥
Price:
2: ٢٥ میلیون
3: ٢٠ ميليون
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
🖥 Github: https://github.com/yunncheng/MMRL
📕 Paper: https://arxiv.org/abs/2503.08497v1
🌟 Dataset: https://paperswithcode.com/dataset/imagenet-s
@Machine_learn
