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Machine learning books and papers
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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Ko'proq ko'rsatish📈 Telegram kanali Machine learning books and papers analitikasi
Machine learning books and papers (@machine_learn) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 522 obunachidan iborat bo'lib, Taʼlim toifasida 8 070-o'rinni va Eron mintaqasida 13 771-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 24 522 obunachiga ega bo‘ldi.
22 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -150 ga, so‘nggi 24 soatda esa -5 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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- Post qamrovi: Har bir post o‘rtacha 1 829 marta ko‘riladi; birinchi sutkada odatda 465 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
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“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
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24 522
Obunachilar
-524 soatlar
-417 kunlar
-15030 kunlar
Postlar arxiv
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
