Machine learning books and papers

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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

📈 Análisis del canal de Telegram Machine learning books and papers

El canal Machine learning books and papers (@machine_learn) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 24 522 suscriptores, ocupando la posición 8 070 en la categoría Educación y el puesto 13 771 en la región Irán.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 24 522 suscriptores.

Según los últimos datos del 22 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -150, y en las últimas 24 horas de -5, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.45%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.90% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 829 visualizaciones. En el primer día suele acumular 465 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
Intereses temáticos: El contenido se centra en temas clave como disorder, psy, مقاله, framework, graph.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 23 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

24 522

Suscriptores

-524 horas

-417 días

-15030 días

1 829

Visitas de la publicación

~ 46524 horas

~ 57948 horas

7.45%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

24 521

✔️ "Speech and Language Processing": 🟡Link @Machine_learn

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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

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Carnegie Mellon University's "Advanced Algorithms" course notes 📄 Book @Machine_learn

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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

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با عرض سلام فقط نفر ۲ از این پروژه باقی مانده است....! @Raminmousa

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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

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📄Multimodal deep learning approaches for precision oncology: a comprehensive review 📎 Study the paper @Machine_learn

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📖 Applied Bioinformatics 💥Free Online Book from Oregon State 🌐 Study @Machine_learn

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تنها نفر ۳ از این پروژه باقی مانده...! @Raminmousa

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تنها ٤ روز براي شروع اين مقاله باقي مونده. دوستاني كه مي خوان مشاركت كنن لطفا به من اطلاع بدن @Raminmousa

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Repost from Github LLMs

Large Language Model Course 😮 📌 Github @LLM_learning

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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

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📽 Current and Future Integrations of Genomics and AI 🎞 Watch @Raminmousa

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Repost from Papers

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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

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📃 A Comprehensive Guide to Validating Bioinformatics Findings: From In Silico to In Vitro 📎 Study the paper @Machine_learn

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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

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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

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https://assets-eu.researchsquare.com/files/rs-6324973/v1_covered_06e9d819-8b0e-45e2-9eb6-49dacf0e7123.pdf?c=1743391966