Machine learning books and papers

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

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📈 Telegram kanali Machine learning books and papers analitikasi

Machine learning books and papers (@machine_learn) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 502 obunachidan iborat bo'lib, Taʼlim toifasida 8 053-o'rinni va Eron mintaqasida 13 774-o'rinni egallagan.

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невідомо sanasidan buyon loyiha tez o‘sib, 24 502 obunachiga ega bo‘ldi.

30 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -131 ga, so‘nggi 24 soatda esa -4 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

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Jalb etish (ER): Auditoriya o‘rtacha 7.24% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.98% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 1 773 marta ko‘riladi; birinchi sutkada odatda 484 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 1 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent disorder, psy, مقاله, framework, graph kabi asosiy mavzularga jamlangan.

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

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24 502

Obunachilar

-424 soatlar

-187 kunlar

-13130 kunlar

1 773

Post ko'rishlar

~ 48424 soatlar

~ 59648 soatlar

7.24%

Muloqot nisbati

Ma'lumot yo'q

Kuniga postlar

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Postlar arxiv

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اخرين تخفيف تا فردا شب #٥٠٪؜

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Programming for Problem Solving.pdf1.50 MB

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🪄 Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark. Github: https://github.com/naver/kapture-localization Paper: https://arxiv.org/abs/2205.15761v1 Data: https://paperswithcode.com/dataset/inloc @Machine_learn

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🦠 MaSIF- Molecular Surface Interaction Fingerprints: Geometric deep learning to decipher patterns in protein molecular surfaces. MaSIF is a proof-of-concept method to decipher patterns in protein surfaces important for specific biomolecular interactions. Github: https://github.com/LPDI-EPFL/masif Paper: https://www.nature.com/articles/s41592-019-0666-6 Data: https://github.com/LPDI-EPFL/masif#MaSIF-data-preparation @Machine_learn

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با عرض سلام هر دو پكيج يادگيري ماشين و يادگيري عميق تا اخر هفته تخفيف ٥٠٪؜ براي دوستان گذاشتم جهت تهيه مي تونين به ايدي بنده پيام بدين @Raminmousa

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B978-0-12-810408-8.00023-7.pdf1.71 MB

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B978-0-12-810408-8.00022-5.pdf1.46 MB

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B978-0-12-810408-8.00020-1.pdf7.55 KB

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📍 Perturbation Augmentation for Fairer NLP Responsible NLP projects from Meta AI. Github: https://github.com/facebookresearch/responsiblenlp Paper: https://arxiv.org/abs/2205.12586v1 Dataset: https://paperswithcode.com/dataset/glue @Machine_learn

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[CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (Oral) https://github.com/Garfield-kh/PoseTriplet @Machine_lean

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🔸لیستی از برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی, پایتون و یادگیری ماشین ‏❯ هوش مصنوعی: 1️⃣ @Ai_Tv 2️⃣ @AI_PYTHON 3️⃣ @HomeAI 4️⃣ @eventai ‏❯ یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn 2️⃣ @Programming4all_0to100 ‏❯ آموزش پایتون: ‏ 1⃣ @pythonchallenge 2⃣ @raspberry_python

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📝 Automated Crossword Solving Pretrained models, precomputed FAISS embeddings, and a crossword clue-answer dataset. Github: https://github.com/albertkx/berkeley-crossword-solver Paper: https://arxiv.org/abs/2205.09665v1 Dataset: https://www.xwordinfo.com/JSON/ @Machine_learn

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🔸لیستی از برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی, پایتون و یادگیری ماشین ‏❯ هوش مصنوعی: 1️⃣ @Ai_Tv 2️⃣ @ai_python 3️⃣ @HomeAI 4️⃣ @eventai ‏❯ یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn 2️⃣ @Programming4all_0to100 ‏❯ آموزش پایتون: ‏ 1⃣ @pythonchallenge

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جهت درخواست اين پكيج مي توانين با ايدي بنده در ارتباط باشين

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Weighted Deep Neural Network Ensemble Approach for Multi-Domain Sentiment Analysis Author: @Raminmousa Doi:https://dx.doi.org/10.22105/jarie.2021.288364.1332 cite: Mousa, Ramin, et al. "Weighted Deep Neural Network Ensemble Approach for Multi-Domain Sentiment Analysis." Journal of Applied Research on Industrial Engineering (2021).