Machine learning books and papers

Introduction to Data Science – Lecture Material 🔗 Github @Machine_learn

🀄 GuoFeng Webnovel: A Discourse-Level and Multilingual Corpus of Web Fiction 🖥 Github: https://github.com/longyuewangdcu/guofeng-webnovel 📕 Paper: https://arxiv.org/abs/2412.11732v1 🌟 Dataset: www2.statmt.org/wmt24/literary-trans @Machine_learn

در اين پروژه ابتدا BioparsData ارائه ميشود كه فرايند جمع اوري سنگيني خواهد داشت. پس از ان BioparsQ ارائه ميشود كه ١٠ هزار سوال بيولوژكي براي ارزيابي مدل ارائه خواهد شد. در انتها Biopars را ارائه خواهيم داد. تمامي اين فرايند پس از نهايي شدن در دسترس عموم قرار ميدهيم.

با عرض سلام نفر سوم براي مقاله زير رو خالي داريم. Title: Alzheimer’s disease (AD) classification using swin transformer wavelet and Improved Gray Wolf Optimization (IGWO) Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification. journal: https://www.sciencedirect.com/journal/expert-systems-with-applications if:7.5 هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

📃A Comprehensive Survey on Automatic Knowledge Graph Construction 📎 Study paper @Machine_learn

PDF Math Translate DF scientific paper translation with preserved formats Creator: Byaidu Stars ⭐️: 5.1k Forked By: 375 https://github.com/Byaidu/PDFMathTranslate @Machine_learn

امكان واگذاري co-author هم داره.

با عرض سلام نفر سوم براي مقاله زير رو خالي داريم. Title: Alzheimer’s disease (AD) classification using swin transformer wavelet and Improved Gray Wolf Optimization (IGWO) Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification. journal: https://www.sciencedirect.com/journal/expert-systems-with-applications if:7.5 هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

⚡️ Byte Latent Transformer: Patches Scale Better Than Tokens Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness. 🖥 Github: https://github.com/facebookresearch/blt 📕 Paper: https://arxiv.org/abs/2412.09871v1 🌟 Dataset: https://paperswithcode.com/dataset/mmlu @Machine_learn

⚡️ Byte Latent Transformer: Patches Scale Better Than Tokens Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness. 🖥 Github: https://github.com/facebookresearch/blt 📕 Paper: https://arxiv.org/abs/2412.09871v1 🌟 Dataset: https://paperswithcode.com/dataset/mmlu @Machine_learn

📃 Large language models and their applications in bioinformatics 📎 Study the paper @Machine_learn

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با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم از ١ ام دي ماه روي حوزه ي LLM مدل ها كار كنيم. این کار تحت نظر استاد Rex (Zhitao) Ying انجام میشه. link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en ۲نفر براي همکاری نياز داريم. BioPars: a pre-trained biomedical large language model for persian biomedical text mining. ١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...) ٢- پيش پردازش متن ها و تميز كردن متن ها ٣- اموزش ترنسفورمرها ي مورد نظر ٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...) دوستاني كه مايل به مشاركت هستن مي تونين تا ١ دي بهم اطلاع بدن. هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. هزينه به ترتيب براي نفرات علاوه بر انجام تسك ها به صورت زير مي باشد. 🔹نفر چهارم 500 دلار 🔺نفر پنجم 400 دلار @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

Large language models (LLMs): survey, technical frameworks, and future challenges https://link.springer.com/content/pdf/10.1007/s10462-024-10888-y.pdf @Machine_learn

Large Language Models: A Survey https://arxiv.org/pdf/2402.06196 @Machine_learn

WIS Python programming course started in 2024.04 📖 Github @Machine_learn

با عرض سلام نفر ۳ از مقاله زیر رو نیاز داریم. Title: hybrid deep learnings and machine learning frameworks for air quality prediction during the COVID‑19 pandemic journal: https://www.sciencedirect.com/journal/expert-systems-with-applications if:7.5 در این مقاله تاثیر ۲۶ مدل ansemble و ترکیبی رو برای پیش بینی کیفیت هوا در بازه ۱ روزه ۳ روزه و ۷ روزه بررسی کردیم. جهت شرکت در این مقاله به ایدی بنده پیام بدین. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

DATA SCIENCE ROADMAP 🔗 Github @Machine_learn

OASIS Alzheimer's Detection Large-scale brain MRI dataset for deep neural network analysis About Dataset The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease. To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts. For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis. Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression. During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB. @Machine_learn

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