Machine learning books and papers

📖 Penn State University's "Graph Theory" 📌 Lectures @Machine_learn

O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson? 🖥 Github: https://github.com/gair-nlp/o1-journey 📕 Paper: https://arxiv.org/abs/2411.16489v1 🌟 Dataset: https://paperswithcode.com/dataset/lima 💠@Machine_learn

Title: Transformer and XGBoost for time-series forecasting of Bitcoin prices using high-dimensional features ABSTRACT: Bitcoin price prediction based on price indicators has become a hot field of study. In this article, Bitcoin price prediction is discussed based on hash rate features. For this purpose, a series of price indices were used in the beginning and the selection of features was done among 20 features. On the other hand, the selection of features was also done on the raw data of eight rates. This research used forecasting for one, seven, thirty and ninety days. In the classification based on raw features, the highest accuracy is 81%, and for a 90-day interval, on the other hand, the lowest RMSE value is 1.85, which is for a one-day interval. In the classification based on the features extracted from the indicators, the highest accuracy is 73% for the 90-day interval and the lowest RMSE is 1.58 for the 1-day interval. نياز به co-author براي اين مقاله هستيم شرايط رو اگر كسي از دوستان داشت به بنده مراجعه كنن. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

👩‍💻 Julia Programming Language for Biologists 📎 Study the paper @Machine_learn

تيم دوم : fmri alzheimer's disease classification target journal:https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics نفر ٣ رو كم داريم. نيازمند كسي هستيم كه بتونه هزينه سرور رو پرداخت كنه و توي نگارش مقاله كمكمون كنه. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

📑 A review of transformers in drug discovery and beyond 📎 Study the paper 🔺@Machine_learn

https://github.com/andrewyng/aisuite #LLMs https://t.me/deep_learning_proj

📄Advancing biomolecular simulation through exascale HPC, AI and quantum computing 📎 Study the paper @Machine_learn

C O M P U T E R V I S I O N : F O U N D AT I O N S A N D A P P L I C AT I O N S 🖥 book @Machine_learn

Primers • Overview of Large Language Models 📖 Link @Machine_learn

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Pattern recognition and machine learning 📖 Link @Machine_learn

ليست پروژه هاي جديد كه دوستان مي تونن به تيم هاي ما اضافه بشن. تيم اول: Survey on whole slide image target journal: https://www.nature.com/srep/ نفرات ٤ و ٥ رو كم داريم تيم دوم : fmri alzheimer's disease classification target journal:https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics نفر ٣ رو كم داريم. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

The hitchhikers guide to python 📖 Book @Machine_learn

با عرض سلام مقالات اين ماه سابميت شده با كمك دوستان 1- Skin cancer detection Group 1: -Ramin M(Zanjan University), Saeed C(Tehran), Mohammad.M,*,+, Seyyed Mohammad.O(Sharif),Parsa.H(Sharif), and Soroush.S( Raderon AI Lab, BC, Canada) submit: https://www.nature.com/srep/ Group2: Ramin Mousa(Zanjan),Amir Ali. B(University of Tehran), Hakimeh. K( University of Zanjan) submit: https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics 2- Survey: Survey on evaluation of metrics for learning system Ramin Mousa, Masoud.p submit: https://www.sciencedirect.com/journal/knowledge-based-systems 3- NLP Group1: multi-domain SA BertCapsule: Mohammadali M, Soghra M, Amir.P, Mehrshad.E and Ramin Mousa submit: https://www.sciencedirect.com/journal/array به زودي ليستي از كارهاي جديد معرفي ميشه كه در صورت نياز دوستان مي تونن به گروه هامون اضافه بشن. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

An Introduction to Machine Learning 📖 book @Machine_learn

SLAck: Semantic, Location, and Appearance Aware Open-Vocabulary Tracking 📖 Arxiv @Machine_learn

Machine Learning for Hackers 📖 link @Machine_learn

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با عرض سلام جایگاه ۲ از مقاله زیر باقی مونده دوستانی که نیاز دارند به ایدی بنده پیام بدن. همچنین امکان ریکام دادن بعد چاپ امکان پذیر. title: UNet++ and LSTM combined approach for Breast Ultrasound Image Segmentation Abstract:  Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality rates. The utilization of ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has exhibited commendable segmentation outcomes through the application of UNet and UNet++ networks. Nevertheless, a notable drawback of these models resides in their inattention towards the temporal aspects embedded within the images. This research endeavors to enrich the UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit temporal characteristics for segmentation purposes. Furthermore, the incorporation of a Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++. Through the amalgamation of our proposed methodology with data augmentation on the BUSI with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%, sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These findings demonstrate competitiveness with cutting-edge techniques outlined in existing literature. Keywords: Attention mechanisms, BUSI dataset, Deep Learning, Feature Extraction, Multi-Scale features   🔹@Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0