23171
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Gaussian Processes for Machine Learning
📚 link
@Machine_learn
⚡️ Biggest open text dataset release of the year: SmolTalk is a 1M sample big synthetic dataset that was used to train SmolLM v2.
TL;DR;
🧩 New datasets: Smol-Magpie-Ultra (400K) for instruction tuning; Smol-contraints (36K) for precise output; Smol-rewrite (50K) & Smol-summarize (100K) for rewriting and summarization.
🤝 Public Dataset Integrations: OpenHermes2.5 (100K), MetaMathQA & NuminaMath-CoT, Self-Oss-Starcoder2-Instruct, LongAlign & SystemChats2.0
🥇 Outperforms the new Orca-AgenInstruct 1M when trained with 1.7B and 7B models
🏆 Outperform models trained on OpenHermes and Magpie Pro on IFEval and MT-Bench
distilabel to generate all new synthetic datasets
🤗 Released under Apache 2.0 on huggingface
Apache 2.0
Synthetic generation pipelines and training code released.
Dataset: https://huggingface.co/datasets/HuggingFaceTB/smoltalk
Generation Code: https://github.com/huggingface/smollm
Training Code: https://github.com/huggingface/alignment-handbook/tree/main/recipes/smollm2
@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
/channel/+SP9l58Ta_zZmYmY0
📖 Penn State University's "Graph Theory"
📌 Lectures
@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
/channel/+SP9l58Ta_zZmYmY0
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
/channel/+SP9l58Ta_zZmYmY0
https://github.com/andrewyng/aisuite
#LLMs
/channel/deep_learning_proj
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
فرصت محدود برای این پروژه ها ...!
Читать полностью…
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
/channel/+SP9l58Ta_zZmYmY0
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
/channel/+SP9l58Ta_zZmYmY0
SLAck: Semantic, Location, and Appearance
Aware Open-Vocabulary Tracking
📖 Arxiv
@Machine_learn
امشب اخرین زمان برای سابمیت این مقاله هستش...!
Читать полностью…
Compare NLP Transformer-based Models used for Sentiment Analysis code
🔺@Machine_learn
BashBook
📚 Book
@Machine_learn
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 .
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
📖 General Relativity
📌 Book
@Machine_learn
ShowUI is a lightweight vision-language-action model for GUI agents.
🖥 Github: https://github.com/showlab/showui
📕 Paper: https://arxiv.org/abs/2411.17465v1
🌟 Dataset: https://huggingface.co/datasets/showlab/ShowUI-desktop-8K
@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
👩💻 Julia Programming Language for Biologists
📎 Study the paper
@Machine_learn
📑 A review of transformers in drug discovery and beyond
📎 Study the paper
🔺@Machine_learn
📄Advancing biomolecular simulation through exascale HPC, AI and quantum computing
📎 Study the paper
@Machine_learn
Primers • Overview of Large Language Models
📖 Link
@Machine_learn
Pattern recognition and machine learning
📖 Link
@Machine_learn
The hitchhikers guide to python
📖 Book
@Machine_learn
An Introduction to Machine Learning
📖 book
@Machine_learn
Machine Learning for Hackers
📖 link
@Machine_learn
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
/channel/+SP9l58Ta_zZmYmY0
Decision Trees: A Comprehensive Guide
with Handwritten Notes, Explanations,
and Code
#DT
🔸@Machine_learn