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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
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
📃Understanding Graph Databases: A Comprehensive Tutorial and Survey
📎 Study paper
@Machine_learn
Nexusflow released Athene v2 72B - competetive with GPT4o & Llama 3.1 405B Chat, Code and Math 🔥
> Arena Hard: GPT4o (84.9) vs Athene v2 (77.9) vs L3.1 405B (69.3)
> Bigcode-Bench Hard: GPT4o (30.8) vs Athene v2 (31.4) vs L3.1 405B (26.4)
> MATH: GPT4o (76.6) vs Athene v2 (83) vs L3.1 405B (73.8)
> Models on the Hub along and work out of the box w/ Transformers 🤗
https://huggingface.co/Nexusflow/Athene-V2-Chat
They also release an Agent model: https://huggingface.co/Nexusflow/Athene-V2-Agent
@Machine_learn
Collection of resources in the form of eBooks related to Data Science, Machine Learning, and similar topics
📖 Github
@Machine_learn
با عرض سلام خيلي از دوستان در رابطه با طراحي صفر تا صد پروژه هاي ديپ از بنده سوال پرسيدن داخل پك زير ٣٦ پروژه رو با جزئيات شرح دادم:
1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
دوستاني كه نياز به اين پروژه ها دارن ميتونن با بنده در ارتباط باشن.
@Raminmousa
@Machine_learn
✅ کانال دانشکده مهندسی کامپیوتر دانشگاه صنعتی شریف 🔹⬇️⬇️⬇️⬇️
/channel/CEinUse
برای کنکور ارشد کمک نیاز به کمک داری ؟
نمیدونی برای دروس کنکور ارشدت کدوم استاد بهتره ؟
میخوای از تجربه دوستات و ترم بالاییا استفاده کنی؟
💯نمونه سوال و جزوه رو لازم داری ؟
/channel/CEinUse
همراه با فعالترین و پر عضوترین گروه دانشکده مهندسی کامپیوتر دانشگاه شریف
✌️ با حضور امیررضا آبانی رتبه ۹۰ کنکور ارشد مهندسی کامپیوتر
جوین شو که جزوه و کتاب نیازت میشه😁👇👇
/channel/CEinUse
/channel/CEinUse
/channel/CEinUse
📄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
سلام دوستان
ما دو‌نفر از دانشجوی دکترای یکی از دانشگاه های تاپ آمریکا هستم.
خوش حالیم که اعلام کنیم مقالات قبلی ما اکسپت شد.
در حال حاضر جایگاه‌های نفرات در دو مقاله در حوزه کامپیوتر ساینس با موضوعات کوانتیزیشن، تفسیرپذیری و یادگیری بدون نظارت در زمینه پزشکی آزاد است.
بعد از سابمیت مقاله، پیپر، متن و کدهای مربوطه در اختیار شما قرار میگیرد. با تضمین چاپ مقاله توسط ما، میتونید برای اپلای دانشگاه‌ها و گرین‌کارت اقدام بفرمایید.
همچنین با معرفی همکار یا نفرات قبلی، از تخفیف بهره‌مند شوید.
لطفا در صورت تمایل به آی دی ما پیام دهید.
( پاسخ با اکانت قبلی ما به دلیل رمزورود به تلگرام امکان پذیر نیست)
آی دی جدید ما:
@rezaa_alvandi
A Brief Introduction to Neural Networks
📕 Book
@Machine_learn
با عرض سلام
اگر از دوستان کسی توانایی گرفتن اکسپت برای مقاله زیر رو داره و توانایی پرداخت هزینه ی سرور رو داره به بنده پیام بده. اسم شخص به عنوان نفر ۴ در مقاله درج میشه.
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.
@Raminmousa
@Machine_learn
Deep Learning and Computational Physics - Lecture Notes, University of South California
📓 book
@Machine_learn
Competitive Programmer's Handbook
📚 Book
🔺@Machine_learn
💠Title:BERTCaps: BERT Capsule for persian Multi-domain Sentiment Analysis.
🔺Abstract:
Sentiment classification is widely known as a domain-dependent problem. In order to learn an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are expensive and time-consuming to annotate. Multi-domain sentiment analysis based on multi-task learning can leverage labeled samples in each single domain, which can alleviate the need for large amount of labeled data in all domains. In this article, the purpose is BERTCaps to provide a multi-domain classifier. In this model, BERT was used for Instance Representation and Capsule was used for instance learning. In the evaluation dataset, the model was able to achieve an accuracy of 0.9712 in polarity classification and an accuracy of 0.8509 in domain classification.
journal: https://www.sciencedirect.com/journal/array
If:2.3
جايگاه ٤ اين مقاله رو نياز داريم.
دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن.
@Raminmousa
@Paper4money
@Machine_learn