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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/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
FRONTIERMATH: A BENCHMARK FOR EVALUATING ADVANCED
MATHEMATICAL REASONING IN AI
📚 Read
💠@Machine_learn
دوستانی که نیاز به این مقاله دارند تا امشب وقت باقی مانده است.
@Raminmousa
The Arcade Learning Environment (ALE) is a simple framework that allows researchers and hobbyists to develop AI agents for Atari 2600 game
🖥 Github: https://github.com/farama-foundation/arcade-learning-environment
📕 Paper: https://arxiv.org/abs/2410.23810v1
⚡️ Dataset: https://paperswithcode.com/dataset/mujoco
@Machine_learn
📃A Comprehensive Survey on Automatic Knowledge Graph Construction
📎 Study paper
🔺@Machine_learn
🔸برترین کانالهای آموزشی در زمینه های هوشمصنوعی, پایتون و یادگیری ماشین
☑️ هوش مصنوعی :
1️⃣ @Ai_Tv
2⃣ @ai_in_research
3⃣ @eventai
☑️معرفی و آموزش کار با هوشمصنوعی های مولد و پرامپت نویسی
1⃣ @Ai_NewsTv
☑️ علم داده :
1️⃣ @DataPlusScience
☑️ یادگیری ماشین :
1️⃣ @Machine_learn
☑️ آموزش پایتون و یادگیری ماشین:
1⃣ @Python4all_pro
2⃣ @raspberry_python
3⃣ @pythony
☑️ دوره های رایگان و منابع آموزشی پایتون ، علم داده و یادگیری ماشین :
1⃣ @programmers_street
🖥 Awesome LLM Strawberry (OpenAI o1)
▪ Github
✅/channel/deep_learning_proj
📖 A Data-Centric Introduction to Computing
link
@Machine_learn
Foundations Of The Theory Of Probability by
Andrey Nikolaevich Kolmogorov
🔥🔥🔥
Read the book
@Machine_learn
How to Build Your Career in AI
📚 Book
@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
How to Build Your Career in AI
📚 Book
@Machine_learn
DeepArUco++: improved detection of square fiducial markers in challenging lighting conditions
🖥 Github: https://github.com/avauco/deeparuco
📕 Paper: https://arxiv.org/pdf/2411.05552v1.pdf
⚡️ Dataset: https://paperswithcode.com/dataset/coco
@Machine_learn
📃A Comprehensive Review of Propagation Models in Complex Networks: From Deterministic to Deep Learning Approaches
📎 Study paper
🔺@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. فردا زمان سابميت هستش.
دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن.
@Raminmousa
@Paper4money
@Machine_learn
20 Python Libraries You Aren't Using But Should
📕 Book
@Machine_learn
با عرض سلام نفرات ٢ و ٣ اين مقاله باقي موندن
Читать полностью…
Financial Statement Analysis with Large Language Models (LLMs)
📕 Book
@Machine_learn
با عرض سلام مقاله زیر در مرحله ی اولیه ارسال می باشد. نفرات 2و ۳ خالی می باشد. دوستانی که نیاز دارند می تونن به ایدی بنده پیام بدن. همچنین امکان ریکامدادن بعد اتمام کار وجود داره.
💠💠
Title:
Automated Concrete Crack Detection and Geometry Measurement Using YOLOv8
Description:
This paper presents a comprehensive approach for automatic detection and quantification of concrete cracks using the YOLOv8 deep learning model. By leveraging advanced object detection capabilities, our system identifies concrete cracks in real-time with high accuracy, addressing challenges of complex backgrounds and varying crack patterns. Following crack detection, we employ image processing techniques to measure key geometric parameters such as width, length, and area. This integrated system enables rapid, precise analysis of structural integrity, offering a scalable solution for infrastructure monitoring and maintenance.
🔸Target Journal:
Nature, Scientific Reports
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
understanding deep learning
📚 Book
@Machine_learn