Dataset Name: FIFA23 OFFICIAL DATASET
Basic Description: From FIFA17 to FIFA23 statistics for each football player

📖 FULL DATASET DESCRIPTION:

The dataset contains +17k unique players and more than 60 columns, general information and all KPIs the famous videogame offers. As the esport scene keeps rising espacially on FIFA, I thought it can be useful for the community (kagglers and/or gamers)

📥 DATASET DOWNLOAD INFORMATION


🔴 Dataset Size: Download dataset as zip (14 MB)

🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/bryanb/fifa-player-stats-database

📊 Additional information:

File count not found
Views: 107,000
Downloads: 66,500

@Machine_learn

💻 ++101 Linux commands Open-source eBook

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@Machine_learn

Dataset Name: Real Life Violence Situations Dataset
Basic Description: 1000 videos containing real street fight and 1000 video from other classes



🔴 Dataset Size: Download dataset as zip (4 GB)

🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/mohamedmustafa/real-life-violence-situations-dataset


1. Real Time Violence Detection | MobileNet Bi-LSTM | Upvotes: 424
URL: https://www.kaggle.com/code/abduulrahmankhalid/real-time-violence-detection-mobilenet-bi-lstm

2. Real life violence detection using InceptionV3 | Upvotes: 395
URL: https://www.kaggle.com/code/nandinibagga/real-life-violence-detection-using-inceptionv3

3. Real Life Violence Detection / KERAS-TENSORFLOW | Upvotes: 115
URL: https://www.kaggle.com/code/brsdincer/real-life-violence-detection-keras-tensorflow

4. Video Fights Dataset | Upvotes: 24
URL: https://www.kaggle.com/datasets/shreyj1729/cctv-fights-dataset


@Machine_learn

🔹 Title: Forecasting Probability Distributions of Financial Returns with Deep Neural Networks

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18921
• PDF: https://arxiv.org/pdf/2508.18921
• Github: https://github.com/jmichankow/deep_learning_probability

@Machine_learn

Dataset Name: Linked In Job Postings (2023 - 2024)
Basic Description: LinkedIn Job Postings (2023 - 2024)

📖 FULL DATASET DESCRIPTION:

Scraper Code - https://github.com/ArshKA/LinkedIn-Job-Scraper
Every day, thousands of companies and individuals turn to LinkedIn in search of talent. This dataset contains a nearly comprehensive record of 124,000+ job postings listed in 2023 and 2024. .

🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/arshkon/linkedin-job-postings

📊 Additional information:

File count not found
Views: 126,000
Downloads: 53,100

📚 RELATED NOTEBOOKS:

1. "Decoding the Job Market: An In-depth Exploration | Upvotes: 84
URL: https://www.kaggle.com/code/pratul007/decoding-the-job-market-an-in-depth-exploration

2. LinkedIn Job Postings 2023 Data Analysis | Upvotes: 58
URL: https://www.kaggle.com/code/enricofindley/linkedin-job-postings-2023-data-analysis

@Machine_learn

Dataset Name: Online Payments Fraud Detection Dataset
Basic Description: Online payment fraud big dataset for testing and practice purpose

📖 FULL DATASET DESCRIPTION:

The below column reference:

📥 DATASET DOWNLOAD INFORMATION


🔴 Dataset Size: Download dataset as zip (186 MB)

🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/rupakroy/online-payments-fraud-detection-dataset

@Machine_learn

Sharing State Between Prompts and Programs

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@Machine_learn

🔹 Title: Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15213
• PDF: https://arxiv.org/pdf/2508.15213

@Machine_learn

🚀 AI Agents for Android Apps


📌 GitHub: https://github.com/actionstatelabs/android-action-kernel


@Machine_learn

🔹 Title: Self-Rewarding Vision-Language Model via Reasoning Decomposition

🔹 Publication Date: Published on Aug 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19652
• PDF: https://arxiv.org/pdf/2508.19652

@Machine_learn

🔹 Title: Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents

🔹 Publication Date: Published on Aug 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19493
• PDF: https://arxiv.org/pdf/2508.19493
• Project Page: https://zhixin-l.github.io/SAPA-Bench
• Github: https://github.com/Zhixin-L/SAPA-Bench

@Machine_learn

How we made Python's packaging library 3x faster

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@Machine_learn

Dataset Name: Gallstone Dataset (UCI)
Basic Description: Gallstone Dataset (UCI Machine Learning Repository)



📥 DATASET DOWNLOAD INFORMATION
==================================

🔴 Dataset Size: Download dataset as zip (81 kB)

🔰 Direct dataset download link:
URL not found

📊 Additional information:
==================================
File count not found
Views: 1,128
Downloads: 246

📚 RELATED NOTEBOOKS:
==================================
1. Heart Attack Risk Prediction Dataset | Upvotes: 274
URL: https://www.kaggle.com/datasets/iamsouravbanerjee/heart-attack-prediction-dataset

@Machine_learn

رمضان الکریم ❤️
@Machine_learn

🔹 Title: CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning

🔹 Publication Date: Published on Aug 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20096
• PDF: https://arxiv.org/pdf/2508.20096
• Project Page: https://github.com/OpenIXCLab/CODA
• Github: https://github.com/OpenIXCLab/CODA

@Machine_learn

🔹 Title: Predicting the Order of Upcoming Tokens Improves Language Modeling

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19228
• PDF: https://arxiv.org/pdf/2508.19228
• Github: https://github.com/zaydzuhri/token-order-prediction

@Machine_learn

هر ثانیه توقف، یعنی از دست رفتن زمان، هزینه و فرصت…
پایداری دیگر یک انتخاب نیست؛ یک ضرورت است.

🚀 ایران‌GPU؛جایی که پروژه‌ها متوقف نمی‌شوند.

🏛 تنها و اولین شرکت بورسی هوش مصنوعی ایران
🕒 بیش از ۵ سال سابقه فعالیت حرفه‌ای
🌐 شبکه‌ای از ۲۰+ دیتاسنتر غیرمتمرکز در سراسر کشور
🧠 مناسب تیم‌ها، پژوهشگران و سازمان‌های حرفه‌ای AI
🛟 پشتیبانی ۲۴ ساعته، ۷ روز هفته
📈 تضمین SLA با دسترس‌پذیری 99.9٪ و ارائه سرور داخل ایران


📩 ثبت درخواست مشاوره | شروع مسیر هوشمندانه
https://b2n.ir/qk8423

با عرض سلام مي خواهيم مقاله كنفرانسي با عنوان زير بنويسيم


Complex Sig: Complex deep model for signal classification

Abstract: The ability to classify signals is an important task that provides the opportunity for many different applications. In the early research for signal classification, they had to
decompose the signal using FT (Fourier transform), SIFT, MFCC or other manual methods using statistical modulation features, then classify these signals by a traditional machine learning approach. In the last few years, the process of learning deep models that lead to the automatic extraction of features has positively affected classification. Different deep-learning models with di erent depths have been proposed in the literature.
This article proposes different approaches to classify signals in different SNR conditions. ResNet-based approaches perform well for high SNRs but poorly when dealing with low SNRs. Therefore, TRansforme-based approaches were proposed for classification, reaching an average accuracy of 0.7056 in low SNR and an average of 0.9089 in high SNR.

نتايج اوليه خوب بوده و قابل مقايسه با ساير مقالات تو اين حوزه ميباشد. نياز به ٢ يا سه نفر داريم كه مشاركت كنند.
Price:
2: 300$
3:200$
4:100$

@Raminmousa
@paper4money
@Machine_learn