gpt-engineer

gpt-engineer is a project in which you specify what you want in plain English and it iterates to produce a working codebase or scaffolded app. It’s an excellent playground for anyone exploring code-generation agents. And the repo contains clear install/usage instructions and a low-friction dev loop.

Creator:   AntonOsika
Stars ⭐️:  55,000
Forked by:  7,300

Github Repo:
https://github.com/AntonOsika/gpt-engineer

@Machine_learn

A combined UNet++ and LSTM approach for breast ultrasound image segmentation

Author: @Raminmousa
Doi:https://kwnsfk27.r.eu-west-1.awstrack.me/L0/https:%2F%2Fdoi.org%2F10.1016%2Fj.fraope.2025.100385/1/0102019a29ac6163-f3d94222-1d67-4a4b-8bb0-22ed875a5711-000000/VLVCKG-PEYhELOu00YPCeqOuaaA=449
Link:https://www.sciencedirect.com/science/article/pii/S2773186325001732?ref=pdf_download&fr=RR-8&rr=9958aaca19dd11fc

@Machine_learn

با عرض سلام در حال تنظیم مقاله ای تحت عنوان
Title: MediSeg: Medical Segmentation and classification Recommender system ....
Journal: IEEE Transactions on Medical Imaging
If: 9.8
این کار ۶ ماه طول خواهد کشید و به مسائل بهینه سازی انرژی، جلوگیری از اموزش مجدد شبکه ها، و مسائل تولید کربن در شبکه ها ی عصبی پرداخته خواهد شد.
هزینه مشارکت :

2: 600$
3:500 $
4: 400$
5:300$
6: 200$
7:200$

@Raminmousa
@Machine_learn
@Paper4money

5-phase path every ML systems engineer follows but almost no one talks about.

📚 Read


@Machine_learn

The Smol Training Playbook:
The Secrets to Building World-Class LLMs

📚 Read

@Machine_learn

با عرض سلام دوستاني كه مايل به اين پروژه هستن مي تونن بهمون ملحق بشن
@Raminmousa

State of AI-assisted Software Development

📕 Report

@Machine_learn

با عرض سلام در حال تنظیم مقاله ای تحت عنوان
Title: MediSeg: Medical Segmentation and classification Recommender system ....
Journal: IEEE Transactions on Medical Imaging
If: 9.8
این کار ۶ ماه طول خواهد کشید و به مسائل بهینه سازی انرژی، جلوگیری از اموزش مجدد شبکه ها، و مسائل تولید کربن در شبکه ها ی عصبی پرداخته خواهد شد.
هزینه مشارکت :

2: 600$
3:500 $
4: 400$
5:300$
6: 200$
7:200$

@Raminmousa
@Machine_learn
@Paper4money

نفر ۷ از این مقاله پر شد...!

🔹 Title: OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19209
• PDF: https://arxiv.org/pdf/2508.19209
• Project Page: https://omnihuman-lab.github.io/v1_5/

@Machine_learn

🔹 Title: UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18756
• PDF: https://arxiv.org/pdf/2508.18756
• Github: https://github.com/ZihaoHuang-notabot/Ultra-Sparse-Memory-Network

@Machine_learn

📹 AI in Bioinformatics Overcoming Pitfalls in Statistical, ML and Generative AI Approaches

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@Machine_learn

با عرض سلام برای مقاله ی زیر نفرات ۲ تا ۴ قابل اضافه شدن می باشد.
Title: Independently Recurrent Neural Network XGBoost (IXGBOOST) proposed method for Short term load forecasting

Abstract: Short-term load forecasting (STLF) is one of the most important and critical issue for power system operators. Therefore, it plays a fundamental role in improving the reliability of the power system, facilitating the integration of renewable energy sources and making demand response processes more efficient. Today, electricity forecasting based on sensor data with the increasing popularity of smart meter applications. On the other hand, STLF is one of the most critical inputs for the power plant planning undertaking. STLF reduces the overall scheduling uncertainty added by the intermittent generation of renewable resources. Therefore, it helps to minimize the cost of hydrothermal power generation in a power grid. Machine learning (ML) models have obtained acceptable results in this field. These approaches require manual feature extraction, which is challenging. Because of feature selection, deep learning approaches have automatically achieved results in prediction problems. This research proposes a network approach based on IndRNN+XGBoost to forecast electricity consumption in three modes: hourly, daily and weekly. ....

Journal: Optik

2: 20 milion
3:15 milion
4:10 milion
@Raminmousa
@Machine_learn
@paper4money

برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی, پایتون و یادگیری ماشین و علم  داده

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