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📃A key review on graph data science: The power of graphs in scientific studies



📎 Study paper

@Machine_learn
Paper: Scalable Autoregressive Image Generation with Mamba

Paper: https://arxiv.org/pdf/2408.12245v1.pdf

Code: https://github.com/hp-l33/aim

Dataset: ImageNet

@Machine_learn
🎓 Graph Neural Networks in Intrusion Detection

📘A thesis submitted in fulfilment of the requirements for the degree of MSc. Computer Science
🗓Publish year: 2024



📎Study Thesis


@Machine_learn
DocsGPT

DocsGPT is a cutting-edge open-source solution that streamlines the process of finding information in project documentation. With its integration of the powerful GPT models, developers can easily ask questions about a project and receive accurate answers.

Say goodbye to time-consuming manual searches, and let DocsGPT help you quickly find the information you need. Try it out and see how it revolutionizes your project documentation experience. Contribute to its development and be a part of the future of AI-powered assistance.

Creator: Arc53
Stars ⭐️: 7.4k
Forked By: 769
https://github.com/arc53/DocsGPT

#DocsGPT #GPT

@Machine_learn
An open source UI to train your own Flux LoRA just landed on Hugging Face 🚀 Also, probably the easiest and cheapest (local training also supported).

https://huggingface.co/spaces/autotrain-projects/train-flux-lora-ease


@Machine_learn
Forwarded from Papers
با عرض سلام مقاله اي تحت ريوايزد داريم که در حوزه Ultrasound Image Segmentation هستش. دوستانی که نیاز دارن نفر سومش رو می تونیم اختصاص بدیم.

@Raminmousa
@Paper4money
@Machine_learn
Reinforcement Learning_ An Introduction, 2nd Edition

Book

@Machine_learn
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Artificial Intelligence and Games
(2nd Edition)


📚 Book

@Machine_learn
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Data-Intensive Text Processing with MapReduce

📚 Book

@Machine_learn
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⚡️ Yi-Coder

🟢Yi-Coder-9B;
🟢Yi-Coder-9B-Chat;
🟠Yi-Coder-1.5B;
🟠Yi-Coder-1.5B-Chat.


# Clone repository
git clone https://github.com/01-ai/Yi-Coder.git
cd Yi-Coder

# Install requirements
pip install -r requirements.txt




🟡Arxiv
🖥Github


@Machine_learn
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Book of machine learning algorithms & concepts explained to simply, even a human can understand.

📓 Book

@Machine_learn
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Forwarded from Papers
با عرض سلام
در ادامه فرایند نگارش مقالات سعی داریم چند گروه ۴ نفره برای مقالات با موضوعات مختلف ایجاد کنیم. چهار موضوع که می خواهیم در ان ها کار کنیم از قبیل زیر می باشند:
۱ - طبقه بندی تصاویر پزشکی
۲- پیش بینی ترافیک شبکه
۳- حل مشکلات شبکه های RNN در مساله سری زمانی
۴-پیش بینی بار مصرفی در شبکه های smart grid
جهت اطلاعات بیشتر کسانی که دوست دارند می تونن به بنده پیام
بدن.

@Raminmousa
@Paper4money
@machine_learn
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Machine learning books and papers pinned «با عرض سلام در ادامه فرایند نگارش مقالات سعی داریم چند گروه ۴ نفره برای مقالات با موضوعات مختلف ایجاد کنیم. چهار موضوع که می خواهیم در ان ها کار کنیم از قبیل زیر می باشند: ۱ - طبقه بندی تصاویر پزشکی ۲- پیش بینی ترافیک شبکه ۳- حل مشکلات شبکه های RNN در مساله…»
Fluent Python

📚 Book

@Machine_learn
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This open-source RAG tool for chatting with your documents is Trending at Number-1 in Github from the past few days

🔍 Open-source RAG UI for document QA
🛠️ Supports local LLMs and API providers
📊 Hybrid RAG pipeline with full-text & vector retrieval
🖼️ Multi-modal QA with figures & tables support
📄 Advanced citations with in-browser PDF preview
🧠 Complex reasoning with question decomposition
⚙️ Configurable settings UI
🔧 Extensible Gradio-based architecture

Key features:

🌐 Host your own RAG web UI with multi-user login
🤖 Organize LLM & embedding models (local & API)
🔎 Hybrid retrieval + re-ranking for quality
📚 Multi-modal parsing and QA across documents
💡 Detailed citations with relevance scores
🧩 Question decomposition for complex queries
🎛️ Adjustable retrieval & generation settings
🔌 Customizable UI and indexing strategies



Github

@Machine_learn
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WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling

Paper: https://arxiv.org/pdf/2408.16532v1.pdf

Code: https://github.com/jishengpeng/wavtokenizer

Dataset: AudioSet LibriTTS SLURP

@Machine_learn
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Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

Paper: https://arxiv.org/pdf/2408.15998v1.pdf

Code: https://github.com/nvlabs/eagle

@Machine_learn
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2025/02/22 15:43:16
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