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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

Fluent Python

📚 Book

✅@Machine_learn

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

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

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

Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming

Paper: https://arxiv.org/pdf/2408.16725v2.pdf

Code: https://github.com/gpt-omni/mini-omni

Dataset: LibriSpeech

✅@Machine_learn

🖥 UNet 3+ Implementation in TensorFlow

This article presents an implementation of the UNet 3+ architecture using TensorFlow.

UNet 3+ extends the classic UNet and UNet++ architecture.

This article looks at each block of the UNet 3+ architecture and explains how they work and what helps improve the performance of the model.

Understanding these blocks will help us understand the mechanisms behind UNet 3+ and how it effectively tackles tasks such as image segmentation or other pixel-wise prediction tasks.

https://idiotdeveloper.com/unet-3-plus-implementation-in-tensorflow/

✅@Machine_learn

🖥 An Introduction to Tensors for Students
of Physics and Engineering

▪ Book

@Machine_learn

🖥 Awesome LLM Strawberry (OpenAI o1)



▪ Github

✅@Machine_learn

https://www.sciencedirect.com/journal/knowledge-based-systems

✅if=7.2
✅@Raminmousa
✅@Machine_learn

Title: The Little Book of Deep Learning
Author: François Fleuret
Tags: #Deep_learning

✅@Machine_learn

LLM based Multi-Agent methods

🖥 Github: https://github.com/AgnostiqHQ/multi-agent-llm

📕 Paper: https://arxiv.org/abs/2409.12618v1

🤗 Dataset: https://paperswithcode.com/dataset/hotpotqa

✅https://www.tg-me.com/deep_learning_proj

Title: DEEP LEARNING INTERVIEWS
Author: SHLOMO KASHANI
Tags: Deep_learning

✅@Machine_learn

Paper: Understanding LLMs: A Comprehensive Overview from Training to Inference

Tags: LLMs

✅@Machine_learn