Machine learning books and papers

WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training 🖥 Github: https://github.com/penfever/wildchat-50m 📕 Paper: https://arxiv.org/abs/2501.18511v1 🧠 Dataset: https://huggingface.co/collections/nyu-dice-lab/wildchat-50m-679a5df2c5967db8ab341ab7 @Machine_learn

📃Can social network analysis contribute to supply chain management? A systematic literature review and bibliometric analysis 📎 Study paper @Machine_learn

⁉️چرا وقتی می‌تونی با همین دانش در ایران درآمد دلاری داشته باشی، به ریال قانع می‌شی؟ 💎 با شرکت در وبینار «دکتر علیرضا قیمتی» از تجربه‌های این کارآفرین جوان در بازارهای بین‌المللی رایگان استفاده کن. ⭕️ سرفصل‌های مهم این وبینار: - معرفی پلتفرم‌های جهانی فریلنسری - شرایط حضور در بازارهای جهانی - میزان مهارت و میانگین حقوق دریافتی ✅ این وبینار مناسب چه رشته‌هایی هست؟ - برنامه‌نویسی، طراحی سایت، UI & UX دیزاین، معماری، مهندسی مکانیک، موشن گرافیک، دیتا ساینس و... . - دانشجویان و افراد باتجربه در رشته‌های فوق و تمام افرادی که با یک لپ‌تاپ قابلیت ارائه مهارت خود را دارند. ⛔️فرصت استثنایی⛔️ ⚠️ آفر ویژه این هفته‌مون مخصوص افرادی هست که در وبینار شرکت می‌کنن، پس این هفته رو از دست نده! 📌 لینک ثبت‌نام مستقیم رایگان : https://links.etekanesh.com/Machine_le ⬅️ تلگرام : @TekaneshAcademy 👥 پشتیبانی : @Academy_Tekanesh

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism Paper: https://arxiv.org/pdf/2401.02954v1.pdf Code: https://github.com/deepseek-ai/deepseek-llm Dataset: AlignBench @Machine_learn

OpenAI o3-mini System Card 📚 Reed @Machine_learn

اخرین زمان برای مشارکت در این پروژه تا اخر شب...! @Raminmousa

🐋 DeepClaude git clone https://github.com/getasterisk/deepclaude.git cd deepclaude ▪ Github ▪Docs @Machine_learn

با عرض سلام در يكي از پروژه هاي طبقه بندي سرطان پوست نياز به مشاركت داريم. جايگاه نفر سوم خالي مي باشد. 🔸🔻🔸🔻🔸🔻🔻 @Raminmousa

International AI Safety Report 📚 Report @Machine_learn

⭐️ Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph 🖥 Github: https://github.com/dosonleung/fasttog 📕 Paper: https://arxiv.org/abs/2501.14300v1 @Machine_learn

How to run 🐋 DeepSeek locally on your Computer @Machine_learn

نفر ٧ از اين پروژه باقي مونده و تا اخر امشب مي تونيم اضافه كنيم به تيم...! @Raminmousa

💡 Distilabel ▪ GitHub https://t.me/deep_learning_proj

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning Paper submitted by #DeepSeek team has generated significant attention in the AI community. This work addresses the enhancement of reasoning capabilities in Large Language Models (LLMs) through the application of reinforcement learning techniques. The authors introduce a novel framework, DeepSeek-R1, which aims to improve LLM reasoning abilities by incorporating incentives for logical reasoning processes within their training. This integration of reinforcement learning allows LLMs to go beyond basic linguistic processing, developing sophisticated reasoning methods that can boost performance across a wide array of complex applications. This approach has cause lots of discussions in different communities, but it definitely opens up the whole new direction of development for the research. Paper: https://arxiv.org/abs/2501.12948 #nn #LLM @Machine_learn

JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications. To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training. Extensive experiments show that JanusFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches across standard benchmarks. This work represents a step toward more efficient and versatile vision-language models. Paper: https://arxiv.org/pdf/2411.07975v1.pdf Code: https://github.com/deepseek-ai/janus Datasets: GQA MMBench MM-Vet SEED-Bench @Machine_learn

📃 Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development 📎 Study the paper @Machine_learn

در پروژه MedicalRec ما نياز به يه نفر جهت مشاركت داريم(جايگاه ٧) Project Title: MedRec: Medical recommender system for image classification without retraining Github: https://github.com/Ramin1Mousa/MedicalRec Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence Impact factor: 20.8 🔸 7- 200$❌ جهت مشارکت می تونید به ایدی بنده پیام بدین. اموزش نحوه ی انجام کار ، ریویی مقاله و کد نویسی هم داخل این کار خواهیم داشت. 🧠🧠🧠🧠🧠 @Raminmousa