Machine Learning lab
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Welcome to Machine Learning Lab! Explore machine learning and data science with discussions, tutorials, and resources. Discover insights in ML approaches, Projects and practical applications. Admin: @kian_bd
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доля реакций к просмотрам- 17 апр.If you're looking to learn AI from a top institution, Harvard University offers a great collection of Artificial Intelligence courses—many of them free. Explore topics like machine learning, data science, and AI fundamentals, all in one place: https://pll.harvard.edu/subject/artificial-intelligence Good resource for both beginners and experienced developers looking to strengthen their AI skills. @Machin_learning_lab_K0,79%
- 30 нояб.Deep Research: A Systematic Survey Covers: - A 3-stage roadmap for deep research systems - Deep research vs. RAG - Key components: query planning, information acquisition, memory management, answer generation - Optimization: prompting, supervised fine-tuning, agentic RL - Evaluation criteria Posted Date: 26 November 2025 Link: https://www.preprints.org/manuscript/202511.2077 @Machin_learning_lab_K0,58%
- 8 дек.what is model welfare? Model welfare is an emerging field in AI research focused on whether advanced artificial intelligence models could become conscious, experience distress, and thus deserve moral consideration or even rights, prompting research into "low-cost" interventions like letting them end harmful chats, though critics see it as a distraction from human welfare issues. It involves studying AI "preferences" and "distress" (like refusing harmful requests) to understand their inner states, but researchers acknowledge significant uncertainty and debate about true AI sentience. @Machin_learning_lab_K0,56%
- 13 нояб. 2024 г.The Brains Behind LLMs: An Introductory Guide to Transformers and Attention Mechanisms https://www.linkedin.com/pulse/brains-behind-llms-introductory-guide-transformers-attention-sa1me #transformers #embeddings #rag #finetuning #AI @Machine_learning_lab_k0,56%
- 10 дек.Machine Learning from Stanford- by Andrew Ng @Machin_learning_lab_K0,52%
- 3 дек.Teaching Machines How to Think When something broke - and things always break - I didn’t fix the bug. I asked a different question: What was missing from the system’s understanding? Then I added that understanding. Not a patch. Not a hotfix. A new piece of institutional knowledge that would prevent entire categories of future failures. Link: https://yewjin.substack.com/p/the-future-is-solving-problem-solving @Machin_learning_lab_K0,51%
- 27 февр. 2025 г.без подписи0,48%
- 31 янв. 2025 г.📢 Interested in Shiny applications? If you’re curious about how Shiny works in real projects, I’ve built a few interactive applications using R and Python. You can check them out on my GitHub! 🔗 GitHub Repositories Feel free to explore, give feedback, or even contribute! 🚀 #Shiny #DataScience #Python #R #WebApps #MachineLearning @Machine_learning_lab_K0,46%
- 15 дек.Build your first CrewAI Agent Dive in and start creating your own agentic workflows: https://docs.crewai.com/en/quickstart#build-your-first-crewai-agent @Machin_learning_lab_K0,37%
- 26 нояб.Building an AI-Native Engineering Team This document explains how AI coding agents are transforming software engineering. Modern AI models can now handle long, multi-step tasks, allowing them to assist across the entire software development lifecycle—from planning and design to building, testing, reviewing, documenting, and maintaining code. AI takes over repetitive, mechanical work, while engineers focus on architecture, decision-making, and quality. By integrating coding agents into workflows, teams can build faster, reduce errors, improve documentation, and operate more efficiently as AI-native engineering organizations. @Machin_learning_lab_K0,28%
- 4 дек.AWS re:Invent 2025 - The Future of Agentic AI https://x.com/i/broadcasts/1LyGBXjgjpnxN @Machin_learning_lab_K0,27%
- 27 апр.CME 296 - Diffusion & Large Vision Models This course explores diffusion-based generative models for vision. You will study the foundations of diffusion, score matching and flow matching, modern architectures such as Diffusion Transformers, and methods for controllable image generation and evaluation. Link: https://www.youtube.com/playlist?list=PLoROMvodv4rNdy8rt2rZ4T2xM0OjADnfu @Machin_learning_lab_K0,24%