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Soul Machines는 AI 경험을 인간화하는 글로벌 선도 혁신 기업입니다. 우리는 최첨단의 특허받은 생물학적 AI 기술을 활용하여 개인화된 디지털 연결을 혁신하며, 모두를 위한 접근 가능하고 윤리적인 AI 실현에 전념합니다.
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fast.ai 웹사이트이란 무엇인가요?
fast.ai는 실용적인 딥러닝, 머신 러닝 및 인공지능 (AI)을 위한 강의 및 자원을 제공하는 웹사이트입니다. 이 웹사이트는 신경망을 모든 사람들이 이해하기 쉽고 접근하기 쉽도록 만들기 위해 기술적 배경과 상관없이 모든 사람들을 위한 다양한 주제를 다루는 강의, 자습서 및 기사를 제공합니다.
fast.ai 웹사이트을 어떻게 사용하나요?
fast.ai를 사용하려면, 먼저 강의 및 교육 자료를 탐색하여 시작할 수 있습니다. 이 웹사이트는 코더를 위한 실용적인 딥러닝 강의를 제공하며, PyTorch와 같은 인기있는 프레임워크를 사용하여 신경망을 구축하고 훈련하는 방법을 배울 수 있습니다. 또한 AI 윤리, 기술 발전 및 실제 응용 등에 대한 기사들을 게시하는 블로그에 접근할 수 있습니다. 이 웹사이트는 데이터 과학자들을 위한 도구와 라이브러리도 제공하여 작업 흐름을 향상시킬 수 있습니다.
fast.ai 웹사이트의 핵심 기능
코더를 위한 실용적인 딥러닝 강의
머신 러닝 및 인공지능 자습서
AI 윤리, 기술 발전 및 실제 응용에 대한 블로그 기사
데이터 과학자를 위한 도구와 라이브러리
딥러닝, 머신 러닝 및 AI에 대한 교육 자료
fast.ai 웹사이트의 사용 사례
#1
실용적인 딥러닝 및 머신 러닝 기술 습득
#2
AI 윤리와 발전에 대한 탐구
#3
실제 응용 중인 AI 및 머신 러닝 구현
#4
데이터 과학자들을 위한 도구와 자원에 접근
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fast.ai 웹사이트 Twitter 링크: https://twitter.com/fastdotai
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Dec 2022 - Mar 2025 데스크톱 장치만 해당
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웹사이트 배지를 사용하여 커뮤니티에서 Toolify Launch에 대한 지원을 유도하세요. 홈페이지나 바닥글에 쉽게 삽입할 수 있습니다.
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A Hackers' Guide to Language Models
In this deeply informative video, Jeremy Howard, co-founder of fast.ai and creator of the ULMFiT approach on which all modern language models (LMs) are based, takes you on a comprehensive journey through the fascinating landscape of LMs. Starting with the foundational concepts, Jeremy introduces the architecture and mechanics that make these AI systems tick. He then delves into critical evaluations of GPT-4, illuminates practical uses of language models in code writing and data analysis, and offers hands-on tips for working with the OpenAI API. The video also provides expert guidance on technical topics such as fine-tuning, decoding tokens, and running private instances of GPT models. As we move further into the intricacies, Jeremy unpacks advanced strategies for model testing and optimization, utilizing tools like GPTQ and Hugging Face Transformers. He also explores the potential of specialized datasets like Orca and Platypus for fine-tuning and discusses cutting-edge trends in Retrieval Augmented Generation and information retrieval. Whether you're new to the field or an established professional, this presentation offers a wealth of insights to help you navigate the ever-evolving world of language models. (The above summary was, of course, created by an LLM!) For the notebook used in this talk, see https://github.com/fastai/lm-hackers. 00:00:00 Introduction & Basic Ideas of Language Models 00:18:05 Limitations & Capabilities of GPT-4 00:31:28 AI Applications in Code Writing, Data Analysis & OCR 00:38:50 Practical Tips on Using OpenAI API 00:46:36 Creating a Code Interpreter with Function Calling 00:51:57 Using Local Language Models & GPU Options 00:59:33 Fine-Tuning Models & Decoding Tokens 01:05:37 Testing & Optimizing Models 01:10:32 Retrieval Augmented Generation 01:20:08 Fine-Tuning Models 01:26:00 Running Models on Macs 01:27:42 Llama.cpp & Its Cross-Platform Abilities This is an extended version of the keynote given at posit::conf(2023). Thanks to @wolpumba4099 for chapter titles.
Agentic RAG Explained - Build Your Own AI Agent System from scratch! (Step-by-step code)
🤔 Looking for using AI Agents with RAG? In this episode, join Angelina and Mehdi, for a discussion about Agentic RAG. Who's Angelina: VP of AI and data, Co-founder of Transform AI Studio, two-time fast.ai fellows under Jeremy Howard, published author. https://www.linkedin.com/in/MeetAngelina/ Who's Mehdi: Professor of Computer Science, Co-founder and Chief AI Engineer at Transform AI Studio, NSF fellow, published author. https://www.linkedin.com/in/mehdiallahyari/ What You'll Learn: 🔎 System architecture of Agentic RAG 🚀 Components of an Agentic RAG system: LLM, memory, tools 🛠 Implementing Agentic RAG from scratch vs using frameworks like LangChain ✏️ In This Episode: 00:00 Intro 00:56 What's AI Agent 03:02 Basic architecture for Agentic RAG 09:27 LangChain Code 10:12 Implementation without frameworks 23:17 Minimizing dependencies 📝 Course survey: https://maven.com/forms/e48159 🗓️ Course landing page: https://maven.com/angelina-yang/mastering-rag-systems-a-hands-on-guide-to-production-ready-ai Implementation: https://github.com/mallahyari/twosetai/blob/main/13_agentic_rag.ipynb 🦄 Any specific contents you wish to learn from us? Sign up here: https://noteforms.com/forms/twosetai-youtube-content-sqezrz 🧰 Our video editing tool is this one!: https://get.descript.com/nf5cum9nj1m8 🔨 Implementation: coming soon 📬 Don't miss out on the latest updates - Subscribe to our newsletter: https://mlnotes.substack.com/ 📚 If you'd like to learn more about RAG systems, check out our book on the RAG system: https://angelinamagr.gumroad.com/ 🕴️ Our consulting firm: We help companies that don't want to miss the boat of the current wave of AI advancement by integrating these solutions into their business operations and products. https://www.transformaistudio.com/ Stay tuned for more content! 🎥 Thanks you for watching! 🙌
How Contextual Retrieval Elevates Your RAG to the Next Level
🤔 Looking to enhance your RAG performance? Before we dive in, we have some exciting news! Our RAG live course is coming up soon, and as a way of giving back to our amazing community, we're offering you 15% off. Just use this link: https://maven.com/angelina-yang/mastering-rag-systems-a-hands-on-guide-to-production-ready-ai?promoCode=TwoSetAI We'd love to see you there! 🎉 In the course, you'll have the chance to connect directly with Professor Mehdi (just like I do 😉 in the videos), and you can even ask him your questions 1:1. Bring your real work projects, and during our office hours, we'll help you tackle your day-to-day challenges. This course is for: 01 👇 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: For AI engineers/developers looking to master production-ready RAG systems combining search with AI models. 02 👇 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀: Ideal for data scientists seeking to expand into AI by learning hands-on RAG techniques for real-world applications. 03 👇 𝗧𝗲𝗰𝗵 𝗟𝗲𝗮𝗱𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀: Perfect for tech leads/product managers wanting to guide teams in building and deploying scalable RAG systems In this episode, Mehdi and Angelina introduce the concept of contextual retrieval in Retrieval-Augmented Generation (RAG). We compare it to traditional retrieval methods and discuss how this new approach by Anthropic can significantly reduce retrieval failures by up to 70%. The episode explains the importance of context for Language Learning Models (LLMs) and describes how contextual retrieval enriches document chunks with additional information for improved accuracy. We presents details of the the implementation process, cost considerations, and provide results from Anthropic's experiments showing how contextual retrieval decreases retrieval errors and enhances precision. Who's Angelina: VP of AI and data, Co-founder of Transform AI Studio, two-time fast.ai fellows under Jeremy Howard, published author. https://www.linkedin.com/in/MeetAngelina/ Who's Mehdi: Professor of Computer Science, Co-founder and Chief AI Engineer at Transform AI Studio, NSF fellow, published author. https://www.linkedin.com/in/mehdiallahyari/ What you’ll learn🤓: 🔎 How contextual retrieval works 🚀 Performance and implementation considerations 🛠 Cost considerations and using prompt caching ✏️ In This Episode: 00:00 Introduction to Contextual Retrieval in RAG 00:29 Understanding Contextual Retrieval 02:59 Challenges with Traditional RAG 05:45 Anthropic's Contextual Retrieval Solution 12:54 Implementation and Cost Considerations 15:44 Experimental Results and Recommendations 18:45 Conclusion and Upcoming Course Announcement 🦄 Any specific contents you wish to learn from us? Sign up here: https://noteforms.com/forms/twosetai-youtube-content-sqezrz 🧰 Our video editing tool is this one!: https://get.descript.com/nf5cum9nj1m8 🖼️ Blogpost for today: 🔨 Implementation: 📬 Don't miss out on the latest updates - Subscribe to our newsletter: https://mlnotes.substack.com/ 📚 If you'd like to learn more about RAG systems, check out our book on the RAG system: https://angelinamagr.gumroad.com/ 🕴️ Our consulting firm: We help companies that don't want to miss the boat of the current wave of AI advancement by integrating these solutions into their business operations and products. https://www.transformaistudio.com/ Stay tuned for more content! 🎥 Thanks you for watching! 🙌
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