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🌟 Understanding the Building Blocks of Large Language Models (LLMs) 🌟 Recently, I had the opportunity to dive deep into the core concepts behind building Large Language Models (LLMs), thanks to an insightful lecture by Yann Dubois a PhD student at Stanford, which you can check out here: https://lnkd.in/gPPWFYGe 📚 Through this lecture, I gained a much clearer understanding of: • Data Processing : The importance of transformer models, autoregressive approaches, and tokenization for Language modelling. • Training & Evaluation: Key methods like how perplexity score can be used for evalution in the pretraining and challenges around data efficiency, and research going on synthetic data generation for training LLMs were explored. • Scaling laws & Current SOTA models: Understood how scaling laws can be used to predict how larger models performance can be improved by more data and how costly it is to train a current open source model like LlaMA 3 was discussed. The lecture gave me a better perspective on the intricacies of understanding LLMs, including how data is processed in training the LLMs, techniques like Supervised Fine tuning and Reinforcement Learning Human Feedback which is the core for building Chat assistant bots like ChatGPT. For anyone looking to expand their knowledge on LLMs, I highly recommend giving this lecture a watch! #LLMs #MachineLearning #AI #Stanford #Transformers #Chatbots #LanguageModelling

Diving Deep into the World of LLMs Ever wondered how language models like ChatGPT work? 🤔 Then this video is a MUST WATCH! Check out this insightful lecture from Stanford CS229: 🌐 https://lnkd.in/gE2eB2Ux It breaks down the complex concepts of building Large Language Models (LLMs) into digestible pieces. You'll learn about: Transformer architecture Self-supervised learning Fine-tuning Ethical considerations Whether you're a machine learning enthusiast or just curious about AI, this video is a must-watch. #machinelearning #AI #LLMs #deeplearning #artificialintelligence #Stanford #CS229 #naturallanguageprocessing #NLP #computerscience #datascience #tech #technology #futureofAI #ivy #learning #code #advance #ai

🤖 Machine Learning I Building Large Language Models (LLMs) This lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF). For each component, it explores common practices in data collection, algorithms, and evaluation methods. This guest lecture was delivered by Yann Dubois in Stanford’s CS229: Machine Learning course, in Summer 2024. #AI #MachineLearning #ArtificialIntelligence #NLP

Interested in understanding how Large Language Models (LLMs) work? Check out this insightful video from Stanford's CS229 on Building Large Language Models—a must-watch for anyone passionate about AI and machine learning! 🌐 In this video, Yann Dubois breaks down the core components of LLMs: - Architecture: How Transformers power LLMs by modeling probability distributions over sequences of tokens. - Training: Learn how pre-training teaches the model to predict the next word, and how fine-tuning adapts it to specific tasks. - Scaling Laws: Bigger models and more data often lead to better performance, but with diminishing returns. - Evaluation: Measuring performance on downstream tasks like summarization and question answering. Dive deep into how LLMs are built and what makes them so powerful. 📚 Watch here: https://lnkd.in/d75DViV9 #MachineLearning #AI #LLMs #DeepLearning #StanfordCS229 #Transformers #ArtificialIntelligence #DataScience #NLP

Stanford University has just uploaded a new lecture on YouTube titled "Building Large Language Models (LLMs)", and it's a must-watch for anyone diving into the world of AI and ML! 🎓✨ This lecture provides a comprehensive overview of building ChatGPT-like models, covering both pretraining (language modeling) and post-training (Supervised Fine-Tuning / Reinforcement Learning from Human Feedback). It’s an insightful look into the methods and techniques that go into creating LLMs. 💡 Topics covered include: - Data collection best practices 📊 - Key algorithms used in language modeling 🔍 - Evaluation methods to ensure optimal performance ✅ If you're curious about the inner workings of LLMs, here’s the link to the lecture: https://lnkd.in/g83ngUrE Happy learning! 📚💻 ps. I have attached the slides to the lecture. Thanks to Yann Dubois for sharing! #ai #machinelearning #ml #llms #technology #engineering #genai #llm

In the rapidly advancing domain of artificial intelligence, efficiently operating large language models (LLMs) on consumer-grade hardware represents a significant technical challenge..... #additive #algorithm #AQLM #article #compression #Extreme #helps #language #Large #Learning #Machine #models #Presents #Quantization

Course - Large Language Models (LLMs) Concepts Completed 6th July 2023 #ai #llms #generativeai

LinkedIn Learning: Introduction to Prompt Engineering for Generative AI Software developer and instructor Ronnie Sheer guides you through what large language models are and what problems they may be able to solve. Ronnie dives into text generation, starting with a warning to use text generation AI responsibly, then moving on to Chat GPT, GPT-3, and J1 with few-shot learning. He introduces you to the AI generated image landscape, then shows you how to use Dall-E and Midjourney. Plus, Ronnie goes over a couple of advanced concepts, like how to fine tune your prompts and how to interact with language models using an API. Course Duration: 44 minutes Course Link: https://sl.richmond.edu/uQ

LinkedIn Learning: Introduction to Prompt Engineering for Generative AI Software developer and instructor Ronnie Sheer guides you through what large language models are and what problems they may be able to solve. Ronnie dives into text generation, starting with a warning to use text generation AI responsibly, then moving on to Chat GPT, GPT-3, and J1 with few-shot learning. He introduces you to the AI generated image landscape, then shows you how to use Dall-E and Midjourney. Plus, Ronnie goes over a couple of advanced concepts, like how to fine tune your prompts and how to interact with language models using an API. Course Duration: 44 minutes Course Link: https://sl.richmond.edu/uQ

🚀 Exploring Language Representation Techniques in NLP! In the ever-evolving field of Natural Language Processing (NLP), one core concept is language representation—transforming words into formats that machines can understand. From traditional approaches like Bag of Words to advanced models like BERT and GPT, each technique offers unique insights and strengths for analyzing text data. Here’s a quick overview: 1️⃣ Language Vectorization: Essential for translating complex language data into structured numerical formats. 2️⃣ Embeddings: Low-dimensional, dense representations that capture the meaning and context of words, making it easier for machines to process text. 3️⃣ Word2Vec vs. BERT vs. GPT: - Word2Vec shines with word similarity tasks. - BERT excels in understanding contextual nuances. - GPT is a powerhouse for text generation. Whether you’re working on text classification, sentiment analysis, or chatbots, choosing the right language representation method can make a world of difference! 💡 Curious about how to use these models for your NLP projects? Dive deeper into language representation and learn which technique best suits your needs! #NLP #MachineLearning #ArtificialIntelligence #DataScience #Word2Vec #BERT #GPT #LanguageRepresentation #Embeddings #TextProcessing #innomatocsresearchlabs Kanav Bansal Innomatics Research Labs