Shruti Chamare’s Post

Understanding the generative AI development process https://lnkd.in/gfTxqiYr Back in the ancient days of machine learning, before you could use large language models (LLMs) as foundations for tuned models, you essentially had to train every possible machine learning model on all of your data to find the best (or least bad) fit. By ancient, I mean prior to the seminal paper on the transformer neural network architecture, “Attention is all you need,” in 2017. To read this article in full, please click here

🚀 Exciting Announcement for #EvoStar2025! 🚀 We are thrilled to announce our special session on the integration of LLMs and Evolutionary Computing at EvoStar 2025! 🌐🤖 This session will explore the cutting-edge intersection of Large Language Models (LLMs) and Evolutionary Computing (EC), unlocking new possibilities in optimization, creativity, and explainability. 🔍 Are you interested in: - Hybrid AI systems combining the strengths of LLMs and EC? - Creative problem-solving through AI evolution? - Leveraging EC to optimize and improve LLMs? Join us in #Trieste from April 23-25, 2025, and be part of the conversation shaping the future of AI and EC! 💡✨ 🔗 Learn more about this session in our latest blog post: https://lnkd.in/eUPdMdGm Don’t miss the opportunity to connect with top researchers and practitioners in this exciting domain. Submit your papers by November 1, 2024! #AI #EvolutionaryComputing #LLMs #EvoStar2025 #EvoLLMs #ArtificialIntelligence #Optimization #ExplainableAI #NaturalComputing #Research

An excellent course for those seeking to dive deep into the technical aspects of real-world Generative AI implementation. Key takeaways : 1. Gen AI Architecture part to build real-world applications with LLM 2. Prompt Engineering 3. Fine tuning algorithms and techniques such as PEFT, LoRA, QLoRA, RLHF, RAG, ReAct, etc.. 4. Compute and Storage constraints/budget Thanks to members of DeepLearning.AI for well structured, well explained and surely lot of effort and dedication for a wonderful course. https://lnkd.in/g6a7BGqr #GenerativeAI #llm #artificialintelligence

🌟 Excited to share a breakthrough in Large Language Models (LLMs) efficiency: SUBLLM. This innovation integrates subsampling, upsampling, and bypass modules, resulting in remarkable enhancements in both training and inference speeds as well as memory usage when compared to LLaMA. Find out more about this novel architecture and its impact on LLMs here: https://bit.ly/4en487x #LanguageModels #AI #Innovation

I’ve successfully completed the "Generative AI with Large Language Models" course. 🎓 This experience has deepened my understanding of LLMs, their architecture, fine-tuning techniques, and real-world applications in generative AI. 🚀 #KnowledgeIsPower

Excited to share that I’ve completed the "Generative AI with Large Language Models" course! 🙌🏻 I learned about Large Language Models' use cases and lifecycles, LLMs types and transformer architecture, and how to fine-tune these models with soft prompts and LoRA for better performance in different tasks. I also gained insights into overcoming LLM challenges with RAG and LangChain. #GenerativeAI #LLM #MachineLearning #AI

Day 3 of Learning: Today, I explored CNN architecture and CNN layers. Check out the full story on my blog! 👇 https://lnkd.in/gKa7iz2s #CNN #MachineLearning #AI #DeepLearning #TechBlog #NeuralNetworks #NewLearning

I finished writing an exhaustive piece on a novel neural network architecture that beats MLPs, KANs, conventional Transformers, and Mamba in different real-world tasks. It's a deep dive where we learn to build this architecture from the ground up, both mathematically and then in code, all in easy-to-follow language. This is a ton of value! Publishing soon. Follow along: https://intoai.pub #artificialintelligence #ai #datascience #tech

Just finished the course "Generative AI with Large Language Models" This amazing course provides an in-depth understanding of working with LLMs, covering everything from the Transformers architecture to developing generative AI applications. #deeplearning.ai #amazonwebservices #cursera #generativeai

Fourier Analysis Networks (FANs) Are Here To Break Barriers In AI