Cris Ippolite’s Post

Explore the world of open source LLMs. The latest episode of the ODSC Ai X Podcast takes you through the entire lifecycle of open source large language models (LLMs) with Dr. Jon Krohn, Co-Founder and Chief Data Scientist at Nebula and bestselling author of Deep Learning Illustrated. Get ready to explore: ✅ Open Source: Delve into what is required for a LLM to truly be considered open source.  ✅ Tools and Techniques: Learn about Key Libraries, the LoRA (Low-Rank Adaptation) technique for efficient fine-tuning, RAG, and more ✅ Deploying to Production: Best practices for deploying models to production to reduce costs and increase accuracy. 🚀 This episode is a must-listen for anyone interested leveraging open source LLMs!🚀 🎧 Listen Now:  https://linktr.ee/odsc #LLM #AI #OpenSource #Podcast #ODSC

Thanks Open Data Science Conference (ODSC) for having me on your "Ai X" podcast! Listen in to to hear what about deploying open-source vs proprietary LLMs to production, fine-tuning with LoRA and retrieval-augmented generation (RAG).

A compelling talk about #AI and potential of RAG (Retrieval Augmented Generation) with Edo Liberty. RAG involves a process where an AI accesses a repository of documents or information. This approach allows models to provide answers that are not just based on generalized pre-training, but are also tailored to the specifics of the query by incorporating up-to-date and specific information from the retrieved documents. 🔗 https://lnkd.in/e8QAHfEZ #AI #RAG #Innovation

In this episode, Jon Krohn delves into the lifecycle of open source Large Language Models, from transformer architecture to deploying to production. #DataScience #AI #ArtificialIntelligence https://hubs.li/Q02zPYxv0

In this episode, Jon Krohn delves into the lifecycle of open source Large Language Models, from transformer architecture to deploying to production. #AI #datascience #artificialintelligence https://hubs.li/Q02C14YF0

In this episode, Jon Krohn delves into the lifecycle of open source Large Language Models, from transformer architecture to deploying to production. #AI #datascience #artificialintelligence https://hubs.li/Q02C13KQ0

In this episode, Jon Krohn delves into the lifecycle of open source Large Language Models, from transformer architecture to deploying to production. #DataScience #AI #ArtificialIntelligence https://hubs.li/Q02ywsp20

Explore the world of open source LLMs. In this episode of the ODSC Ai X Podcast takes you through the entire lifecycle of open source large language models (LLMs) with Dr. Jon Krohn, Co-Founder and Chief Data Scientist at Nebula and bestselling author of Deep Learning Illustrated. Get ready to explore: ✅ Open Source: Delve into what is required for a LLM to truly be considered open source. ✅ Tools and Techniques: Learn about Key Libraries, the LoRA (Low-Rank Adaptation) technique for efficient fine-tuning, RAG, and more ✅ Deploying to Production: Best practices for deploying models to production to reduce costs and increase accuracy. 🚀 This episode is a must-listen for anyone interested leveraging open source LLMs!🚀 🎧 Listen Now: https://hubs.ly/Q02ypshW0 #LLM #AI #OpenSource #Podcast #ODSC

"But If you are using it just as an editing assistant, you don't even need to disclose that you used it." In this episode, we talk with professor Lennart Nacke about the use of AI in academic writing. We talk about: - The role of mindset in embracing AI as an assistant in academic writing. - Ethical considerations and best practices for utilizing AI tools. - The future of academic publishing with AI integration - and much more Listen to episode 76 here: (link in comments)

Today's GeekWire Podcast with Juan M. Lavista Ferres of Microsoft's AI for Good Lab is based on the new book, "AI for Good: Applications in Sustainability, Humanitarian Action, and Health," due out next week, detailing the lab's work with its partners. I got to read an advance copy in advance of the conversation with Juan. I'd recommend the book for anyone interested in getting a better understanding of the inner workings of large-scale AI projects, including the limitations and long-term potential. • The book is well-organized and formatted so you can jump to the projects that interest you in sustainability, humanitarian action, and health. • Each chapter gives an executive summary of the project, and then goes deeper on the methods and implications. If you want to know more, there are references to the full research articles for additional information. • The first three chapters serve as a primer on AI. They filled in several gaps in my knowledge, things I didn't know I didn't know. There's so much abstract discussion about AI these days, the tangible examples were refreshing. As a non-computer scientist trying to improve my understanding of AI and inform my reporting, I found it worth the time. Book: https://lnkd.in/g4njVbD9 Podcast: https://lnkd.in/g7_dYUef