Share
))
HBO and The Ringer's Bill Simmons hosts the most downloaded sports podcast of all time, with a rotating crew of celebrities, athletes, and media staples, as well as mainstays like Cousin Sal, Joe House, and a slew of other friends and family members who always happen to be suspiciously available.
…
continue reading
Manage episode 504542387 series 3579845
Content provided by Maven Analytics. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Maven Analytics or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
LLMs seem like a hot solution now, until you try deploying one.
In this episode, Andriy Burkov, machine learning expert and author of The Hundred-Page Machine Learning Book, joins us for a grounded, sometimes blunt conversation about why many LLM applications fail.
We talk about sentiment analysis, difficulty with taxonomy, agents getting tripped up on formatting, and why MCP might not solve your problems.
If you’re tired of the hype and want to understand the real state of applied LLMs, this episode delivers.
What You'll Learn:What is often misunderstood about LLMs
The reliability of sentiment analysis
How can we make agents more resilient?
📚 Check out Andriy’s books on machine learning and LLMs:
The Hundred-Page Machine Learning Book
The Hundred-Page Language Models Book: hands-on with Pytorch
Register for free to be part of the next live session: https://bit.ly/3XB3A8b
Follow us on Socials:
68 episodes
