))
Data Pruning to Improve AI Performance
Manage episode 444738223 series 3605861
Innhold levert av Brian Carter. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Brian Carter eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
The source is a blog post that describes the author's journey in exploring the potential of data pruning to improve the performance of AI models. They start by discussing the Minipile method, a technique for creating high-quality datasets by clustering and manually discarding low-quality content. The author then explores the concept of "foundational datasets", arguing that refining datasets can lead to better performance and lower training costs. They also discuss how the use of "hard" or "easy" examples in training can affect the model's performance. The post concludes with a practical experiment where the author trains an AI model using varying proportions of a pruned dataset, showcasing how the model's performance changes with different amounts of data. Overall, the post highlights the importance of data quality and refinement in AI model development, suggesting that more data is not always better.
Read more: https://snats.xyz/pages/articles/breaking_some_laws.html
71 episoder
Del
