))
Data Pruning to Improve AI Performance
Fetch error
Hmmm there seems to be a problem fetching this series right now.
Last successful fetch was on November 09, 2024 13:09 (
What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.
Manage episode 444738223 series 3605861
เนื้อหาจัดทำโดย Brian Carter เนื้อหาพอดแคสต์ทั้งหมด รวมถึงตอน กราฟิก และคำอธิบายพอดแคสต์ได้รับการอัปโหลดและจัดหาให้โดยตรงจาก Brian Carter หรือพันธมิตรแพลตฟอร์มพอดแคสต์ของพวกเขา หากคุณเชื่อว่ามีบุคคลอื่นใช้งานที่มีลิขสิทธิ์ของคุณโดยไม่ได้รับอนุญาต คุณสามารถปฏิบัติตามขั้นตอนที่แสดงไว้ที่นี่ https://th.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 ตอน
แบ่งปัน
