2min chapter

Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 cover image

LLMs Everywhere: Running 70B models in browsers and iPhones using MLC — with Tianqi Chen of CMU / OctoML

Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0

CHAPTER

XGBoose and Deep Learning: A Comparison

If you try to run on tabular data, still most people opt for tree-based models. They have nice properties like being agnostic to scale of input and be able to automatically compose feature together. I do feel like it's good to have a bit of diversity in the model space. It was actually, when we're building TVM, we build cost models for the programs,. That's why we're using actually boost for that as well.

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