Leveraging Large Models
Machine learning practitioners can harness the power of large language models, fine-tuning them with just a few hundred data points for impressive results at minimal cost. The discussion highlights the advantages of using LLMs to solve specific business problems, making data science more efficient. Additionally, the introduction of Polars, a faster alternative to Pandas, offers a significant boost in performance for data manipulation tasks.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
826: In Case You Missed It in September 2024 — with Jon Krohn (@JonKrohnLearns)
Related Questions
How are large language models (LLMs) trained, as discussed in the episode 670: LLaMA: GPT-3 performance, 10x smaller — with Jon Krohn (@JonKrohnLearns) and the clip Llama Model Insights?
How do you leverage different models in machine learning as discussed in the episode 708: ChatGPT Code Interpreter: 5 Hacks for Data Scientists — with Jon Krohn (@JonKrohnLearns) and the clip Model Training Hacks?
What are some techniques for training machine learning models as discussed in the episode 708: ChatGPT Code Interpreter: 5 Hacks for Data Scientists — with Jon Krohn (@JonKrohnLearns) and the clip Model Training Hacks?