Machine Learning Rankings
Patrick and Tatsu discuss the importance of raw LMs, supervised fine-tuning, and reinforcement learning in machine learning models. They delve into how the ranking of these methods can change based on the desired level of generalization and amount of data and compute resources available, highlighting the trade-offs between task-specific training and model robustness.In this clip
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Unsupervised Learning
Ep 11: Stanford Professor Tatsu Hashimoto on AI Biases and Improving LLM Performance
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