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Episode 32: Building Reliable and Robust ML/AI Pipelines

Vanishing Gradients

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Navigating Machine Learning and LLMs

This chapter explores the distinctions between traditional machine learning models and large language models (LLMs), focusing on their error handling and robustness against data issues. It discusses the complexities of validating these models, emphasizing the importance of aligning evaluations with real-world outcomes and scientific rigor. The chapter also covers the shift towards generative AI and the implications of in-context learning, shedding light on the evolving landscape of machine learning applications.

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