Foundations of Machine Learning
The conversation highlights the enduring importance of foundational concepts like linear algebra and calculus in machine learning, despite rapid advancements in the field. Both speakers emphasize the stability of frameworks like PyTorch, noting that code from years ago remains functional due to backward compatibility. They also discuss how modern architectures, such as transformers, are built upon these fundamental building blocks, reinforcing the idea that the core principles of machine learning are here to stay.In this clip
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