
From Physics to Computer Science: Symmetry in Neural Networks with Prof. Tess Smidt
Physics for Future
00:00
Navigating AI: From Basics to Breakthroughs
This chapter clarifies the distinctions and relationships between artificial intelligence, machine learning, and deep learning, while addressing classical versus quantum computing. It highlights the complexities of training neural networks, the importance of data representation, and the role of symmetries in enhancing machine learning tasks. Additionally, the discussion anticipates future innovations in geometric deep learning, aiming to improve applications in fields such as self-driving cars and drug design.
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