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What is 03 15Grad about?
Chapter 3 discusses five essential supervised learning algorithms, focusing on gradient descent as a key optimization technique. It explains the differences between convex and non-convex functions, outlines practical implementations, and highlights the importance of learning rates and mini-batch gradient descent. The chapter also introduces advanced optimizers like Adagrad and Adam, emphasizing their role as optimization solvers rather than machine learning algorithms.
- Author
- A. Kedharnath
- Language
- EN