In this class, we give some background on numerical methods for the minimization of real functions, including convex sets and functions, steepest descent and possibly Newton's method in arbitrary dimension.
Then we will detail the stochastic gradient method, and its applications to Machine Learning and Deep Neural Networks.
- Enseignant: Sylvain Arguillère