BIO-642 / 1 crédit

Enseignant: Gerstner Wulfram

Langue: Anglais


Frequency

Only this year

Summary

The Loss Landscape of Neural Networks is in general non-convex and rough, but recent mathematical results lead provide insights of practical relevance. 9 online lectures, lecturers from NYU, Stanford, Shanghai, IST Austria, Google, Facebook, EPFL.

Content

Note

By the end of this course you should be able to explain recent results on the shape and convergence properties of the loss landscape in neural networks.

Keywords

Artificial Neural Networks, Deep Learning, Optimization, Gradient Descent, Loss landscape, Convergence, Convexity, Permutation Symmetry

Learning Prerequisites

Required courses

A Master-level class on Artificial Neural Networks or Machine Learning or Optimization

Dans les plans d'études

  • Nombre de places: 30
  • Forme de l'examen: Mémoire (session libre)
  • Matière examinée: State of the Art Topics in Neuroscience XIII
  • Cours: 10 Heure(s)
  • Projet: 15 Heure(s)

Semaine de référence