Lectures | Olly Britton

Lecture - Theories of Deep Learning MT25, II, Why deep learning^U
Lecture - Theories of Deep Learning MT25, III, Exponential expressivity with depth^U
Lecture - Theories of Deep Learning MT25, IV, Data classes for which DNNs can overcome the curse of dimensionality^U
Lecture - Theories of Deep Learning MT25, V, Controlling the exponential growth of variance and correlation^U
Lecture - Theories of Deep Learning MT25, VI, Controlling the variance of the Jacobian’s spectrum^U
Lecture - Theories of Deep Learning MT25, VII, Stochastic gradient descent and its extensions^U
Lecture - Theories of Deep Learning MT25, VIII, Optimisation algorithms for training DNNs^U
Lecture - Theories of Deep Learning MT25, XI, Visualising the filters and response in a CNN^U
Lecture - Theories of Deep Learning MT25, XII, The scattering transform and into auto-encoders^U
Lecture - Theories of Deep Learning MT25, XIII, Autoencoders^U
Lecture - Theories of Deep Learning MT25, XIV, Generative adversarial networks^U
Lecture - Theories of Deep Learning MT25, XV, A few things we missed and a summary^U
Lecture - Theories of Deep Learning MT25, XVI, Ingredients for a successful mini-project report^U