Teaching assistant of Deep Learning: Models and Optimization

Teacher : Marco Cuturi

Introductory course to Deep Learning in Practice, with practical sessions in Pytorch.

Program

• elementary blocks from signal processing and statistics: spatial and temporal convolutions, activation functions, compositions
• automatic differentiation: gradients, jacobians
• review of a few famous nets for vision applications: AlexNet, Resnet,…
• stochastic optimization of parameters for non-convex problems (RMSprop, ADAM etc..)
• theory: convex models for simple two-layer perceptrons; network structure optimization
• recurrent networks and the vanishing gradient problem, LSTM, memory and attention mechanisms.
• deep networks in action: GANs and VAEs
• applications to structured data: graph NN.