MATH-212 Analyse numérique et optimisation Summer 24 Monday sessions
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Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Explores Recurrent Neural Networks for behavioral data, covering Deep Knowledge Tracing, LSTM, GRU networks, hyperparameter tuning, and time series prediction tasks.
Delves into the challenges and benefits of deep learning, highlighting the transition to convolutional neural networks and the impact of network width on the loss landscape.
Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.
Introduces a functional framework for deep neural networks with adaptive piecewise-linear splines, focusing on biomedical image reconstruction and the challenges of deep splines.
Covers Multi-Layer Perceptrons (MLP) and their application from classification to regression, including the Universal Approximation Theorem and challenges with gradients.