05 Imperial's Deep learning course: Equivariance and Invariance

Admin about this course: http://wp.doc.ic.ac.uk/bkainz/teaching/70010-deep-learning/ Deep Learning Module aims This module addresses the fundamental concepts and advanced methodologies of deep learning and relates them to real-world problems in a variety of domains. The aim is to provide an overview of different approaches, both classical and emerging. The module will equip you with the necessary knowledge and skills to work in the field of deep learning and to contribute to ongoing research in the area. Learning outcomes Upon successful completion of this module you will be able to: express the underlying theoretical concepts of modern deep learning methods compare, characterise and quantitively evaluate various deep learning approaches evaluate the limitations of deep learning apply deep learning techniques to real-world problems in computer vision, speech, text analysis, and graph processing Module syllabus Supervised vs unsupervised learning, generalisation, overfitting Perceptrons, including deep vs shallow models Stochastic gradient descent and backpropagation Convolutional neural networks (CNN) and underlying mathematical principles CNN architectures and applications in image analysis Recurrent neural networks (RNN), long-short term memory (LSTM), gated recurrent units (GRU) Applications on RNNs in speech analysis and machine translation Mathematical principles of generative networks; variational autoencoders (VAE); generative adversarial networks (GAN) Applications of generative networks in image generation Graph neural networks (GNN): spectral and spatial domain methods, message passing