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

Видео 05 Imperial's Deep learning course: Equivariance and Invariance канала Bernhard Kainz