Learning with Neural Networks
Learn how to mathematically design, interpret and evaluate deep learning models that are designed to overcome challenges in traditional machine learning models by learning succesive layers of representations in terms of other simpler representations, modelled as neural networks.
This course provides an in-depth theoretical introduction to Deep Learning - a subfield of machine learning that is underpinning modern advances in artificial intelligence with applications including self-driving cars, object detection, machine vision, speech recognition, fraud detection, drug discovery and bioinformatics. This course explores in detail the major topics in Deep Learning including gradient-based optimisation, deep feedforward networks, regularisation and optimisation, recurrent neural networks and long/short term memory networks, convolutional neural networks, deep belief networks, generative adversarial networks and autoencoders. This course is a fundamental pre-requisite in order to design, build, interpret and evaluate applied deep learning models using modern deep learning frameworks including TensorFlow and Keras, as it enables principal and lead data scientists to genuinely understand how deep learning works under-the-hood beyond simple deployment of existing cloud-based deep learning services.