Machine learning has become the main framework for building programs that perform intelligent tasks. In fields such as computer vision and natural language processing, many recent successes have been achieved using neural nets with several layers, so-called deep neural nets. Students will look at deep neural nets, techniques for training them from data, and significant applications. They will be presented with network architectures such as convolutional neural nets, autoencoders, recurrent neural nets, long-short term memory networks, and generative adversarial networks. Advanced training techniques to be described include dropout, batch normalization, and adaptive step size selection.
Machine learning has become the main framework for building programs that perform intelligent tasks. In fields such as computer vision and natural language processing, many recent successes have been achieved using neural nets with several layers, so-called deep neural nets. Students will look at deep neural nets, techniques for training them from data, and significant applications. They will be presented with network architectures such as convolutional neural nets, autoencoders, recurrent neural nets, long-short term memory networks, and generative adversarial networks. Advanced training techniques to be described include dropout, batch normalization, and adaptive step size selection.
