This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras.
The PUNLAG seminar is intended to supplement the numerical linear algebra course sequence at Purdue. The standard course CS515 doesn't have room for a number of interesting problems -- we hope to cover some in this seminar!
This archive holds videos of past Fields lectures. Lectures are archived in two formats.The interactive format, viewed in a flash-player-enabled desktop web browser, allows you to zoom in and out on specific areas of the blackboards or screens (providing a viewing experience more like being present in the room). The static format, although it does not allow for zooming in to read small blackboard writing, is downloadable and compatible with a wide variety of desktop and mobile video players.