IE 534, Fall 2018
Instructor: Justin Sirignano
What is Deep Learning? |
Deep learning has revolutionized image recognition, speech recognition, and natural language processing. There's also growing interest in applying deep learning to science, engineering, medicine, and finance.
At a high level, deep neural networks are stacks of nonlinear operations, typically with millions of parameters. This produces a highly flexible and powerful model which has proved effective in many applications. The design of network architectures and optimization methods have been the focus of intense research.
Topics include convolution neural networks, recurrent neural networks, and deep reinforcement learning. Homeworks on image classification, video recognition, and deep reinforcement learning. Training of deep learning models using TensorFlow and PyTorch. A large amount of GPU resources are provided to the class. See Syllabus for more details.
Mathematical analysis of neural networks, reinforcement learning, and stochastic gradient descent algorithms will also be covered in lectures. (However, there will be no proofs in homeworks and the midterm.)
IE 534 Deep Learning is cross-listed with CS 598.
This course is part of the Deep Learning sequence:
A large amount of GPU resources are provided to the class (50,000 hours). Graphics processing units (GPUs) can massively parallelize the training of deep learning models. This is a unique opportunity for students to develop sophisticated deep learning models at large scales.
Extensive TensorFlow and PyTorch code is provided to students. This code is distributed to UIUC students who are enrolled in the course.
In HW6, a deep learning model is trained to predict the action occurring in a video solely using the raw pixels in the sequence of frames. The five most likely actions according to the deep learning model are reported (selected from a total of 400 possible actions).
In HW9, a deep learning model learns to play the Atari video game using only the raw pixels in the sequence of frames (as a human would learn).