Introduction to Deep Learning
Learning Objective:To provide focused training on deep learning algorithms. The students should be able to acquire a principled understanding for the various techniques that have a proven successful record in solving important engineering problems. Further, hands-on experimental training will be provided through the course projects.
Introduction to a principled understanding of the motivation, justification, and design considerations of the deep neural network approach to machine learning, along with hands-on projects using TensorFlow and Keras.
- Students will learn how to make design choices regarding the construction of deep learning algorithms.
- Students will learn about the history and justification for state of the art deep learning algorithms.
- Students will learn how to implement, optimize and tune state of the art deep neural network architectures.
- Students will learn about the security aspects of state of the art deep learning algorithms.
- Students will learn about open research problems in deep learning, and proposed approaches in the literature to tackle them.
Prerequisites:ECE 302, MA 265 (or MA 351)
Applied / Theory:50 / 50
Homework:There will be five homework assignments.
- Convolutional LSTM Neural Networks
- Generative Adversarial Networks
- Adversarial Machine Learning
- Neural Networks with Memory