|
Class number:
3135
|
|
Title: Deep Learning |
|
Department: Computer Science |
|
Career: Undergraduate |
|
Component: Lecture |
|
Session: Regular |
|
Instructor's Permission Required: No |
|
Grading Basis: Regular |
|
Units: 1.00 |
| Enrollment limited to 24 |
|
Current enrollment: 23 |
|
Available seats: 1 |
|
Start date: Tuesday, September 5, 2023 |
|
End date: Thursday, December 21, 2023 |
|
Mode of Instruction: In Person |
|
Schedule: TR: 8:00AM-9:15AM, MECC - 136 |
|
|
Instructor(s): Chakraborttii, Chandranil |
|
Prerequisite(s): Prerequisite: C- or better in Computer Science 215. |
|
Distribution Requirement: Meets Numerical & Symbolic Reasoning Requirement |
Course Description:
The course will introduce the students to the fundamentals aspects of artificial neural networks (ANN), convolution neural networks (CNN), recurrent neural networks (RNNs), generative adversarial networks (GAN), and reinforcement learning. The focus will be primarily on the application of deep learning to realworld problems, with some introduction to mathematical foundations. Application of neural network frameworks to natural language processing (NLP), time series, computer vision, security, and data generation problems will be discussed. Python will be the primary programming language for this course. The students will work in teams towards a semester-long project using Google Tensorflow and Keras. |