240113100 INTRODUCTION TO NEURAL NETWORK AND DEEP LEARNING ( 3 Crd.Hrs )

Pre-Requisites : 240212010

In this course students are introduced to the basic concepts and architecture of deep neural networks, besides the algorithms that are developed to extract high-level feature representations of data. In addition to theoretical foundations of neural networks, including backpropagation and stochastic gradient descent, students get hands-on experience building deep neural network models with Python. Topics covered in the course include; neural networks basics such as cost function, gradient descent, vectorization, and so on. The course will also describe shallow neural networks as well as deep neural networks.