AIT 445 - Neural Networks and Deep Learning

Class Hour(s) 3
3 Semester Credit Hour(s)

This course provides an in-depth study of artificial neural networks and their application in deep learning systems. Students will explore the structure, training, and optimization of feedforward, convolutional, recurrent, and generative neural networks. Topics include backpropagation, activation functions, regularization, dropout, autoencoders, and modern deep learning architectures such as CNNs, RNNs, LSTMs, and transformers. Practical implementation using Python libraries such as TensorFlow and PyTorch will be emphasized through hands-on projects and real-world datasets in image recognition, natural language processing, and autonomous systems.

Prerequisite(s): AIT 201, CS 230