CS 131 Discrete Structures Presentation

This presentation provides an introduction to neural networks, exploring their structure, functionality, and various types, including feedforward, convolutional, recurrent, and transformer networks. It explains how these models process data, learn through backpropagation, and are applied in real-world tasks such as image recognition, natural language processing, and time-series prediction. The goal is to offer a clear understanding of how neural networks work, their strengths, limitations, and why they are central to advancements in artificial intelligence.