Introduction to Deep Learning and Neural Networks

Catalog description

This course dives into deep learning principles, focusing on artificial neural networks and deep architectures. Students will study backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. Practical applications include image recognition, speech processing, and generative models. The course emphasizes practical implementation using various machine learning platforms and ecosystem tools alongside theoretical analysis of deep learning models.