Course Description
Covers advanced theoretical concepts for deep neural networks. Topics include convolutional neural networks and their design principles, encoder-decoder architectures, recurrent neural networks, transformers, bounding box detection, image segmentation, generative adversarial networks, diffusion models, etc. Using open-source Python libraries such as NumPy, TensorFlow, and Keras, to understand how theoretical concepts are implemented.
