This session delves into the architectures and mechanisms of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs). Applications in image classification, language modeling, and generative design will be presented, along with current challenges such as overfitting, interpretability, and training efficiency. The session will include discussions on recent advancements in foundation models like GPT and BERT. Real-world project insights, such as computer vision in autonomous vehicles and sentiment analysis in customer service, will illustrate the transformative power of deep learning.