This course covers the fundamentals of deep learning and its modern applications, including large language models and multimodal systems. It starts with an introduction to deep learning concepts, history, and necessary background. Students will learn the basics of neural networks through programming exercises, including how artificial neurons function, how networks are trained with algorithms such as backpropagation, and how to address issues like vanishing gradients and overfitting. The course then covers advanced topics such as convolutional neural networks for image classification, sequential models for language tasks, and building AI systems for translation, image captioning, and multitask learning. Students will gain practical experience using frameworks like TensorFlow and PyTorch. The course is suitable for those seeking to expand their knowledge and gain skills needed to build and deploy deep learning models.