Deep Learning (2025)
Published:
Embarking on a deep learning course is akin to opening the door to the future of technology and innovation. As you navigate through the foundational chapters on Single-Layer Networks, you will gain the essential skills to understand and manipulate the building blocks of deep learning. This journey progresses into Deep Neural Networks, Optimization, and Regularization, equipping you with the tools to optimize model performance and ensure their robustness against overfitting. The graded and non-graded assignments are designed to reinforce your understanding through practical application, allowing you to bridge the gap between theory and practice.
As you delve deeper into the course, the exploration extends into advanced topics such as Convolutional Neural Networks, Transformers, and Graph Neural Networks. These chapters introduce cutting-edge techniques that are driving breakthroughs in computer vision, natural language processing, and beyond