Dive deep into advanced deep learning models and architectures used in modern AI. From CNNs to Transformers, this course covers it all.
What you will learn
Build Cutting-Edge Models: Design and train advanced deep learning architectures, specializing in both CNNs for vision and the fundamental principles of Transformer Networks.
Solve Diverse AI Problems: Implement practical solutions across all core machine learning paradigms: supervised (Classification/Regression), unsupervised, and self-supervised learning.
Master Feature Engineering: Apply specialized techniques (PCA, t-SNE, Autoencoders) to prepare data, visualize complex feature spaces, and dramatically improve model performance.
Implement Modern Systems: Apply learned theory to model complex challenges like Learning to Rank, graph-based problems, and large language model concepts.