Explores optimizing library interactions, functionality challenges, and modularity in modern workloads, emphasizing strong boundaries between systems and instruction-level optimizations.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.
