Discusses advanced Spark optimization techniques for managing big data efficiently, focusing on parallelization, shuffle operations, and memory management.
Explores optimizing library interactions, functionality challenges, and modularity in modern workloads, emphasizing strong boundaries between systems and instruction-level optimizations.
Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
