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Delves into the energy-efficient distributed machine learning approach in the IoT era, emphasizing the importance of summarizing data for improved communication energy efficiency.
Explains the translation of for-expressions in Scala using map, flatmap, and filter functions, with examples and a discussion on its generalization to different types.
Covers advanced Spark optimizations, memory management, shuffle operations, and data partitioning strategies to improve big data processing efficiency.
Explores the development of data science, education initiatives, and challenges in bridging the gap between data scientists and domain experts at EPFL.
