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Explores optimization strategies for deep learning accelerators, emphasizing data movement reduction through batching, dataflow optimizations, and compression.
Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
Covers the fundamentals of data stream processing, including tools like Apache Storm and Kafka, key concepts like event time and window operations, and the challenges of stream processing.
