Covers data stream processing with Apache Kafka and Spark, including event time vs processing time, stream processing operations, and stream-stream joins.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores the transformation of Newton's second law in the context of Einstein's relativity and Lorentz transformations, discussing the equivalence of physical quantities in different inertial frames.
Explores entity resolution techniques for identifying and aggregating different entity profiles across datasets, covering challenges and solutions.
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