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module-3 PublicModule 3 – Decision-making with Scalable Algorithms: Theoretical foundations of distributed systems and analysis of their scalable algorithms for sorting, joining, streaming, sketching, optimizing …
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Module 3 – Decision-making with Scalable Algorithms: Theoretical foundations of distributed systems and analysis of their scalable algorithms for sorting, joining, streaming, sketching, optimizing and computing in numerical linear algebra with applications in scalable machine learning pipelines for typical decision problems (e.g., prediction, A/…
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Module 2 – Distributed Deep Learning: Introduction to the theory and implementation of distributed deep learning: Classification and regression using generalized linear models, including different learning, regularization, and hyperparameters tuning techniques. The feedforward deep network as a fundamental network, and the advanced techniques to…
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Module 1 – Introduction to Data Science: Introduction to fault-tolerant distributed file systems and computing. The whole data science process illustrated with industrial case-studies. A practical introduction to the scalable data processing to ingest, extract, load, transform, and explore (un)structured datasets. Scalable machine learning pipel…
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