Big Data Analytics Big Data Analytics Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim, Germany C. Distributed Computing Environments / 3. Computational Graphs (TensorFlow) Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany C. Distributed Computing Environments / 3. Computational Graphs (TensorFlow) 1 / 31 Big Data Analytics Syllabus Tue. 9.4. (1) 0. Introduction Tue. 16.4. Tue. 23.4. Tue. 30.4. (2) (3) (4) A. Parallel Computing A.1 Threads A.2 Message Passing Interface (MPI) A.3 Graphical Processing Units (GPUs) Tue. 7.5. Tue. 14.5. Tue. 21.5. (5) (6) (7) B. Distributed Storage B.1 Distributed File Systems B.2 Partioning of Relational Databases B.3 NoSQL Databases Tue. 28.5. Tue. 4.6. Tue. 11.6. Tue. 18.6. (8) (9) (10) C. Distributed Computing Environments C.1 Map-Reduce C.2 Resilient Distributed Datasets (Spark) Pentecoste Break — C.3 Computational Graphs (TensorFlow) Tue. 25.6. Tue. 2.7. (11) (12) D. Distributed Machine Learning Algorithms D.1 Distributed Stochastic Gradient Descent D.2 Distributed Matrix Factorization Tue. 9.7. (13) Questions and Answers Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany C. Distributed Computing Environments / 3. Computational Graphs (TensorFlow) 1 / 31 Big Data Analytics Outline 1. The Computational Graph 2. Variables 3. Example: Linear Regression 4. Automatic Gradients 5. Large Data I: Feeding 6. Large Data II: Reader Nodes 7. Debugging Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany C. Distributed Computing Environments / 3. Computational Graphs (TensorFlow) 1 / 31 Big Data Analytics 1. The Computational Graph Outline 1. The Computational Graph 2. Variables 3. Example: Linear Regression 4. Automatic Gradients 5. Large Data I: Feeding 6. La

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