2021 High Performance Computing Lecture 10 Parallel and Scalable Machine and Deep Learning Part1 💻

Lecture 10 - Parallel and Scalable Machine and Deep Learning 🖥️ - Part One Advanced Scientific Computing 16 university lectures with additional practical lectures for hands-on exercises in context University of Iceland, School of Engineering and Natural Sciences Faculty of Industrial Engineering, Mechanical Engineering and Computer Science Spring 2021 Course Outline 1. High Performance Computing 2. Parallel Programming with MPI 3. Parallelization Fundamentals 4. Advanced MPI Techniques 5. Parallel Algorithms & Data Structures 6. Parallel Programming with OpenMP 7. Hybrid Programming & Patterns 8. Debugging & Profiling & Performance Analysis 9. Accelerators & Graphical Processing Units 10. Parallel & Scalable Machine & Deep Learning 11. HPC in Health & Neurosciences 12. Computational Fluid Dynamics & Finite Elements 13. Systems Biology & Bioinformatics 14. Molecular Systems & Material Sciences 15. Terrestrial Systems & Climate 16. Epilogue Lecture Outline Part One: Machine Learning Fundamentals Learning Methods Overview & Prerequisites Classification Application & Linear Perceptron Model Training & Testing Process using different Datasets Food Inspection Binary Classification Application Example Linear Regression Model & Logistic Regression Model Part Two: Parallel & Scalable Machine & Deep Learning Techniques piSVM MPI Implementation & Remote Sensing Applications Computing Footprint in Training, Testing & Validation Methods Big Data & Increased Computing Demands for Deep Learning Need for interconnecting GPUs in Distributed Training Parallel & Scalable HPDBSCAN Revisited for Data Clustering