MATLAB Makes Analytics Easy for Engineers
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1) The document discusses how MATLAB can be used for big data analytics, machine learning, and deploying analytic applications. 2) It presents an agenda covering introduction to MATLAB for data analytics, big data, machine learning/deep learning, and deploying analytics. 3) MATLAB allows domain experts to perform tasks like data cleaning, exploration, machine learning using familiar MATLAB functions and interfaces while handling large datasets.
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MATLAB Makes Analytics Easy for Engineers
- 1. 1© 2018 The MathWorks, Inc. Big Data Analytics for Domain Experts with MATLAB Michelle Hirsch, Ph.D. Head of MATLAB Product Management
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- 5. 5 § Introduction to MATLAB for data analytics § Big Data § Machine learning / deep learning § Deploying MATLAB analytics Agenda
- 6. 6 Demo: Introduction to MATLAB Calculate Stability Margin for Flight Test Data § Goal: – Calculate stability margin for the aircraft – the relationship between stick force and load factor § Approach: – Load data from files – Synchronize data – Visualize data
- 7. 7 § Introduction to MATLAB for data analytics § Big Data § Machine learning / deep learning § Deploying MATLAB analytics Agenda
- 8. 8 MATLAB makes big data analytics easy for engineers and scientists File collection management In-memory syntax for out-of-core calculations
- 9. 9 Importing from large file collections
- 10. 10 Datastore manages import from large files/large file collections
- 11. 11 Datastore manages import from large files/large file collections
- 12. 12 Datastore manages import from large files/large file collections Databases Images MDF Files Custom Simulink Supported Data Sources
- 13. 13 Use tall arrays to work with out-of-memory data like any MATLAB array
- 14. 14 Create a tall array backed by a datastore
- 15. 15 Use familiar MATLAB functions on tall arrays
- 16. 16 Clean messy data using common preprocessing functions
- 17. 17 Clean messy data using common preprocessing functions
- 18. 18 Execution model makes operations more efficient on big data § Deferred evaluation – Commands are not executed right away – Operations are added to a queue § Execution triggers include: – gather function – summary function – Machine learning models – Plotting
- 19. 19 Execution model makes operations more efficient on big data Unnecessary results are not computed
- 20. 20 Explore the data with tall visualizations plot scatter binscatter histogram histogram2 ksdensity
- 21. 21 § Run in parallel on Spark clusters § Deploy MATLAB applications as standalone applications on Spark clusters § Run in parallel on compute clusters MATLAB Distributed Computing Server § Run in parallel on multicore desktop Scaling Compute with Tall Arrays Spark + Hadoop Compute ClustersLocal disk Shared folders Databases
- 22. 22 § Introduction to MATLAB for data analytics § Big Data § Machine learning / deep learning § Deploying MATLAB analytics Agenda
- 23. 23 MATLAB makes machine learning/deep learning easy for engineers and scientists Apps Easy programming interfaces
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- 26. 26 Transfer learning in 11 lines of code
- 27. 27 Transfer learning in 11 lines of code
- 28. 28 Transfer learning in 11 lines of code
- 29. 29 Regions with Convolutional Neural Network Features (R-CNN) Semantic Segmentation using SegNet Applied deep learning
- 30. 30 Ex: Deep learning semantic segmentation to label LIDAR point clouds Point cloud semantic segmentation Classification Car Truck Ground Background
- 31. 31 § Introduction to MATLAB for data analytics § Big Data § Machine learning / deep learning § Deploying MATLAB analytics Agenda
- 32. 32 Integrate MATLAB Analytics with enterprise AND embedded systems Automatic code generation Package software component (free runtime required) C/C++ CUDA HDL PLC C/C++ Java Python .NET Excel Spark Desktop app Web app Embedded hardware Enterprise/cloud IT systems
- 33. 33 Energy Load Forecasting • Javascript/HTML front end • Java web service, Apache Tomcat • MATLAB Production Server • Hosted on Amazon EC2
- 34. 34 Enterprise Integration Integrate MATLAB analytics into your technology stack
- 35. 35 Integrate MATLAB Analytics with enterprise AND embedded systems Automatic code generation Package software component (free runtime required) C/C++ CUDA HDL PLC C/C++ Java Python .NET Excel Spark Desktop app Web app Embedded hardware Enterprise/cloud IT systems
- 36. 36 Automatically generate CUDA code from MATLAB § Support for deep learning networks § Generate code for performance or deployment § Integrate generated code with external CUDA code § Deploy deep learning networks on: – Embedded devices, e.g. NVIDIA Tegra / Jetson – Intel and ARM processors
- 37. 37 0 50 100 150 200 250 300 MATLAB GPU Coder + TensorRT MATLAB GPU Coder MXNet MATLAB TensorFlow Images/second Vgg16 Inference on TitanXp 0 100 200 300 400 500 600 700 800 900 1000 MATLAB GPU Coder + TensorRT MATLAB GPU Coder MXNet MATLAB TensorFlow Images/second AlexNet Inference on TitanXp GPU code generation provides best-in-class inference performance
- 38. 38 MATLAB makes … § Data analysis § Big data analytics § Machine learning and deep learning § … … easy for engineers and scientists