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Course Outline
Introduction to OpenNN, Machine Learning and Deep Learning
Downloading OpenNN
Working with Neural Designer
- Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics
OpenNN architecture
- CPU parallelization
OpenNN classes
- Data set, neural network, loss index, training strategy, model selection, testing analysis
- Vector and matrix templates
Building a neural network application
- Choosing a suitable neural network
- Formulating the variational problem (loss index)
- Solving the reduced function optimization problem (training strategy)
Working with datasets
- The data matrix (columns as variables and rows as instances)
Learning tasks
- Function regression
- Pattern recognition
Compiling with QT Creator
Integrating, testing and debugging your application
The future of neural networks and OpenNN
Summary and conclusion
Requirements
- An understanding of data science concepts
- C++ programming experience is helpful
Audience
- Software developers and programmers wishing to create Deep Learning applications.
14 Hours