In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.
Format of the course
- Lecture and discussion coupled with hands-on exercises.
- An understanding of data science concepts
- C++ programming experience is helpful
- Software developers and programmers wishing to create Deep Learning applications.
Introduction to OpenNN, Machine Learning and Deep Learning
Working with Neural Designer
- Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics
- CPU parallelization
- 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)
- Function regression
- Pattern recognition
Compiling with QT Creator
Integrating, testing and debugging your application
The future of neural networks and OpenNN
Summary and conclusion