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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