OpenNN: Implementing Neural Networks

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