OpenNN: Implementing Neural Networks Training Course

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