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


  • An understanding of data science concepts
  • C++ programming experience is helpful


  • Software developers and programmers wishing to create Deep Learning applications.
  14 Hours


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