OpenNN: Implementing Neural Networks Treningskurs




14 timer (vanligvis 2 dag inkludert pauser)


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


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


I denne instruktørledede, OpenNN , går vi over prinsippene i nevrale nettverk og bruker OpenNN til å implementere et eksempelapplikasjon.

    Kursets format

    • Foredrag og diskusjon kombinert med praktiske øvelser.

    Machine Translated


    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



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