OpenNN: Implementing neural networks Training Course
14 hours (usually 2 days including breaks)
- An understanding of data science concepts
- C++ programming experience is helpful
OpenNN is an open-source class library written in C++ which implements neural networks, for use in machine learning.
In this course we go over the principles of neural networks and use OpenNN to implement a sample application.
Software developers and programmers wishing to create Deep Learning applications.
Format of the course
Lecture and discussion coupled with hands-on exercises.
Introduction to OpenNN, Machine Learning and Deep Learning
Working with Neural Designer
Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics
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)
Compiling with QT Creator
Integrating, testing and debugging your application
The future of neural networks and OpenNN
Bookings, Prices and Enquiries
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|Number of Delegates||Public Classroom|
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