Code formation
OpenNN
Durée
14 heures (généralement 2 jours pauses comprises)
Pré requis
- An understanding of data science concepts
- C++ programming experience is helpful
Audience
- Software developers and programmers wishing to create Deep Learning applications.
Aperçu
OpenNN est une bibliothèque de classes open-source écrite en C ++ qui implémente des réseaux de neurones, pour une utilisation en apprentissage automatique.
Dans ce cours, nous allons passer en revue les principes des réseaux de neurones et utiliser OpenNN pour implémenter un exemple d'application.
Public
Les développeurs de logiciels et les programmeurs souhaitant créer des applications d'apprentissage approfondi.
Format du cours
Lecture et discussion associées à des exercices pratiques.
Machine Translated
Plan du cours
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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