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Course Outline
Introduction to Neural Networks
- What are Neural Networks
- Current status in the application of neural networks
- Neural Networks versus regression models
- Supervised and Unsupervised learning
Overview of Available Packages
- nnet, neuralnet, and others
- Differences between packages and their limitations
- Visualizing neural networks
Applying Neural Networks
- The concept of neurons and neural networks
- A simplified model of the brain
- The perceptron
- The XOR problem and the nature of value distributions
- The polymorphic nature of the sigmoid function
- Other activation functions
- Construction of neural networks
- The concept of neuron connectivity
- Neural networks as nodes
- Building a network
- Neurons
- Layers
- Scales
- Input and output data
- Range 0 to 1
- Normalization
- Learning Neural Networks
- Backpropagation
- Propagation steps
- Network training algorithms
- Scope of application
- Estimation
- Challenges in approximation possibilities
- Examples
- OCR and image pattern recognition
- Other applications
- Implementing a neural network modelling task to predict stock prices of listed companies
Requirements
Programming experience in any language is recommended.
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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Testimonials (3)
I mostly enjoyed the graphs in R :))).
Faculty of Economics and Business Zagreb
Course - Neural Network in R
We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
Course - Neural Network in R
I liked the new insights in deep machine learning.
