Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Neural Networks
- What are neural networks?
- Current status of neural network applications
- Neural networks versus regression models
- Supervised and unsupervised learning
Overview of Available Packages
- nnet, neuralnet, and others
- Differences between packages and their limitations
- Visualising 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 distribution
- The polymorphic nature of the sigmoid function
- Other activation functions
- Constructing 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
- Normalisation
- Learning neural networks
- Backpropagation
- Propagation steps
- Network training algorithms
- Range of application
- Estimation
- Problems with approximation potential
- Examples
- OCR and image pattern recognition
- Other applications
- Implementing a neural network modelling task to predict stock prices of listed companies
Requirements
Familiarity with programming in any language is recommended.
14 Hours
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.
