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Course Outline
Introduction to Neural Networks
- Understanding Neural Networks
- Current landscape of neural network applications
- Comparing Neural Networks with regression models
- Supervised and unsupervised learning
Overview of Available Packages
- nnet, neuralnet, and other options
- Differences between packages and their respective limitations
- Visualizing neural networks
Applying Neural Networks
- Core concepts of neurons and neural networks
- A simplified model of the brain
- Potential applications of neurons
- The XOR problem and the nature of value distribution
- The polymorphic nature of sigmoidal functions
- Other activation functions
- Constructing neural networks
- The concept of neuron connectivity
- Neural networks represented as nodes
- Building a network structure
- Neurons
- Layers
- Scaling
- Input and output data
- Range 0 to 1
- Normalization
- Training neural networks
- Backpropagation
- Propagation steps
- Network training algorithms
- Range of applications
- Estimation techniques
- Challenges related to approximation capabilities
- Examples
- OCR and image pattern recognition
- Other applications
- Implementing a neural network modeling task to predict stock prices of listed companies
Requirements
Proficiency in any programming 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.
