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Course Outline
Introduction to Neural Networks
- Understanding Neural Networks
- Current status in applying neural networks
- Comparing Neural Networks with regression models
- Supervised and Unsupervised learning
Overview of Available Packages
- nnet, neuralnet, and other relevant tools
- Key differences between packages and their limitations
- Visualizing neural networks
Applying Neural Networks
- Core concepts of neurons and neural networks
- A simplified model of the brain
- Opportunities for neurons
- 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 interconnected neurons
- Neural networks represented as nodes
- Building a network architecture
- Neurons
- Layers
- Scales
- Input and output data
- Data range from 0 to 1
- Normalization techniques
- Learning in Neural Networks
- Backward Propagation
- Propagation steps
- Network training algorithms
- Scope of application
- Estimation methods
- Challenges related to approximation capabilities
- Examples
- OCR and image pattern recognition
- Other applications
- Implementing a neural network model to predict the stock prices of listed companies
Requirements
Proficiency in programming with 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.
