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Course Outline
Introduction to Neural Networks
- What are Neural Networks
- What is current status in applying neural networks
- Neural Networks vs regression models
- Supervised and Unsupervised learning
Overview of packages available
- nnet, neuralnet and others
- Differences between packages and itls limitations
- Visualizing neural networks
Applying Neural Networks
- Concept of neurons and neural networks
- A simplified model of the brain
- Opportunities neuron
- XOR problem and the nature of the distribution of values
- The polymorphic nature of the sigmoidal
- Other functions activated
- Construction of neural networks
- Concept of neurons connect
- Neural network as nodes
- Building a network
- Neurons
- Layers
- Scales
- Input and output data
- Range 0 to 1
- Normalization
- Learning Neural Networks
- Backward Propagation
- Steps propagation
- Network training algorithms
- range of application
- Estimation
- Problems with the possibility of approximation by
- Examples
- OCR and image pattern recognition
- Other applications
- Implementing a neural network modeling job predicting stock prices of listed
Requirements
Programming in any programming language recommended .
14 Hours
Testimonials (4)
Many exercises that I can directly apply in my work
Alior Bank S.A.
Course - Sieci Neuronowe w R
Machine Translated
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.
