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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 (5)
That Haytham started with the basics and gave us enough time to do the examples and ensure that we were at the same page before we moved on to the next topic.
Jaco Dreyer - Africa Health Research Institute
Course - R Fundamentals
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
The subject matter and the pace were perfect.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
I really was benefit from the real life practical examples.
Wioleta
Course - Data and Analytics - from the ground up
The flexible and friendly style. Learning exactly what was useful and relevant for me.