Neural Network in R Training Course

Course Code

rneuralnet

Duration

14 hours (usually 2 days including breaks)

Requirements

Programming in any programming language recommended .

Overview

This course is an introduction to applying neural networks in real world problems using R-project software.

Course Outline

Introduction to Neural Networks

  1. What are Neural Networks
  2. What is current status in applying neural networks
  3. Neural Networks vs regression models
  4. Supervised and Unsupervised learning

Overview of packages available

  1. nnet, neuralnet and others
  2. differences between packages and itls limitations
  3. 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

Testimonials

new insights in deep machine learning

Josip Arneric - Faculty of Economics and Business Zagreb

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 - Faculty of Economics and Business Zagreb

Graphs in R :)))

- Faculty of Economics and Business Zagreb

Bookings, Prices and Enquiries

Guaranteed to run even with a single delegate!

Private Classroom

From £2900

Private Remote

From £2600 (105)

Public Classroom

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