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

Requirements

Programming in any programming language recommended .

 14 Hours

Number of participants



Price per participant

Testimonials (5)

Related Courses

Introduction to Data Visualization with Tidyverse and R

7 Hours

OpenNN: Implementing Neural Networks

14 Hours

Advanced R

7 Hours

Algorithmic Trading with Python and R

14 Hours

Anomaly Detection with Python and R

14 Hours

Programming with Big Data in R

21 Hours

Cluster Analysis with R and SAS

14 Hours

Data and Analytics - from the ground up

42 Hours

Data Analytics With R

21 Hours

Data Mining with R

14 Hours

Deep Learning for Finance (with R)

28 Hours

Deep Learning for Banking (with R)

28 Hours

Data Mining & Machine Learning with R

14 Hours

Foundation R

7 Hours

Forecasting with R

14 Hours

Related Categories