Course Outline

Current state of the technology

  • What is used
  • What may be potentially used

Rules based AI 

  • Simplifying decision

Machine Learning 

  • Classification
  • Clustering
  • Neural Networks
  • Types of Neural Networks
  • Presentation of working examples and discussion

Deep Learning

  • Basic vocabulary 
  • When to use Deep Learning, when not to
  • Estimating computational resources and cost
  • Very short theoretical background to Deep Neural Networks

Deep Learning in practice (mainly using TensorFlow)

  • Preparing Data
  • Choosing loss function
  • Choosing appropriate type on neural network
  • Accuracy vs speed and resources
  • Training neural network
  • Measuring efficiency and error

Sample usage

  • Anomaly detection
  • Image recognition
  • ADAS

 

 

 

 

Requirements

The participants must have programming experience (any language) and engineering background, but are not required to write any code during the course.

  14 Hours
 

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