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 Duration 14 hours

Course Outline

Current state of the technology

  • Existing applications
  • Potential future applications

Rules based AI 

  • Simplifying decision processes

Machine Learning 

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

Deep Learning

  • Basic vocabulary 
  • Determining when to use Deep Learning and when to avoid it
  • Estimating computational resources and costs
  • Concise theoretical background on Deep Neural Networks

Deep Learning in practice (mainly using TensorFlow)

  • Preparing Data
  • Selecting a loss function
  • Choosing the appropriate neural network type
  • Balancing accuracy against speed and resources
  • Training neural networks
  • Evaluating efficiency and error rates

Sample usage

  • Anomaly detection
  • Image recognition
  • ADAS

Requirements

Participants are expected to possess programming experience in any language and a background in engineering; however, there is no requirement to write code during the course sessions.

Number of participants


Price per participant (excl. VAT)

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