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Course Outline

Current state of the technology

  • What is currently used
  • What may be potentially used

Rules-based AI

  • Simplifying decision processes

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, and 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 of neural network
  • Accuracy vs speed and resources
  • Training neural network
  • Measuring efficiency and error

Sample usage

  • Anomaly detection
  • Image recognition
  • ADAS

Requirements

Participants must have programming experience (in any language) and an engineering background, although they are not required to write any code during the course.

 14 Hours

Number of participants


Price per participant

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