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Course Outline

Current state of the technology

  • Existing technologies in use
  • Potential future applications

Rules-based AI

  • Simplifying decision processes

Machine Learning

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

Deep Learning

  • Key terminology
  • When to apply Deep Learning and when to avoid it
  • Estimating computational resources and costs
  • Concise theoretical foundation of Deep Neural Networks

Practical Deep Learning (primarily using TensorFlow)

  • Data preparation
  • Selecting the appropriate loss function
  • Choosing the right neural network architecture
  • Balancing accuracy with speed and resource constraints
  • Training neural networks
  • Assessing efficiency and error rates

Sample applications

  • Anomaly detection
  • Image recognition
  • Advanced Driver Assistance Systems (ADAS)

Requirements

Participants are expected to have programming experience in any language and an engineering background. However, no coding is required during the course.

 14 Hours

Number of participants


Price per participant

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