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Course Outline

Current technological landscape

  • Existing implementations
  • Potential future applications

Rule-based AI

  • Simplifying decision-making processes

Machine Learning

  • Classification
  • Clustering
  • Neural Networks
  • Types of Neural Networks
  • Review of practical examples and discussion

Deep Learning

  • Essential terminology
  • When to utilise Deep Learning and when to avoid it
  • Assessing computational resources and costs
  • Concise theoretical background on Deep Neural Networks

Practical Deep Learning (primarily using TensorFlow)

  • Data preparation
  • Selecting the loss function
  • Choosing the appropriate neural network type
  • Balancing accuracy, speed, and resource usage
  • Training the neural network
  • Evaluating efficiency and error rates

Illustrative use cases

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

Requirements

Participants are required to have a programming background in any language and an engineering foundation. However, no coding exercises are mandatory during the course.

 14 Hours

Number of participants


Price per participant

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