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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
  • Working examples and discussion

Deep Learning

  • Core terminology
  • When to apply Deep Learning and when to avoid it
  • Estimating computational resources and costs
  • Concise theoretical background on Deep Neural Networks

Deep Learning in practice (primarily using TensorFlow)

  • Data preparation
  • Selecting a loss function
  • Choosing the appropriate neural network architecture
  • Balancing accuracy, speed, and resources
  • Training the neural network
  • Evaluating efficiency and error rates

Sample usage

  • Anomaly detection
  • Image recognition
  • ADAS

Requirements

Participants are expected to have a programming background (in any language) and engineering knowledge. However, no coding is required during the course.

 14 Hours

Number of participants


Price per participant

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