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Course Outline

Current State of the Technology

  • Existing applications
  • Potential future applications

Rules-based AI

  • Simplifying decision-making processes

Machine Learning

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

Deep Learning

  • Basic terminology
  • When to use Deep Learning, and when not to
  • Estimating computational resources and cost
  • A concise theoretical overview of Deep Neural Networks

Deep Learning in Practice (primarily using TensorFlow)

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

Sample Use Cases

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

Requirements

Participants are expected to possess programming experience in any language and an engineering background. However, coding is not required during the course.

 14 Hours

Number of participants


Price per participant

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