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 Duration 14 hours

Course Outline

Current State of Technology

  • Existing applications
  • Potential future uses

Rules-Based AI

  • Simplifying decision-making processes

Machine Learning

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

Deep Learning

  • Essential terminology
  • Determining when to use (or avoid) Deep Learning
  • Estimating computational resources and costs
  • Brief theoretical foundation for Deep Neural Networks

Deep Learning in Practice (primarily using TensorFlow)

  • Data preparation
  • Selecting the loss function
  • Choosing the appropriate neural network architecture
  • Balancing accuracy against speed and resource usage
  • Training the neural network
  • Evaluating 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, hands-on coding is not required during the course sessions.

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