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Course Outline

Current State of the Technology

  • Technologies currently in use
  • Potential future applications

Rule-Based AI

  • Simplifying decision processes

Machine Learning

  • Classification
  • Clustering
  • Neural Networks
  • Variations of Neural Networks
  • Review of working examples and group discussions

Deep Learning

  • Key terminology
  • When to apply Deep Learning versus when to avoid it
  • Assessing computational resources and associated costs
  • Concise theoretical introduction to Deep Neural Networks

Practical Applications of Deep Learning (primarily using TensorFlow)

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

Use Cases

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

Requirements

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

 14 Hours

Number of participants


Price per participant

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