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Course Outline

Current State of the Technology

  • Existing applications
  • Potential future uses

Rule-Based AI

  • Simplifying decision processes

Machine Learning

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

Deep Learning

  • Basic terminology
  • When to use Deep Learning versus when not to
  • Estimating computational resources and costs
  • Concise theoretical background of Deep Neural Networks

Deep Learning in Practice (primarily with TensorFlow)

  • Data preparation
  • Selecting a loss function
  • Choosing the appropriate neural network type
  • Accuracy versus speed and resource usage
  • Training the neural network
  • Measuring efficiency and error

Sample Applications

  • Anomaly detection
  • Image recognition
  • ADAS

Requirements

Learners are expected to possess a background in engineering and general programming experience in any language. However, writing code is not a requirement during the course sessions.

 14 Hours

Number of participants


Price per participant

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