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Course Outline

Current State of Technology

  • Existing implementations
  • 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

  • Key terminology
  • Guidelines for when to use or avoid Deep Learning
  • Estimating computational resources and costs
  • Concise theoretical overview of Deep Neural Networks

Practical Deep Learning (primarily using TensorFlow)

  • Data preparation
  • Selecting the appropriate loss function
  • Choosing the right neural network architecture
  • Balancing accuracy against speed and resource constraints
  • Training the neural network
  • Measuring efficiency and error rates

Sample Applications

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

Requirements

Participants are required to possess a background in engineering and programming experience in any language. However, no coding tasks are required during the course.

 14 Hours

Number of participants


Price per participant

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