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Course Outline

Current state of the technology

  • Current industry standards and applications
  • Emerging and potential future technologies

Rules-based AI

  • Simplifying decision-making processes

Machine Learning

  • Classification techniques
  • Clustering methods
  • Neural Networks
  • Types of Neural Networks
  • Review of practical examples and group discussions

Deep Learning

  • Essential terminology
  • Guidelines for when to apply Deep Learning and when to avoid it
  • Evaluating computational resource needs and associated 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, speed, and resource usage
  • Training neural networks
  • Assessing model efficiency and error rates

Sample Applications

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

Requirements

Participants are expected to have a programming background in any language and an engineering foundation. However, writing code is not a requirement during the course.

 14 Hours

Number of participants


Price per participant

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