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Course Outline

Current State of Technology

  • Existing applications and tools
  • Potential future technologies

Rules-based AI

  • Streamlining decision-making processes

Machine Learning

  • Classification techniques
  • Clustering methods
  • Neural Networks fundamentals
  • Different types of Neural Networks
  • Review of practical examples and group discussion

Deep Learning

  • Essential terminology
  • Criteria for applying Deep Learning versus other methods
  • Assessment of computational requirements and associated costs
  • Concise theoretical overview of Deep Neural Networks

Deep Learning in Practice (primarily using TensorFlow)

  • Data preparation
  • Selecting the appropriate loss function
  • Choosing the right neural network architecture
  • Balancing accuracy, speed, and resource utilization
  • Training the neural network
  • Evaluating performance and error rates

Practical Applications

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

Requirements

Participants are expected to possess a programming background in any language along with engineering experience. Please note that no coding exercises are required during the course.

 14 Hours

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