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Course Outline

Current State of Technology

  • Existing applications
  • Potential future use cases

Rule-Based AI

  • Simplifying decision processes

Machine Learning

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

Deep Learning

  • Foundational terminology
  • Determining when to apply Deep Learning versus other methods
  • Estimating computational requirements and costs
  • Brief theoretical overview of Deep Neural Networks

Practical Deep Learning (primarily using TensorFlow)

  • Data preparation
  • Selecting an appropriate loss function
  • Choosing the suitable neural network architecture
  • Balancing accuracy against speed and resource consumption
  • Training the neural network
  • Evaluating efficiency and error rates

Sample Use Cases

  • Anomaly detection
  • Image recognition
  • ADAS

Requirements

Participants are expected to have programming experience in any language and an engineering background. However, no coding tasks are required during the course sessions.

 14 Hours

Number of participants


Price per participant

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