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Course Outline

Current State of Technology

  • Technologies currently in use
  • Technologies with potential future applications

Rule-Based AI

  • Simplifying decision-making processes

Machine Learning

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

Deep Learning

  • Key terminology
  • Appropriate use cases for Deep Learning
  • Estimating computational resources and costs
  • Concise theoretical background on Deep Neural Networks

Practical Deep Learning (Primarily using TensorFlow)

  • Data preparation
  • Selecting loss functions
  • Choosing the appropriate neural network architecture
  • Balancing accuracy, speed, and resources
  • Training neural networks
  • Evaluating efficiency and error rates

Sample Applications

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

Requirements

Participants are expected to have a programming background in any language and an engineering foundation. No coding tasks are required during the course.

 14 Hours

Number of participants


Price per participant

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