Get in Touch
 Duration 14 hours

Course Outline

Current state of the technology

  • Current applications
  • Potential future applications

Rules based AI

  • Simplifying decision processes

Machine Learning

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

Deep Learning

  • Essential terminology
  • Scenarios suitable or unsuitable for Deep Learning
  • Estimating computational resources and costs
  • Concise theoretical overview of Deep Neural Networks

Deep Learning in practice (mainly using TensorFlow)

  • Data preparation
  • Selecting the loss function
  • Selecting the appropriate neural network type
  • Trade-offs between accuracy, speed, and resources
  • Training the neural network
  • Assessing efficiency and error rates

Sample usage

  • Anomaly detection
  • Image recognition
  • ADAS

Requirements

Participants are expected to have a background in engineering and prior experience with programming (in any language). However, no actual coding is required during the course sessions.

Number of participants


Price per participant

Upcoming Courses

Related Categories