Course details

Training Neural Networks: Advanced Learning Algorithms

Training Neural Networks: Advanced Learning Algorithms


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore how to design advanced learning algorithms like training patterns, pattern association, Hebbian learning rule, and competitive learning.



Expected Duration (hours)
1.7

Lesson Objectives

Training Neural Networks: Advanced Learning Algorithms

  • identify the subject areas covered in this course
  • describe features of online and offline training methods in artificial neural network
  • describe the training patterns and teaching inputs that are used in artificial neural networks
  • describe the approach of managing training samples
  • implement overfitting and underfitting using baseline model
  • describe the regularization techniques used in deep neural network
  • train built models of neural networks using Python to implement prediction with high accuracy
  • list the prominent training algorithms that are used for pattern association
  • describe the architecture along with the algorithm associated with learning vector quantization
  • define the essential phases involved in implementing Hebbian learning
  • implement the Hebbian learning rule using Python
  • describe the steps involved in implementing competitive learning
  • list approaches of optimizing neural networks
  • debug neural networks
  • recall the training algorithms used for pattern association, list the steps of implementing competitive learning, and implement the Hebbian learning rule using Python
  • Course Number:
    it_mltnnndj_02_enus

    Expertise Level
    Intermediate