Course details

Training Neural Networks: Implementing the Learning Process

Training Neural Networks: Implementing the Learning Process


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Discover how to use frameworks and Python to implement training algorithms for neural networks, as well as how to implement classification, regularization and backpropagation algorithms.



Expected Duration (hours)
1.7

Lesson Objectives

Training Neural Networks: Implementing the Learning Process

  • identify the subject areas covered in this course
  • describe the characteristics of perceptrons and neural networks
  • recognize the essential components of perceptrons and perceptron learning algorithms
  • identify the different types of learning rules that can be applied in neural networks
  • compare the supervised and unsupervised learning methods of artificial neural networks
  • list neural network algorithms that can be used to solve complex problems across domains
  • prepare and curate data for neural network training implementation
  • implement the artificial neural network training process using Python
  • recall the algorithms that can be used to train neural networks
  • implement backpropagation using Python to train artificial neural networks
  • use backpropagation and Keras to implement multi-layer perceptron or neural net
  • implement regularization in multilayer perceptron using Keras
  • compare the supervised and unsupervised learning methods, recall algorithms that can be used to train neural networks, and implement backpropagation using Python to train ANN
  • Course Number:
    it_mltnnndj_01_enus

    Expertise Level
    Intermediate