Course details

Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms

Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Discover the basics of perceptrons, including single layer, multilayer, and the roles of linear and non-linear functions. Explore how to implement perceptrons and perceptron classifiers using Python.



Expected Duration (hours)
0.8

Lesson Objectives

Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms

  • discover the key concepts covered in this course
  • describe perceptrons and the computational role they play in artificial neural networks
  • recognize the algorithms that can be used to implement single layer perceptron training models
  • define multilayer perceptrons and illustrate the algorithmic difference from single layer perceptrons
  • classify the role of linear and non-linear functions in perceptrons
  • demonstrate the implementation of perceptrons using Python
  • describe approaches and benefits of using the backpropagation algorithm in neural networks
  • recognize the uses of linear and non-linear activation functions in artificial neural networks
  • implement a simple perceptron classifier using Python
  • recall the benefits of using the backpropagation algorithm in neural networks, and implement perceptrons and perceptron classifiers using Python
  • Course Number:
    it_mlfdnndj_02_enus

    Expertise Level
    Intermediate