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 and multilayer, and the roles of linear and nonlinear functions in this 10-video course. Learners will explore how to implement perceptrons and perceptron classifiers by using Python for machine learning solutions. Key concepts covered in this course include perceptrons, single-layer and multilayer perceptrons, and the computational role they play in artificial neural networks; learning the algorithms that can be used to implement single-layer perceptron training models; and exploring multilayer perceptrons and illustrating the algorithmic difference from single-layer perceptrons. Next, you will learn to classify the role of linear and nonlinear functions in perceptrons; learn how to implement perceptrons by using Python; and learn approaches and benefits of using the backpropagation algorithm in neural networks. Then learn the uses of linear and nonlinear activation functions in artificial neural networks; learn to implement a simple perceptron classifier using Python; and learn the benefits of using the backpropagation algorithm in neural networks and implement perceptrons and perceptron classifiers by 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