Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms

Neural Networks    |    Intermediate
  • 10 videos | 44m 8s
  • Includes Assessment
  • Earns a Badge
Rating 4.6 of 20 users Rating 4.6 of 20 users (20)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 1m 23s
  • 5m 42s
    Upon completion of this video, you will be able to describe perceptrons and the computational role they play in artificial neural networks. FREE ACCESS
  • Locked
    3.  Single Layer Perceptron Training Model
    4m 45s
    After completing this video, you will be able to recognize the algorithms that can be used to implement single-layer perceptron training models. FREE ACCESS
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    4.  Multilayer Perceptrons
    5m 9s
    In this video, you will learn how to define multilayer perceptrons and illustrate the algorithmic difference from single-layer perceptrons. FREE ACCESS
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    5.  Linear and Non-Linear Functions
    4m 52s
    In this video, you will learn how to classify linear and non-linear functions in perceptrons. FREE ACCESS
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    6.  Implement Perceptrons with Python
    4m 51s
    Learn about the implementation of perceptrons using Python. FREE ACCESS
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    7.  Backpropagation
    3m 52s
    After completing this video, you will be able to describe the approaches and benefits of using the backpropagation algorithm in neural networks. FREE ACCESS
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    8.  Activation Functions
    4m 18s
    Upon completion of this video, you will be able to recognize the uses of linear and non-linear activation functions in artificial neural networks. FREE ACCESS
  • Locked
    9.  Perceptron Classifier
    4m 45s
    In this video, you will learn how to implement a simple perceptron classifier using Python. FREE ACCESS
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    10.  Exercise: Implement Perceptrons
    4m 31s
    Upon completion of this video, you will be able to recall the benefits of using the backpropagation algorithm in neural networks, and implement perceptrons and perceptron classifiers using Python. FREE ACCESS

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