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

In this 13-video course, learners can explore how to work with machine learning frameworks and Python to implement training algorithms for neural networks. You will learn the concept and characteristics of perceptrons, a single layer neural network that aggregates the weighted sum of inputs, and returns either zero or one, and neural networks. You will then explore some of the prominent learning rules that to apply in neural networks, and the concept of supervised and unsupervised learning. Learn several types of neural network algorithms, and several training methods. Next, you will learn how to prepare and curate data by using Amazon SageMaker, and how to implement an artificial neural network training process using Python, and other prominent and essential learning algorithms to train neural networks. You will learn to use Python to train artificial neural networks, and how to use Backpropagation in Keras to implement multilayer perceptrons or neural networks. Finally, this course demonstrates how to implement regularization in multilayer perceptrons by using Keras.



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