Training Neural Networks: Implementing the Learning Process

Neural Networks    |    Intermediate
  • 13 videos | 1h 38m 53s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 4 users Rating 5.0 of 4 users (4)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 1m 29s
  • 5m 47s
    After completing this video, you will be able to describe the characteristics of perceptrons and neural networks. FREE ACCESS
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    3.  Perceptron Learning Algorithm
    5m 4s
    After completing this video, you will be able to recognize the essential components of perceptrons and perceptron learning algorithms. FREE ACCESS
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    4.  Learning Rules in Neural Networks
    7m 48s
    In this video, find out how to identify the different types of learning rules that can be applied to neural networks. FREE ACCESS
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    5.  Supervised and Unsupervised Learning
    8m 2s
    In this video, you will learn how to compare the supervised and unsupervised learning methods of artificial neural networks. FREE ACCESS
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    6.  Neural Network Algorithms
    6m 46s
    After completing this video, you will be able to list neural network algorithms that can solve complex problems across domains. FREE ACCESS
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    7.  Data Preparation For Neural Networks
    7m 10s
    In this video, find out how to prepare and curate data for neural network training. FREE ACCESS
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    8.  ANN Training Process in Python
    9m 19s
    In this video, you will learn how to train an artificial neural network using Python. FREE ACCESS
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    9.  Algorithms to Train Neural Networks
    9m 36s
    Upon completion of this video, you will be able to recall the algorithms that can be used to train neural networks. FREE ACCESS
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    10.  Backpropagation in Python
    10m 21s
    In this video, you will learn how to implement backpropagation using Python to train artificial neural networks. FREE ACCESS
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    11.  Classification Algorithm for Learning
    7m 10s
    Learn how to use backpropagation and Keras to implement a multi-layer perceptron or neural net. FREE ACCESS
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    12.  Regularization in Multilayer Perceptrons
    6m 4s
    Learn how to implement regularization in a multilayer perceptron using Keras. FREE ACCESS
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    13.  Exercise: Implement ANN Learning
    14m 17s
    During this video, you will learn how to compare the supervised and unsupervised learning methods, recall algorithms that can be used to train neural networks, and implement backpropagation using Python to train an artificial neural network. FREE ACCESS

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