Training Neural Networks: Implementing the Learning Process

Neural Networks
  • 13 Videos | 1h 44m 23s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Course Overview
    1m 29s
    UP NEXT
  • Playable
    2. 
    Perceptrons and Neural Networks
    5m 47s
  • Locked
    3. 
    Perceptron Learning Algorithm
    5m 4s
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    4. 
    Learning Rules in Neural Networks
    7m 48s
  • Locked
    5. 
    Supervised and Unsupervised Learning
    8m 2s
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    6. 
    Neural Network Algorithms
    6m 46s
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    7. 
    Data Preparation For Neural Networks
    7m 10s
  • Locked
    8. 
    ANN Training Process in Python
    9m 19s
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    9. 
    Algorithms to Train Neural Networks
    9m 36s
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    10. 
    Backpropagation in Python
    10m 21s
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    11. 
    Classification Algorithm for Learning
    7m 10s
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    12. 
    Regularization in Multilayer Perceptrons
    6m 4s
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    13. 
    Exercise: Implement ANN Learning
    14m 17s

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