The Need for Multi-Layer Networks

Contents

• Introduction Introduction

  • Neural networks 101: Your path to AI brilliance

    53s
  • What you should know

    42s
  • How to use the challenge exercise files

    1m 37s
• 1. What Are Neural Networks? 1. What Are Neural Networks?

  • Machine learning and neural networks

    4m 55s
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    Biological neural networks

    2m 33s
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    Artificial neural networks

    1m 52s
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    Single-layer perceptron

    4m 12s
• 2. Key Components in Neural Network Architecture 2. Key Components in Neural Network Architecture

  • Multilayer perceptron

    4m 7s
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    Layers: Input, hidden, and output

    2m 55s
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    Transfer and activation functions

    3m 41s
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    How neural networks learn

    5m 39s
• 3. Other Types of Neural Networks 3. Other Types of Neural Networks

  • Convolutional neural networks (CNN)

    9m 16s
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    Recurrent neural networks (RNN)

    7m 55s
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    Transformer architecture

    4m 27s
• 4. Build a Simple Neural Network Using Keras 4. Build a Simple Neural Network Using Keras

  • The Keras Sequential model

    4m 20s
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    Use case and determine evaluation metric

    5m 25s
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    Data checks and data preparation

    2m 48s
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    Data preprocessing

    2m 11s
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    Train the neural network using Keras

    6m 26s
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    Challenge: Build a neural network

    51s
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    Solution: Build a neural network

    4m 4s
• 5. Best Practices for Optimizing a Neural Network 5. Best Practices for Optimizing a Neural Network

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    Overfitting and underfitting: Two common ANN problems

    4m 54s
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    Hyperparameters and neural networks

    3m 24s
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    How do you improve model performance?

    3m 56s
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    Regularization techniques to improve overfitting models

    7m 40s
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    Challenge: Manually tune hyperparameters

    45s
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    Solution: Manually tune hyperparameters

    2m 4s
• Conclusion Conclusion

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    Next steps

    1m 46s