Artificial Intelligence Foundations: Neural 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
  • Biological neural networks

    2m 33s
  • Artificial neural networks

    1m 52s
  • Single-layer perceptron

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

  • Multilayer perceptron

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

    2m 55s
  • Transfer and activation functions

    3m 41s
  • How neural networks learn

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

  • Convolutional neural networks (CNN)

    9m 16s
  • Recurrent neural networks (RNN)

    7m 55s
  • 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
  • Use case and determine evaluation metric

    5m 25s
  • Data checks and data preparation

    2m 48s
  • Data preprocessing

    2m 11s
  • Train the neural network using Keras

    6m 26s
  • Challenge: Build a neural network

    51s
  • Solution: Build a neural network

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

  • Overfitting and underfitting: Two common ANN problems

    4m 54s
  • Hyperparameters and neural networks

    3m 24s
  • How do you improve model performance?

    3m 56s
  • Regularization techniques to improve overfitting models

    7m 40s
  • Challenge: Manually tune hyperparameters

    45s
  • Solution: Manually tune hyperparameters

    2m 4s
• Conclusion Conclusion

  • Next steps

    1m 46s