Artificial Intelligence Foundations: Neural Networks

Learn the fundamental techniques and principles behind artificial neural networks.

Introduction

• Neural networks 101: Your path to AI brilliance
• What you should know
• How to use the challenge exercise files

1. What Are Neural Networks?

• Machine learning and neural networks
• Biological neural networks
• Artificial neural networks
• Single-layer perceptron

2. Key Components in Neural Network Architecture

• Multilayer perceptron
• Layers: Input, hidden, and output
• Transfer and activation functions
• How neural networks learn

3. Other Types of Neural Networks

• Convolutional neural networks (CNN)
• Recurrent neural networks (RNN)
• Transformer architecture

4. Build a Simple Neural Network Using Keras

• The Keras Sequential model
• Use case and determine evaluation metric
• Data checks and data preparation
• Data preprocessing
• Train the neural network using Keras
• Challenge: Build a neural network
• Solution: Build a neural network

5. Best Practices for Optimizing a Neural Network

• Overfitting and underfitting: Two common ANN problems
• Hyperparameters and neural networks
• How do you improve model performance?
• Regularization techniques to improve overfitting models
• Challenge: Manually tune hyperparameters
• Solution: Manually tune hyperparameters

Conclusion

• Next steps