Neural Networks and Convolutional Neural Networks Essential Training

Introduction

• Explore neural networks

1. Getting to Know Neural Networks

• Neurons and artificial neurons
• Gradient descent
• The XOR challenge and solution
• Neural networks

2. Components of Neural Networks

• Activation functions
• Backpropagation and hyperparameters
• Neural network visualization

3. Neural Network Building Blocks

• Introduction to FashionMNIST
• Analyzing the dataset
• Defining the neural network
• Challenge: How many parameters?
• Solution: How many parameters?
• Loss functions
• Visualizing the loss
• Optimizers

4. Training a Neural Network

• Before you train a neural network model
• Training the neural network model
• Testing the neural network model

5. Convolutional Neural Networks (CNNs)

• Convolutional neural networks (CNNs)
• Zero padding and pooling
• Implementing CNNs in PyTorch
• The CIFAR-10 dataset and neural networks
• CIFAR-10 with CNNs
• LLMs and CNNs

Conclusion

• Next steps