Introduction

• Welcome
• What you should know
• Using the exercise files

1. Introduction to 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 Implementation in Keras

• Understanding the components in Keras
• Setting up a Microsoft account on Azure
• Introduction to MNIST
• Preprocessing the training data
• Preprocessing the test data
• Building the Keras model
• Compiling the neural network model
• Training the neural network model
• Accuracy and evaluation of the neural network model

4. Convolutional Neural Networks

• Convolutions
• Zero padding and pooling

5. Convolutional Neural Networks in Keras

• Preprocessing and loading of data
• Creating and compiling the model
• Training and evaluating the model

6. Enhancements to Convolutional Neural Networks (CNNs)

• Enhancements to CNNs
• Image augmentation in Keras

7. ImageNet

• ImageNet challenge
• Working with VGG16

Conclusion

• Next steps