This repo contains the explanation of how the neural network works and how it learn.
This notebook shows the process of training neural network.
- Neural networks introduction
- Forward propagation
- Cost functions
- Backward propagation
- Gradient checking
- Optimization (Gradient descent, Parameter update)
This notebook shows norms or some of the tricks that helps neural networks to learn faster and prevent overfitting.
- Parameters initialization
- Data pre-processing
- Activation functions
- Batch normalizations
- Regularizations
- Advance optimization