Primers • AI
Introduction
- Here’s a hand-picked selection of tutorials on AI fundamentals that cover the entire process of building neural nets to training them to evaluating results.
The neural net design process
- Xavier Initialization and Regularization
- L1 vs. L2 Norm
- Regularization
- Multiclass vs. Multilabel Classification
Hyperparameters
The training process
- Gradient Descent and Backprop
- Training Loss > Validation Loss?
- Splitting Datasets
- Double Descent
- Debugging deep learning projects