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.

Neural net architectures

• Generative Adversarial Networks (GANs)

The neural net design process

• Xavier Initialization and Regularization
• L1 vs. L2 Norm
• Regularization
• Multiclass vs. Multilabel Classification

Hyperparameters

• Hyperparameter Tuning
• Hyperparameter Logging
• TensorBoard

The training process

• Gradient Descent and Backprop
• Training Loss > Validation Loss?
• Splitting Datasets
• Double Descent
• Debugging Deep Learning Projects

MLOps/Production

• Concept Drift

Miscellaneous

• PyTorch vs. TensorFlow
• Transformers
• BERT
• BigBird
• Evaluation Metrics
• Chain Rule