NeRF introduces a novel way to represent 3D scenes using neural networks, enabling highly realistic image rendering from novel viewpoints. It outperforms traditional methods in rendering quality but requires significant computational resources.

## [Optimization] Variational Inference

Variational inference in a method to approximate the posterior $p(\mathbf{z}\vert\mathbf{x})$. This is a key technique for Variational AutoEncoder, one of the most famous generative model.

## [Optimization] MLE & MAP

One of the most important tasks in ML is to optimize parameters in a model. There are various approaches and I'll explain the most fundamental methods (MLE & MAP) in this post.

## Statistical Interpretation of Loss Function

Machine learning(ML) defines a loss function and optimizes its model to minimize the loss. Since ML is based on probability theory and statistics, it is reasonable to interpret the loss function from a statistical perspective.

## Probabilistic Models in Machine Learning

Machine learning(ML) is an approach to learn some pattern of data, and leverage it to predict properties of unseen data. The same statement is valid for statistics. This is because the essence of ML theory comes from statistics. In this post, I will explain how to introduce probabilistic models and statistics to ML problems.