Machine learning is a very broad field of research. One regularly deals with new topics, which are sometimes difficult to understand. On this page I collect links to resources that helped me to get started and are well explained. Topics are sorted alphabetically. If applicable, I sorted links in ascending order of difficulty.

Batch Normalization
Batch Normalization Explained

Contrastive Learning
The Illustrated SimCLR Framework

Implicit Layers
Deep Implicit Layers

Multiple Instance Learning
Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Neural Architecture Search
Neural Architecture Search

Spatial Transformer Networks
Deep Learning Paper Implementations: Spatial Transformer Networks - Part I
Deep Learning Paper Implementations: Spatial Transformer Networks - Part II

Transformer
The Illustrated Transformer
Transformer Architecture: The Positional Encoding

Variational Inference
A Beginner’s Guide to Variational Methods: Mean-Field Approximation
Variational inference
Variational Inference: A Review for Statisticians