It is wasteful to store zeros elements in a sparse matrix, especially for incrementally data. When constructing tf-idf and bag-of-words features or saving graph ajacent matrix, non-efficient sparse matrix storage might lead to the memory error. To circumvent this problems, efficient sparse matrix storage is a choice.

Notes of clustering approaches.

Graph Neural Networks (GNNs) has demonstrated efficacy on non-Euclidean data, such as social media, bioinformatics, etc.

Image source: [1]

GANs are widely applied to estimate generative models without any explicit density function, which instead take the game-theoretic approach: learn to generate from training distribution via 2-player games.

This is a concise introduction of Variational Autoencoder (VAE).

When we check and filter out the duplicates for a web crawler, bloom filter is a good choice to curtail the memory cost. Here is a brief introduction.

Flow models are used to learn continuous data.

The brain has about 1014 synapses and we only live for about 109 seconds. So we have a lot more parameters than data. This motivates the idea that we must do a lot of unsupervised learning since the perceptual input (including proprioception) is the only place we can get 105 dimensions of constraint per second.
(Geoffrey Hinton)

Techniques of NN training. Keep updating.

This is an introduction of recent BERT families.