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As the technology emerges from the state where the machine did not even get the words you were uttering and responded in a choppy robotic way to the open space of the creativity unimaginable ever before some basic code snippets never age.

Taxonomy

Supervised Learning

Artificial Neural networks ( ANN ) Multilayer Perceptrons (MLPs) Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Long short-term memory (LSTM)

Unsupervised Learning

Self-Organizing Maps (SOM) Restricted Boltzman Machines (RBM) Deep Boltzmann Machines (DBM) Deep Believe Networks (DBN) Auto-Encoders (AE) Sparse Auto-Encoder (SAE) Stacked Auto-Encoder (SAE) Stacked Sparse Auto-Encoder (SSAE) Variational Autoencoder (VAE)

Application

Supervised Learning ( Discriminative ) Typical discriminative models include logistic regression (LR), support vector machines (SVM), conditional random fields (CRFs) (specified over an undirected graph), decision trees, neural networks.

Artificial Neural networks ( ANN ) Used mainly for Regression and Classification

Multilayer Perceptrons (MLPs)

Convolutional Neural Networks (CNNs) Used Mainly for Machine Vision

Recurrent Neural Networks (RNNs) Used mainly for Time Series Analysis

Long short-term memory (LSTM)

Unsupervised Learning ( Generative ) Typical generative model approaches include naive Bayes classifiers, Gaussian mixture models, variational autoencoders.

Self-Organizing Maps (SOM) Used mainly for Feature Detection.

Restricted Boltzman Machines (RBM) Used mainly for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling.

Deep Boltzmann Machines (DBM) Used Mainly for Recommendation Systems.

Deep Believe Networks (DBN) Difference between Deep Belief and Deep Boltzman is that Deep Boltzman is bidirectional on every level.

Auto-Encoders (AE) Used Mainly for Recommendation Systems. https://medium.com/@venkatakrishna.jonnalagadda/sparse-stacked-and-variational-autoencoder-efe5bfe73b64