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Introduction

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.

The time has arrived to venture into improving the attention span of Amazon's Alexa, Google's Assistant, and Apple's Siri. Alexa services exploded into over seventy thousand of skills. Companies and governments use speech recognition and speech synthesis in telephony, in the browser or in in the field applications.

Now we're talking.

The text you are reading on this Wiki page right now is just a graphical representation of the soundtrack. Let's cut out this impediment. Typing skills just like a calligraphy a century ago would become obliviated. Not so quick. Writing provides a spectrum of services perhaps underestimated before. First, writing allows us to synchronize on pronunciation - or communicating despite different pronunciations ( ask Chinese). Second, writing provides an access to the treasury of historical records. At this time the writing is still a better form of data representation for conducting searches and for more advanced forms of information processing than soundtracks render.

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 modelling.

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