ML AI Working Page

:::::::::::::::::::::::::::::::::::META::::::::::::::::::::::::::::::::::

What should go in this area?

This is a glossary of common ML/AI terms and strategies. If you are knowledgable about a particular ML/AI topic and want to see how it is applied/implemented in/relates to CLA, it should be added below.

Basic terms should also be listed.

For each term:

  • There should be a very brief definition of the term. (The assumption is that readers will be familiar with these terms, however there can be several ways a given term is used so this is a great place to disambiguate)
  • Similar terms or strategies in CLA should be discussed.
    • Links to NuPIC documentation, or code that explore this idea further.
  • If this idea is NOT IMPLEMENTED in CLA. Note that explicitly.
  • If this idea is NOT APPLICABLE to CLA. Note that explicitly.

:::::::::::::::::::::::::::::::::::END - META:::::::::::::::::::::::::::

Dimension
TODO

Feature
TODO

Feature vector
TODO

Input Layer
TODO

Hidden Layer
TODO

Model
TODO

Output Layer
TODO

Semi-Supervised learning

Supervised Learning
TODO

Unit
TODO

Unsupervised Learning
TODO

Restricted Boltzmann Machines

Bayesian Nets

Long Short-Term Memory

Recurrent Neural Networks

Deep Learning

Back propagation

Maxout

Weights / Parameters

vs. Permanence

Dropout

Training

– Batch

– Mini batches

– Online

– Offline

– Epochs

Performance Metrics

– Error

– Accuracy

Sequence Mining

Adverse Event Prediction

Neuron Types

– Logistic

– Rectified

1 Like