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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.
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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