Add to this list all the types of Neural Networks and Computer Science terms.

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For the Computer Science Terms at the bottom of this post, you may open Google Chrome Browser's/other Browser's "Find Text" tool, or just eye-scan it.

1   Feedforward neural network
2   Radial basis function (RBF) network
2.1   General descripton of RBF networks
2.2   How RBF networks work
2.2.1   Radial Basis Function
2.2.2   RBF Network Architecture
2.2.3   Training RBF Networks
2.2.4   The RBF Network Property Page
3   Kohonen self-organizing network
4   Learning vector quantization
5   Recurrent neural network
5.1   Fully recurrent network
5.1.1   Hopfield network
5.1.2   Boltzmann machine
5.2   Simple recurrent networks
5.3   Echo state network
5.4   Long short-term memory network
5.5   Bi-directional RNN
5.6   Hierarchical RNN
5.7   Stochastic neural networks
6   Modular neural networks
6.1   Committee of machines
6.2   Associative neural network (ASNN)
7   Physical neural network
8   Other types of networks
8.1   Holographic associative memory
8.2   Instantaneously trained networks
8.3   Spiking neural networks
8.4   Dynamic neural networks
8.5   Cascading neural networks
8.6   Neuro-fuzzy networks
8.7   Compositional pattern-producing networks
8.8   One-shot associative memory
8.9   Hierarchical temporal memory

weights state liklihood metaheuristics estimate predict statistics graph tables datasheets labels training data test data generalizes decision decision trees classifier decisions leaves hyperparameters model performance error loss function nearest-neighbor hierarchical classed Perceptron multi-layer networks hidden linear greedy algorithms Hierarchial databases. Data mining/prediction. Convergence probabilistically reconstruct inputs. Linear Regression Analysis estimate relationships among independent and dependent variables Logistic Regression Analysis, Decision Tree Categorization, economics. Expert Agents. Random Decision Forests overfit hyperplanes feature dimensions accuracy overtraining. Recursion and traversals. Covariance variances. Support Vector Machines. Dimensionality reduction, optimal image and signal processing, hierarchical clustering, metric embeddings, sublinear algorithms, convex programming, computational geometry approximation algorithm, kernals, combinatorics, symbolic computation, stochastic models, queues, discrete and computational geometry, enumeration algorithms, approximation algorithms, automatic differentiation, hybrid dynamic systems, data management, data quality, data analytics, information extraction, create, copy, transfer, save, modify, erase, link, access, sensory input, motory output



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To anyone that wondered where he had the types of neural networks;
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So many names and types… all based on the same basic principles but all exhibiting efficiency in a specific narrow problem space… if only there was a way too combine them all into one coherent schema. Lol.  ::)
It thunk... therefore it is!



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