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Wiki: DMelt:AI/1.Backpropagation.Neural.Net
[100%] (in title)
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Wiki: DMelt:AI/2.Bayesian.Neural.Net
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DMelt: org.encog.neural.NeuralNetworkError [100%] (Java API)
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DMelt: org.encog.neural.package-frame [100%] (Java API)
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DMelt: org.encog.neural.package-summary [100%] (Java API)
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DMelt: org.encog.neural.art.ART [87%] (Java API)
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DMelt: org.encog.neural.art.ART1 [87%] (Java API)
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DMelt: org.encog.neural.art.PersistART1 [87%] (Java API)
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DMelt: org.encog.neural.art.package-frame [87%] (Java API)
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DMelt: org.encog.neural.art.package-summary [87%] (Java API)
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DMelt: org.encog.neural.bam.BAM [87%] (Java API)
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DMelt: org.encog.neural.bam.PersistBAM [87%] (Java API)
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DMelt: org.encog.neural.bam.package-frame [87%] (Java API)
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DMelt: org.encog.neural.bam.package-summary [87%] (Java API)
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DMelt: org.encog.neural.cpn.CPN [87%] (Java API)
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DMelt: org.encog.neural.cpn.PersistCPN [87%] (Java API)
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DMelt: org.encog.neural.cpn.package-frame [87%] (Java API)
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DMelt: org.encog.neural.cpn.package-summary [87%] (Java API)
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DMelt: org.encog.neural.data.NeuralData [87%] (Java API)
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DMelt: org.encog.neural.data.NeuralDataPair [87%] (Java API)
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DMelt: org.encog.neural.data.NeuralDataSet [87%] (Java API)
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DMelt: org.encog.neural.data.package-summary [87%] (Java API)
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DMelt: org.encog.neural.error.ATanErrorFunction [87%] (Java API)
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DMelt: org.encog.neural.error.CrossEntropyErrorFunction [87%] (Java API)
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DMelt: org.encog.neural.error.ErrorFunction [87%] (Java API)
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DMelt: org.encog.neural.error.LinearErrorFunction [87%] (Java API)
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DMelt: org.encog.neural.error.OutputErrorFunction [87%] (Java API)
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DMelt: org.encog.neural.error.package-frame [87%] (Java API)
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DMelt: org.encog.neural.error.package-summary [87%] (Java API)
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DMelt: org.encog.neural.flat.FlatLayer [87%] (Java API)
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DMelt: org.encog.neural.flat.FlatNetwork [87%] (Java API)
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DMelt: org.encog.neural.flat.FlatNetworkRBF [87%] (Java API)
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DMelt: org.encog.neural.flat.package-frame [87%] (Java API)
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DMelt: org.encog.neural.flat.package-summary [87%] (Java API)
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DMelt: org.encog.neural.freeform.FreeformConnection [87%] (Java API)
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DMelt: org.encog.neural.freeform.FreeformContextNeuron [87%] (Java API)
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DMelt: org.encog.neural.freeform.FreeformLayer [87%] (Java API)
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DMelt: org.encog.neural.freeform.FreeformNetwork [87%] (Java API)
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DMelt: org.encog.neural.freeform.FreeformNetworkError [87%] (Java API)
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DMelt: org.encog.neural.freeform.FreeformNeuron [87%] (Java API)
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DMelt: org.encog.neural.freeform.InputSummation [87%] (Java API)
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DMelt: org.encog.neural.freeform.TempTrainingData [87%] (Java API)
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DMelt: org.encog.neural.freeform.package-frame [87%] (Java API)
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DMelt: org.encog.neural.freeform.package-summary [87%] (Java API)
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DMelt: org.encog.neural.hyperneat.FactorHyperNEATGenome [87%] (Java API)
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DMelt: org.encog.neural.hyperneat.HyperNEATCODEC [87%] (Java API)
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DMelt: org.encog.neural.hyperneat.HyperNEATGenome [87%] (Java API)
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DMelt: org.encog.neural.hyperneat.package-frame [87%] (Java API)
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DMelt: org.encog.neural.hyperneat.package-summary [87%] (Java API)
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DMelt: org.encog.neural.neat.FactorNEATGenome [87%] (Java API)
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DMelt: org.encog.neural.neat.NEATCODEC [87%] (Java API)
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DMelt: org.encog.neural.neat.NEATGenomeFactory [87%] (Java API)
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DMelt: org.encog.neural.neat.NEATLink [87%] (Java API)
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DMelt: org.encog.neural.neat.NEATNetwork [87%] (Java API)
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DMelt: org.encog.neural.neat.NEATNeuronType [87%] (Java API)
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DMelt: org.encog.neural.neat.NEATPopulation [87%] (Java API)
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DMelt: org.encog.neural.neat.NEATUtil [87%] (Java API)
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DMelt: org.encog.neural.neat.PersistNEATPopulation [87%] (Java API)
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DMelt: org.encog.neural.neat.package-frame [87%] (Java API)
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DMelt: org.encog.neural.neat.package-summary [87%] (Java API)
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DMelt: org.encog.neural.networks.BasicNetwork [87%] (Java API)
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DMelt: org.encog.neural.networks.ContainsFlat [87%] (Java API)
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DMelt: org.encog.neural.networks.NeuralDataMapping [87%] (Java API)
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DMelt: org.encog.neural.networks.PersistBasicNetwork [87%] (Java API)
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DMelt: org.encog.neural.networks.package-frame [87%] (Java API)
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DMelt: org.encog.neural.networks.package-summary [87%] (Java API)
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DMelt: org.encog.neural.pattern.ADALINEPattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.ART1Pattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.BAMPattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.BoltzmannPattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.CPNPattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.ElmanPattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.FeedForwardPattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.HopfieldPattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.JordanPattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.NeuralNetworkPattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.PNNPattern [87%] (Java API)
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DMelt: org.encog.neural.pattern.package-frame [87%] (Java API)
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DMelt: org.encog.neural.pattern.package-summary [87%] (Java API)
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DMelt: org.encog.neural.pnn.AbstractPNN [87%] (Java API)
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DMelt: org.encog.neural.pnn.BasicPNN [87%] (Java API)
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DMelt: org.encog.neural.pnn.PNNKernelType [87%] (Java API)
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DMelt: org.encog.neural.pnn.PNNOutputMode [87%] (Java API)
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DMelt: org.encog.neural.pnn.PersistBasicPNN [87%] (Java API)
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DMelt: org.encog.neural.pnn.package-frame [87%] (Java API)
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DMelt: org.encog.neural.pnn.package-summary [87%] (Java API)
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DMelt: org.encog.neural.prune.HiddenLayerParams [87%] (Java API)
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DMelt: org.encog.neural.prune.NetworkPattern [87%] (Java API)
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DMelt: org.encog.neural.prune.PruneIncremental [87%] (Java API)
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DMelt: org.encog.neural.prune.PruneSelective [87%] (Java API)
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DMelt: org.encog.neural.prune.package-frame [87%] (Java API)
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DMelt: org.encog.neural.prune.package-summary [87%] (Java API)
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DMelt: org.encog.neural.rbf.PersistRBFNetwork [87%] (Java API)
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DMelt: org.encog.neural.rbf.RBFNetwork [87%] (Java API)
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DMelt: org.encog.neural.rbf.package-frame [87%] (Java API)
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Java Example: neural_net_4.java [100%] Simple backpropagation neural net
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Jython Example: neural_net_2.py [92%] Backpropagation neural net (2): Create a neural network
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Jython Example: neural_net_encog3.py [81%] Backpropogation and predictions with Encog
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Jython Example: recurn_lstm1.py [81%] Recurrent Neural Network (LSTM)
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Jython Example: neural_net_hbayes.py [81%] Bayesian networks as GUI
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Jython Example: neural_net_workbench.py [81%] making a workbench
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Jython Example: neural_net_symbols.py [79%] Backpropagation neural net using text as input and output
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Jython Example: neural_net_all.py [79%] Backpropagation neural net (all steps). Create data and train.
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Jython Example: neural_net_analysis.py [79%] Backpropogation neural network training/analysis
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Jython Example: neural_net_bpnn.py [79%] Back-Propagation Neural Network implemented in Python
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Jython Example: neural_net_3.py [79%] Backpropagation neural net (3): Read data and train it
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Jython Example: neural_net_1.py [79%] Backpropagation neural net (1): Create input data
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Jython Example: neural_net_smile.py [79%] Complex neural net with many layers using Smile
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Java Example: neural_net_bayesian_k2.java [65%] Bayesian network using Encog Java and simpel logic
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Jython Example: neural_net_MLP.py [65%] Multi-Layer Perceptron for the XOR problem using Neuroph (Jython)
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Jython Example: neural_net_bsom1.py [65%] Build a bayesian Self-Organizing Map. Example I
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Java Example: neural_net_encog1.java [65%] Read a CSV file and do beckpropogation with encog
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Jython Example: neural_net_kohonen_map2D.py [65%] Kohonen Feature Map in 2D (SOM)
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Jython Example: neural_net_encog2.py [65%] Read a CSV file and do backpropogation with Encog, and then analyse it
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Jython Example: neural_net_som.py [65%] Self-Organizing Maps in Python
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Jython Example: neural_net_bayesian.py [65%] Bayesian networks using Encog Java
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Java Example: neural_net_MLP.java [65%] Multi-Layer Perceptron for the XOR problem using Neuroph (Java)
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Jython Example: joone_wine2.py [65%] Joone. Forecast wine production using saved trained Neural Net (II).
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Java Example: joone_XOR.java [65%] Neural network using Joone for solving XOR problem
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Java Example: joone_XORMemory.java [65%] Demonstrate Neural Network using Joone with InMemory arrays
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Java Example: joone_TimeSeries.java [65%] Neural Network with Joone for TimeSeries forecasting
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Jython Example: joone_XORMemory1.py [65%] Building a neural net in memory for XOR problem using Joone
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Jython Example: joone_XORMemory2.py [65%] Training and veryfing a neural net using Joone
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Jython Example: joone_timeseries.py [65%] Forecasting time series using Joone neural network
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Java Example: neural_net_bayesian.java [65%] Bayesian networks using Encog Java
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Jython Example: neural_net_bayesian_k2.py [65%] Bayesian networks using Encog Java and simple logic
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Java Example: neural_net_MLP_RT.java [65%] Multi Layer Perceptron network using Resilient Propagation
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Jython Example: neural_net_kohonen_mapND.py [65%] Kohonen Feature Map in N dimension (SOM)
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Jython Example: classify_neural_net_plot.py [65%] Classify data with neural network with many input layers
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Jython Example: neural_net_kohonen_map3D.py [65%] Kohonen Feature Map in 3D (SOM)
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Jython Example: javacnn1.py [65%] Convolutional neural network for 28x28 px and its prediction
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Jython Example: neural_net_jayes1.py [65%] Bayesian network using jayes Java library
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Jython Example: neural_net_jayes3.py [65%] Bayesian network using using the Jayes library
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Jython Example: neural_net_kohenmap1.py [65%] Example of Kohonen Feature Map (selforganizing Map, SOM)
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Jython Example: neural_net_bsom2.py [65%] Build a bayesian Self-Organizing Map. Example II
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Jython Example: neural_net_encog1.py [65%] Read a CSV file and do backpropogation with Encog
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Jython Example: neural_net_jayes2.py [65%] Bayesian network using using the Jayes library
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Jython Example: bayes_rain.py [57%] Bayesian network for rain predictions
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Jython Example: joone_wine1.py [46%] Joone. Training NeuralNet using complex input data on wine production (I).
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Jython Example: recurn_lstm2.py [46%] Long Short-Term Memory Networks (LSTM) for text
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Jython Example: recurn_lstm3.py [46%] Long Short-Term Memory Networks (LSTM) example
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Jython Example: classify_neuralnet.py [46%] Classification using Multilayer Perceptron Neural Network
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Jython Example: javacnn2_faces.py [46%] Identify images with faces using convolutional NN (CNN)
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Jython Example: javacnn2.py [46%] Convolutional NN (CNN) for MNIST database of handwritten digits
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Jython Example: javacnn2_many.py [46%] Identify 8 types of PGM images using convolutional NN (CNN)
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