Description
This video tutorial is the second part of the complete course of deep learning in MATLAB. We recommend you to order the complete course to learn completely.
Headlines:
Neural network programming in MATLAB software
Preprocessing and post-processing
(Mapminamx, Mapstd, Processpca, Fixunknowns, and removeconstraints)
Normalize input data
Correction of unmissed data unmissed values
Neural Network App
(nftool, nctool, nprtool and ntstool)
Multilayer Shallow Neural Networks
Neural network commands in MATLAB
(feedforwardnet, cascadeforwardnet, fitnet, patternnet)
Neural network for fitting application
Neural network for pattern recognition application
Neural Network Object Properties
(net.biasConnect, net.inputConnect, net.layerConnect, net.outputConnect , net.biases, net.inputWeights, net.layerWeights , net.outputs, net.IW , net.LW, net.b)
Weight and Bias Values
Neural Network Subobject Properties
( Inputs, Layers, Outputs, Biases, Input Weights, Layer Weights)
Adjust the number of neurons in the hidden layer
Set the neural network training function
Neural network modeling
Select the training function
Which training function should we choose to train the neural network?
What is the difference between trainlm, trainbr and trainscg? Which one is better?
Familiarity with training functions
(traingd, traingdm, traingda, traingdx, trainrp, traincgf, traincgp, traincgb, trainscg, trainbfg, trainoss, trainlm, and trainbr)
What is the most used neural network training function?
Is rapid convergence good?
Neural Networks with Parallel and GPU Computing
Autoencoder class
Learning vector quantization neural network
Clustering
Self-organizing map
Competitive Neural Network
Kohonen Learning Rule (learnk)
Bias Learning Rule (learncon)
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