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.
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
Neural network commands in MATLAB
Neural network for fitting application
Neural network for pattern recognition application
(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
( 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
Learning vector quantization neural network
Competitive Neural Network
Kohonen Learning Rule (learnk)
Bias Learning Rule (learncon)