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Learning,Training,Adapting
Mohammad Hossein Modirrousta
InWord
1
Train
Specific programs
Specific objectives
Short-term process
2
Learn
Long-term process
A series of self-directed
activities
More credibility
I learn every day on the job, yet I am not taking formal training courses.
1
Learning
Min
Gradient Descent Mini-batch Gradient Descent Stochastic Gradient Descent
Learning
2
Training
Backpropagation
Training
. . . . . . . .
e e d e d b d c e d b e d c
a d a d b a c a d b a d c a
              
= = + = + 
              
Training a Neural Network means finding the appropriate Weights of the Neural
Connections thanks to a feedback loop called Gradient Backward propagation
Backpropagation is the process of finding the gradients of the weight parameters of a NN
using chain rule, starting with the last layer, and moving backwards.
Training
3
Adapting
Structural Adaptation: Finding optimal architecture is finding how many layers will be necessary for the neural networks to operate accurately
Functional Adaptation: It keeps on adapting and changing the slope of activation functions of learning models until it reaches the optimal
slope
Parameter Adaptation: If a neural network is adaptable to parameters, then the weights of the network can be changed while training
Adapting
Best learning rate ?
SEPA algorithm, cascading algorithm, constructive algorithm
Gradient Descent, Adagrad, Adam
4
Sample
InMatlab
1
lookforTrain
Gradient descent backpropagation (traingd)
Gradient descent with momentum (traingdm)
Levenberg-Marquardt backpropagation (trainlm)
Random order weight/bias training (trainr)
…
2
lookforLearn
Hebb weight learning rule (learnh)
Kohonen weight learning function (learnk)
Gradient descent weight/bias learning function (learngd)
Learning vector quantization (LVQ) neural network
(lvqnet)
...
InMatlab
Gradient descent backpropagation Gradient descent with adaptive learning rate backpropagation
(lookforAdapt)
Chemicaldataset from Matlab
Lr = 0.01

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Learning,Training,Adapting

  • 2. InWord 1 Train Specific programs Specific objectives Short-term process 2 Learn Long-term process A series of self-directed activities More credibility I learn every day on the job, yet I am not taking formal training courses.
  • 4. Min Gradient Descent Mini-batch Gradient Descent Stochastic Gradient Descent Learning
  • 6. Backpropagation Training . . . . . . . . e e d e d b d c e d b e d c a d a d b a c a d b a d c a                = = + = +                
  • 7. Training a Neural Network means finding the appropriate Weights of the Neural Connections thanks to a feedback loop called Gradient Backward propagation Backpropagation is the process of finding the gradients of the weight parameters of a NN using chain rule, starting with the last layer, and moving backwards. Training
  • 9. Structural Adaptation: Finding optimal architecture is finding how many layers will be necessary for the neural networks to operate accurately Functional Adaptation: It keeps on adapting and changing the slope of activation functions of learning models until it reaches the optimal slope Parameter Adaptation: If a neural network is adaptable to parameters, then the weights of the network can be changed while training Adapting Best learning rate ? SEPA algorithm, cascading algorithm, constructive algorithm Gradient Descent, Adagrad, Adam
  • 11. InMatlab 1 lookforTrain Gradient descent backpropagation (traingd) Gradient descent with momentum (traingdm) Levenberg-Marquardt backpropagation (trainlm) Random order weight/bias training (trainr) … 2 lookforLearn Hebb weight learning rule (learnh) Kohonen weight learning function (learnk) Gradient descent weight/bias learning function (learngd) Learning vector quantization (LVQ) neural network (lvqnet) ...
  • 12. InMatlab Gradient descent backpropagation Gradient descent with adaptive learning rate backpropagation (lookforAdapt) Chemicaldataset from Matlab Lr = 0.01