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Which algorithm is used to train the neural network?
optimization algorithm
The procedure used to carry out the learning process in a neural network is called the optimization algorithm (or optimizer). There are many different optimization algorithms. All have different characteristics and performance in terms of memory requirements, processing speed, and numerical precision.
What is training an algorithm?
A step-by-step procedure for adjusting the connection weights of an artificial neural network. In supervised training, the desired (correct) output for each input vector of a training set is presented to the network, and many iterations through the training data may be required to adjust the weights.
What is the purpose of testing a neural network?
6. Testing analysis. The purpose of testing is to compare the outputs from the neural network against targets in an independent set (the testing instances).
What’s the name of the algorithm used to train a neural network?
The procedure used to carry out the learning process in a neural network is called the training algorithm. There are many different training algorithms, with different characteristics and performance.
Can a neural network be used to train data?
Now for this you can’t simply use data that you’ve already used for training — the neural network has learned to cope with that explicit case (seen in the training error graph).
What does it mean to optimize a neural network?
Many people may be using optimizers while training the neural network without knowing that the method is known as optimization. Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses.
What are the results of testing a neural network?
Typical results from this stage of verification can be seen above as the “test error” line, and notice how they always have a higher error rate than “training error” data. That shouldn’t be unexpected — the neural network trained on explicit data provided and re-verified in the “training error” data line.