Assertion: neural network's parameter | /r/HasProperty | weight
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Source sentences:
text: To do this we have a series of parameters (the weights and the bias) that we are updating using the backpropagation algorithm, which is based on the descent gradient.
text: As I mentioned above, these are known as hyper-parameters for our neural network, in order to distinguish them from the parameters (weights and biases) learnt by our learning algorithm.
text: Basically, what we need to do, we need to somehow explain the model to change the parameters (weights), such that error becomes minimum.
text: If the error is huge then, update the parameters (weights and biases).
text: In summary, we can consider backpropagation as a method to alter the parameters (weights and biases) of the neural network in the right direction.
text: In general, we can see the learning process as a global optimization problem where the parameters (weights and biases) must be adjusted in such a way that the loss function presented above is minimized.
text: We know that we have three parameters in a CNN model – weights, biases and filters.
text: During training, the network will try to minimize the log loss by adjusting the trainable parameters (weights).
text: These parameters (weights, bias) are exactly the numerical values ​​that we’ll try to adjust by training the network with an already labeled dataset, as in any other supervised Machine Learning problem.
text: The parameters (weights and biases) for layer l are represented as