Assertion: neural network | /r/HasProperty | trained
Facets:
value: usually
type: DEGREE
value: than conventional program
type: OTHER
value: something we’ll talk
type: MANNER
value: based on this data
type: OTHER
Source sentences:
text: Neural networks are trained and taught just like a child’s developing brain is trained.
text: When a neural net is being trained, all of its weights and thresholds are initially set to random values.
text: Neural networks are bad at generalizing: A neural network will perform accurately at a task it has been trained for, but very poorly at anything else, even if it’s similar to the original problem.
text: A neural network will perform accurately at a task it has been trained for, but very poorly at anything else, even if it’s similar to the original problem.
text: The neural network is then trained, based on this data, i.e., it adjusts the coefficients and bias until it most accurately determines what digit it is.
text: The binary data is then fed into a neural network that has been trained to make the association between the character image data and a numeric value that corresponds to the character.
text: The neural network must first be "trained" by having it process a large number of input patterns and showing it what output resulted from each input pattern.
text: Neural networks are bad at generalizing: A neural network will perform accurately at a task it has been trained for, but very poorly at anything else, even if it’s similar to the original problem.
text: A neural network will perform accurately at a task it has been trained for, but very poorly at anything else, even if it’s similar to the original problem.
text: Neural networks are much faster in prediction after it has been trained than conventional program.
text: Once the neural network is trained, it can perform complex tasks and a certain level of reasoning.
text: After the neural network has been trained – something we’ll talk about in more detail in a future chapter – we can predict the digit associated with unknown samples by running them through the same network and observing the output values.
text: x(:, ind2); t2 = t(:, ind2); Then, ten neural networks are trained.
text: If I have trained a neural network by showing it different versions of handwritten letters a, then the neural network will be able to recognise not only these letters that it has been trained upon but also similar but different letters.
text: This means that neural networks are usually trained by using iterative, gradient-based optimizers that merely drive the cost function to a very low value, rather than the linear equation solvers used to train linear regression models or the convex optimization algorithms with global convergence guarantees used to train logistic regression or SVMs.
text: A neural network can be trained and improved with each example, but the larger the neural network, the more examples it needs to perform well—often needing millions or billions of examples in the case of deep learning.
text: When a neural net is being trained, all of its weights and thresholds are initially set to random values.