Gradient descent is an optimization algorithm that minimizes error in neural networks by computing gradients (slopes) through backpropagation, allowing the network to learn from data and make accurate predictions on new, unseen data.
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Backpropagation Explained in 30 Seconds | Gradient Descent Explained (Part 2/3)Added:
Descent [music] refers to moving downward on a surface or slope, and gradient is just another term for [music] slope in math.
It's used for many different types of problems. For example, finding the [music] fastest shipping routes or minimizing costs in a large company.
In machine learning, [music] the same idea is used to minimize another type of cost.
How wrong a neural network's predictions [music] are based on a set of data.
After minimizing this error, the neural net is set to have learned the data, >> [music] >> and then it can be used for new data it hasn't seen before.
In this context, [music] you'll often hear the term backpropagation, which describes how the gradients or slopes are computed in a neural network.
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