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Dan Rollo 2023-12-07 16:55:36 -05:00
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lessons/3-NeuralNetworks/04-OwnFramework/README.md
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@ -29,7 +29,7 @@ In the definition of *f* above, *w* and *b* are called **parameters** θ=
## Gradient Descent Optimization
There is a well-known method of function optimization called **gradient descent**. The idea is that we can compute a derivative (in multi-dimensional case call **gradient**) of loss function with respect to parameters, and vary parameters in such a way that the error would decrease. This can be formalized as follows:
There is a well-known method of function optimization called **gradient descent**. The idea is that we can compute a derivative (in multi-dimensional case called **gradient**) of loss function with respect to parameters, and vary parameters in such a way that the error would decrease. This can be formalized as follows:
* Initialize parameters by some random values w<sup>(0)</sup>, b<sup>(0)</sup>
* Repeat the following step many times: