From 41639bf8ae2d6c6961936a8ca77cd5a753918dac Mon Sep 17 00:00:00 2001 From: Dan Rollo Date: Thu, 7 Dec 2023 16:55:36 -0500 Subject: [PATCH] call -> called --- lessons/3-NeuralNetworks/04-OwnFramework/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/lessons/3-NeuralNetworks/04-OwnFramework/README.md index 5afddc6a..f649500b 100644 --- a/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -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(0), b(0) * Repeat the following step many times: