diff --git a/1- Neural Networks and Deep Learning/Readme.md b/1- Neural Networks and Deep Learning/Readme.md index f1a79282..f675e88d 100644 --- a/1- Neural Networks and Deep Learning/Readme.md +++ b/1- Neural Networks and Deep Learning/Readme.md @@ -227,13 +227,13 @@ Here are the course summary as its given on the course [link](https://www.course - Lets say we have these variables: ``` - X1 Feature + X1 Feature X2 Feature W1 Weight of the first feature. W2 Weight of the second feature. B Logistic Regression parameter. M Number of training examples - Y(i) Expected output of i + Y(i) Expected output of i ``` - So we have: @@ -246,7 +246,7 @@ Here are the course summary as its given on the course [link](https://www.course d(z) = d(l)/d(z) = a - y d(W1) = X1 * d(z) d(W2) = X2 * d(z) - d(B) = d(z) + d(B) = d(z) ``` - From the above we can conclude the logistic regression pseudo code: @@ -472,7 +472,7 @@ Here are the course summary as its given on the course [link](https://www.course - Derivation of Sigmoid activation function: ``` - g(z) = 1 / (1 + np.exp(-z)) + g(z) = 1 / (1 + np.exp(-z)) g'(z) = (1 / (1 + np.exp(-z))) * (1 - (1 / (1 + np.exp(-z)))) g'(z) = g(z) * (1 - g(z)) ```