Update Readme.md

2- Improving Deep Neural Networks/Readme.md

@@ -656,13 +656,13 @@ Implications of L2-regularization on:
   - Given `Z[l] = [z(1), ..., z(m)]`, i = 1 to m (for each input)
   - Compute `mean = 1/m * sum(z[i])`
   - Compute `variance = 1/m * sum((z[i] - mean)^2)`
-  - Then `Z_norm[i] = (z(i) - mean) / np.sqrt(variance + epsilon)` (add `epsilon` for numerical stability if variance = 0)
+  - Then `Z_norm[i] = (z[i] - mean) / np.sqrt(variance + epsilon)` (add `epsilon` for numerical stability if variance = 0)
     - Forcing the inputs to a distribution with zero mean and variance of 1.
   - Then `Z_tilde[i] = gamma * Z_norm[i] + beta`
     - To make inputs belong to other distribution (with other mean and variance).
     - gamma and beta are learnable parameters of the model.
     - Making the NN learn the distribution of the outputs.
-    - _Note:_ if `gamma = sqrt(variance + epsilon)` and `beta = mean` then `Z_tilde[i] = Z_norm[i]`
+    - _Note:_ if `gamma = sqrt(variance + epsilon)` and `beta = mean` then `Z_tilde[i] = z[i]`
 
 ### Fitting Batch Normalization into a neural network