Updated answer no. 7 with a more relevant answer (andrewekhalel#13)

kuutsav · andrewekhalel · web-flow · commit 82572fec4e44 · 2023-06-17T16:53:15.000+02:00
* Updated answer no. 7 with a more relevant answer

* Reverted a line deletion

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Co-authored-by: Andrew Khalel &lt;andrew_emel@hotmail.com&gt;
diff --git a/README.md b/README.md
@@ -55,14 +55,15 @@ Principal Component Analysis (PCA) is a dimensionality reduction technique used
 * Computation Efficiency:
   As ReLU is a simple threshold the forward and backward path will be faster.
 * Reduced Likelihood of Vanishing Gradient:
-  Gradient of ReLU is 1 for positive values and 0 for negative values.
+  Gradient of ReLU is 1 for positive values and 0 for negative values while Sigmoid activation saturates (gradients close to 0) quickly with slightly higher or lower inputs leading to vanishing gradients.
 * Sparsity:
   Sparsity happens when the input of ReLU is negative. This means fewer neurons are firing ( sparse activation ) and the network is lighter. 
 
 
 [[src1]](https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0) [[src2]](https://stats.stackexchange.com/questions/126238/what-are-the-advantages-of-relu-over-sigmoid-function-in-deep-neural-networks)
 
 
+
 #### 8) Given stride S and kernel sizes  for each layer of a (1-dimensional) CNN, create a function to compute the [receptive field](https://www.quora.com/What-is-a-receptive-field-in-a-convolutional-neural-network) of a particular node in the network. This is just finding how many input nodes actually connect through to a neuron in a CNN. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
 The receptive field are defined portion of space within an inputs that will be used during an operation to generate an output.
