I don’t understand the concept of the question asked. Can someone help with an explanation. Thanks
In neural networks, when implementing forward propagation, we calculate the output (also known as activation) for each neuron (or unit
) in a layer by multiplying the input vector by the weights and adding the bias for each neuron. The question asks where to place the weights (W
) for each neuron when constructing a neural network in NumPy. The weight matrix W
is a 2D array where each element represents the connection strength between input features and neurons in the current layer. It gives two options:
 Rows in W: This implies that each row in the weight matrix corresponds to a particular input feature and its connection to all neurons.
 Columns in W: This implies that each column in the weight matrix corresponds to a specific neuron and its weights for all input features.
See the figure:

The weight matrix
W
is defined as a 2x3 matrix. Each column ofW
corresponds to a single neuron: Neuron 1 has weights
[1, 2]
 Neuron 2 has weights
[3, 4]
 Neuron 3 has weights
[5, 6]
These weights determine how each neuron interacts with the input vector
a_in
(which is[2, 4]
).  Neuron 1 has weights

The forward pass involves multiplying the input vector with these weights for each neuron and adding the respective biases
b
to get the output activations.
In the matrix multiplication process used during forward propagation (np.dot(W, a_in)
in the code), each column of W
contains the weights corresponding to a particular neuron. When you perform matrix multiplication, you multiply each input vector (a_in
) by the weights of each neuron (which are in the columns of W
). This gives the activation for each neuron. This setup allows the matrix multiplication operation (np.dot(W, a_in)
) to calculate the output for each neuron effectively:
 Columns of W: Each column contains the weights for one neuron, i.e. the dot product
np.dot(W, a_in)
calculates the weighted sum for each neuron based on the input.  Bias: The biases are added to the weighted sum to produce the final activation.
 Activation function: An activation function (
g
) is applied to get the neuron’s outputa_out
.
@nadtriana
Thanks. I get it now