It appears that notation for neural networks isn’t completely standardised so I’m just going to include some notes from a free e-book from Adventures in Machine learning by Dr Andy Thomas. It’s worth pointing out that some formats use [ ] square brackets instead of ( ) so it’s necessary to be aware of the notation description for anything being studied online… as if learning neural network programming wasn’t tough enough ðŸ™‚

In the above is a 3 layer network. Some of the weights notation is shown the diagram above.

### Notation of node weights

w_{11}^{(1)} is the weight in-between the first node of the first later, and the first node of the next later.

To make it clearer what this really means, take a look at the weight in-between the 3rd node of the first layer, and the 2nd node of the second layer.

Here we see w_{23}^{(1)}Â

The ‘w’ denotes weight. First look at the number in the brackets. This refers to the layer number of the first node. The subscript 3 is actually referring to the node number in the first layer, and the 2 preceding that 3 is referring to the node number in the layer that is the value in the brackets +1.

So if we saw

w_{46}^{(5)}

the above means the weight between node 6 in layer 5, and node 4 in layer 6 (5+1).

### Notation of bias

The notation for the bias can be simplified as

b_{i}^{(l)} where i is the node number in later l+1

In the example image above we have a bias in layer 1 that feeds into all the nodes of layer 2.

b_{3}^{(1)} would, therefore, signify the weight of the bias in-between the first layer and the third node in layer 2.

### Output notation

Is simplified as

h_{j}^{(l)} where j denotes the node number of layer l of the network.

So in the above, the output of node 3 in the second layer is denoted as

h_{3}^{(2)}