Create formulas in Excel to calculate these outputs. Calculate the output of the output layer using the sigmoid function and the outputs of the hidden layer neurons:

For simplicity, let's assume the weights and bias for the output layer are:

output = 1 / (1 + exp(-(weight1 * neuron1_output + weight2 * neuron2_output + bias)))

output = 1 / (1 + exp(-(0.5 * input1 + 0.2 * input2 + 0.1)))

Microsoft Excel is a widely used spreadsheet software that can be used for various tasks, including data analysis and visualization. While it's not a traditional choice for building neural networks, Excel can be used to create a simple neural network using its built-in functions and tools. In this article, we'll explore how to build a basic neural network using Microsoft Excel.

| Input 1 | Input 2 | Output | | --- | --- | --- | | 0 | 0 | 0 | | 0 | 1 | 1 | | 1 | 0 | 1 | | 1 | 1 | 0 | Create a new table with the following structure:

You can download an example Excel file that demonstrates a simple neural network using the XOR gate example: [insert link]

| | Neuron 1 | Neuron 2 | Output | | --- | --- | --- | --- | | Input 1 | | | | | Input 2 | | | | | Bias | | | |