output = 1 / (1 + exp(-(0.5 * input1 + 0.2 * input2 + 0.1)))
| | Output | | --- | --- | | Neuron 1 | 0.7 | | Neuron 2 | 0.3 | | Bias | 0.2 | build neural network with ms excel new
| | Neuron 1 | Neuron 2 | Output | | --- | --- | --- | --- | | Input 1 | 0.5 | 0.3 | | | Input 2 | 0.2 | 0.6 | | | Bias | 0.1 | 0.4 | | Calculate the output of each neuron in the hidden layer using the sigmoid function: output = 1 / (1 + exp(-(0
Create a formula in Excel to calculate the output. To train the neural network, we need to adjust the weights and biases to minimize the error between the predicted output and the actual output. We can use the Solver tool in Excel to perform this optimization. For simplicity, let's assume the weights and bias
For simplicity, let's assume the weights and bias for the output layer are:
output = 1 / (1 + exp(-(weight1 * input1 + weight2 * input2 + bias)))