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. This can be done using the backpropagation algorithm.
| Connection | Weight | Bias | | --- | --- | --- | | Input 1 -> Hidden 1 | 0.5 | 0.2 | | Input 1 -> Hidden 2 | 0.3 | 0.1 | | Input 2 -> Hidden 1 | 0.2 | 0.4 | | Input 2 -> Hidden 2 | 0.6 | 0.3 | | Hidden 1 -> Output | 0.8 | 0.5 | | Hidden 2 -> Output | 0.4 | 0.6 | build neural network with ms excel full
Calculate the gradients of the error with respect to each weight and bias: To train the neural network, we need to
Weight_Input1_Hidden1 = Weight_Input1_Hidden1 - Learning Rate * dE/dWeight_Input1_Hidden1 While MS Excel is not the most efficient
Building a neural network with MS Excel is a feasible and educational project that can help beginners understand the basics of neural networks. While MS Excel is not the most efficient tool for large-scale neural network training, it can be used for rapid prototyping and testing of neural network architectures.
| Input 1 | Input 2 | Output | | --- | --- | --- | | 0 | 0 | 0 | | 0 | 1 | 1 | | 1 | 0 | 1 | | 1 | 1 | 0 |
In this article, we built a simple neural network with one hidden layer to predict the output of an XOR function. We initialized the weights and biases, calculated the outputs of the hidden layer neurons, and trained the neural network using backpropagation.