Build Neural Network With Ms Excel Full Review

We will build formulas for the first training row (X1=0, X2=0, Target=0) in columns F through K.

For Row 20 (representing XOR training case 1):

  • Hidden Layer Activation (Sigmoid): Formula: =1/(1+EXP(-Z))
  • Output Layer Input (Z2):
  • Output Prediction:

    • Copy this block of formulas down for rows 21, 22, 23 (the other 3 training cases).

    To build a "full" network in Excel, you typically build a Multi-Layer Perceptron (MLP). The features of this structure include:

  • Output Layer (The Prediction): The final column(s) that represent the network's guess (e.g., Probability of Class A vs. Class B).

    • 🧠 You don’t need Python to build a Neural Network.

    You can build one in Microsoft Excel.

    Forward prop ✅
    Backprop ✅
    Gradient descent ✅ build neural network with ms excel full

    Excel forces you to understand the math behind deep learning. No hidden layers (pun intended).

    Want the full tutorial + free template?
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    #Excel #NeuralNetwork #DataScience

    We will build a 2-2-1 network:

    The hard part wasn't making it think; it was making it learn. A neural network learns through "Backpropagation." It looks at the error (Target - Output) and calculates how much each weight contributed to that mistake. It involves calculus—derivatives and the "Chain Rule."

    Arthur’s forehead beaded with sweat. He created a row for "Target" in Column D. We will build formulas for the first training

    Now, he had to build the math to adjust the weights. He needed a section for Gradients.

    He calculated the error in cell M2: =D2 - K2.

    Then, the derivative of the Sigmoid function: =K2*(1-K2). And the Delta for the output: =M2 * (K2*(1-K2)).

    This was where Excel became a labyrinth. He had to chain these errors backward from the output layer to the hidden layer. He wrote formulas that referenced the output weights, the deltas, and the hidden activations. The formula bars grew long, a chaotic string of cell references like $F$2 and H2.

    Finally, he built the Weight Updates. He created a "New Weights" section. The formula was: Old Weight + (Learning Rate * Delta * Input).

    He set the Learning Rate (a global variable in cell $Z$1) to 0.1. Hidden Layer Activation (Sigmoid): Formula: =1/(1+EXP(-Z))

    Arthur opened a blank workbook. He wasn't just typing numbers; he was architecting a digital brain.

    He started with the Inputs. He needed a problem simple enough to prove the concept but complex enough to require a brain. He chose the classic "XOR problem"—a logic gate where the output is true only if the inputs are different (0 and 1, or 1 and 0). A simple linear model couldn't solve this; it required a hidden layer. It required "deep" learning.

    He designated Columns A and B as his Input Neurons.

    Next came the Hidden Layer, the brain within the brain. Arthur decided on two hidden neurons. This meant Weights. Weights are the dials the network turns to learn.

    He created a section to the right, labeling cells F2 and G2 as "Weights Layer 1." He initialized them with random numbers using the formula =RAND().

    Then, he needed a Bias—a threshold for firing. He allocated cells F4 and G4.