Build — Neural Network With Ms Excel New

| Name | Refers to | Purpose | |---------------|---------------------------|--------------------------| | InputData | =Sheet1!$B$3:$C$6 | 4x2 matrix of inputs | | TargetData | =Sheet1!$D$3:$D$6 | 4x1 matrix of targets | | W1 | =Sheet1!$F$3:$G$4 | 2x2 weights (input→hidden) | | b1 | =Sheet1!$I$3:$I$4 | 2x1 biases (hidden) | | W2 | =Sheet1!$K$3:$K$4 | 2x1 weights (hidden→output) | | b2 | =Sheet1!$M$3 | 1x1 bias (output) |

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The most efficient way to build a neural network today is by using the Python in Excel feature, which provides native access to over 400 libraries like and NumPy . Setup : Enter =PY in a cell to activate the Python editor. Workflow : | Name | Refers to | Purpose |

: Instead of copying formulas down thousands of rows, a single formula can now "spill" an entire layer of calculations across the grid, making the architecture of a Multi-Layer Perceptron (MLP) much easier to manage. Python in Excel Workflow : : Instead of copying formulas down

| Layer | Excel Formula Logic | Purpose | | :--- | :--- | :--- | | | Raw cells (e.g., A2, B2 ) | Feature values | | Hidden | =SIGMOID(SUMPRODUCT(Inputs, Weights_H1) + Bias) | Non-linear feature extraction | | Output | =SIGMOID(SUMPRODUCT(Hidden, Weights_O) + Bias_O) | Final prediction | | Loss | =-(Y_True * LN(Y_Pred) + (1-Y_True) * LN(1-Y_Pred)) | Binary Cross-Entropy |

Name this range Predictions .