Use the =RAND() function to assign small random numbers to the weights connecting each layer.

Current MSE | Cell F3: =AVERAGE(U2:U5) (We will build column U later) 3. Initializing Weights and Biases

Watch your Loss cell drop immediately. Repeat this process to watch the network converge. Method B: Data Table Automation

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

Pass the output dot product through the Sigmoid function one last time. =1/(1+EXP(-Q2)) Step 5: Calculate Error and Loss Cell S2 (Error): =R2-C2 (Predicted minus Target) Cell T2 (Squared Error): =S2^2 Drag all formulas from row 2 down through row 5. 5. The Backward Pass (Backpropagation)