#ExcelHacks #LearnAI #NeuralNetwork #ExcelTips #NoCodeAI
Set up your training data in cells A1:C5 of your spreadsheet: X1cap X sub 1 X2cap X sub 2 2. Initializing Weights and Biases Neural networks learn by adjusting weights ( ) and biases (
"Bah," Arthur muttered, adjusting his glasses. "It’s just math. And what is Excel, if not the greatest canvas for math ever invented?"
He saved the file as NeuralNet_V1.xlsx .
δ3(1)delta sub 3 raised to the open paren 1 close paren power (Cell V2): =S2 * $H$7 * O2 * (1 - O2) Step 3: Weight and Bias Gradients (
σ(z)=11+e−zsigma open paren z close paren equals the fraction with numerator 1 and denominator 1 plus e raised to the negative z power end-fraction In Excel, this translates to: =1 / (1 + EXP(-z)) The derivative of Sigmoid, used during backpropagation, is:
This will be a simplified example, and the resulting neural network will not be as powerful as one built with specialized deep learning libraries like TensorFlow or PyTorch.
Comment “NEURAL” and I’ll send it to you. 🧠📊
#ExcelHacks #LearnAI #NeuralNetwork #ExcelTips #NoCodeAI
Set up your training data in cells A1:C5 of your spreadsheet: X1cap X sub 1 X2cap X sub 2 2. Initializing Weights and Biases Neural networks learn by adjusting weights ( ) and biases (
"Bah," Arthur muttered, adjusting his glasses. "It’s just math. And what is Excel, if not the greatest canvas for math ever invented?"
He saved the file as NeuralNet_V1.xlsx .
δ3(1)delta sub 3 raised to the open paren 1 close paren power (Cell V2): =S2 * $H$7 * O2 * (1 - O2) Step 3: Weight and Bias Gradients (
σ(z)=11+e−zsigma open paren z close paren equals the fraction with numerator 1 and denominator 1 plus e raised to the negative z power end-fraction In Excel, this translates to: =1 / (1 + EXP(-z)) The derivative of Sigmoid, used during backpropagation, is:
This will be a simplified example, and the resulting neural network will not be as powerful as one built with specialized deep learning libraries like TensorFlow or PyTorch.
Comment “NEURAL” and I’ll send it to you. 🧠📊