You don’t need Python, TensorFlow, or expensive hardware to understand how deep learning works. In fact, you can build a fully functional neural network using only Microsoft Excel. This exercise is one of the most powerful ways to demystify the mathematics behind AI—forward propagation, backpropagation, and gradient descent—because you can see every calculation, every weight update, and every error change in real-time.
Example: Update weight B4 (x1→h1):
=B4 - $Z$1 * W14 (assuming W14 holds gradient for that weight) build neural network with ms excel full
Final note This Excel implementation teaches core NN math by making every intermediate derivative explicit. For reproducibility, keep copies of initial random seeds (or fixed initial weights) and record the epoch log. For production or larger experiments, migrate the same formulas to code (Python) for efficiency and flexibility. Building a Neural Network in Microsoft Excel: A
Assign one bias value to every neuron in the hidden and output layers. Towards Data Science 2. Forward Propagation Example: Update weight B4 (x1→h1): =B4 - $Z$1
For each neuron, you must calculate the weighted sum and apply an activation function: Weighted Sum (
Post: Most people think you need TensorFlow, PyTorch, or at least a Jupyter notebook to build a neural network.
Set Learning Rate (α): Go to Parameters sheet, cell N1, type 0.5.