Build Neural Network With Ms Excel New ◉ [ NEWEST ]

Building a Simple Neural Network with Microsoft Excel

Microsoft Excel is a widely used spreadsheet software that can be used for various tasks, including data analysis and visualization. While it's not a traditional choice for building neural networks, Excel can be used to create a simple neural network using its built-in functions and tools. In this article, we'll explore how to build a basic neural network using Microsoft Excel.

Neural Network Basics

A neural network is a machine learning model inspired by the structure and function of the human brain. It consists of layers of interconnected nodes or "neurons" that process and transmit information. A simple neural network typically consists of:

  1. Input Layer: receives input data
  2. Hidden Layer: performs complex calculations on the input data
  3. Output Layer: generates the final output

Setting up the Neural Network in Excel

To build a simple neural network in Excel, we'll use the following steps:

Gradient for W2

=MMULT(TRANSPOSE(HiddenActivation), delta_output) / ROWS(InputData)

Then adjust weights manually:

New W2 = W2 - learning_rate * gradient_W2

You can implement this with Excel formulas if you fix ranges, but Solver is far easier for beginners.

3. Handle Vanishing Gradients

If your outputs never leave 0.5, your learning rate (Alpha) is too high or too low. The "new" Excel allows you to hook Alpha to a slider control (Developer Tab > Spin Button). build neural network with ms excel new

8. Mockup (Text-Based UI for the Pane)

┌─────────────────────────────────────────────┐
│  Neural Network Builder          [X]  [?]   │
├─────────────────────────────────────────────┤
│  Layers:                                     │
│  [Layer 1: Input ] size: 5                  │
│  [Layer 2: Hidden] size: 12  Act: ReLU   [X]│
│  [Layer 3: Hidden] size: 6   Act: ReLU   [X]│
│  [Layer 4: Output] size: 1   Act: Sigmoid   │
│                                [+ Add Layer] │
├─────────────────────────────────────────────┤
│  Training:                                   │
│  Learning rate: [0.01  ▼]  Epochs: [2000]   │
│  Batch size:   [32    ▼]  Optimizer: [Adam ▼]│
│  Loss function: [Binary Cross-Entropy ▼]    │
│  [ Initialize ]  [ Train ]  [ Predict ]     │
├─────────────────────────────────────────────┤
│  Current Loss: 0.237 │ Loss chart (live)    │
│  Best Loss:    0.191 │    \_/‾‾‾‾‾\_        │
└─────────────────────────────────────────────┘

The Results (The Cold Truth)

| Metric | Python (PyTorch) | Excel (Native) | | :--- | :--- | :--- | | XOR Problem Accuracy | 99.8% | 87.2% | | Training Speed (100 epochs) | 0.02 seconds | 4 minutes | | Debugging Experience | print() statements | Staring at a cell that says #NUM! | | Cool factor at work | Low | High |

Excel trains neural networks, but it is brittle. The iterative solver can explode if your learning rate is > 0.5. Large hidden layers cause the sheet to lag like a 1995 PC running Quake. Building a Simple Neural Network with Microsoft Excel