# Handwritten Neural Network: Building a Multilayer Perceptron Step-by-Step with a Calculator

> A unique learning project where the author uses only a digital notepad and a TI-84 calculator to manually compute the forward and backward propagation of a Multilayer Perceptron (MLP), gaining an in-depth understanding of the core mechanisms of neural networks.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-10T18:15:32.000Z
- 最近活动: 2026-06-10T18:19:42.515Z
- 热度: 159.9
- 关键词: 神经网络, 多层感知机, 反向传播, 手动计算, 深度学习入门, MLP, 激活函数, 梯度下降
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-clippie-walkthrough-mlp-from-scratch
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-clippie-walkthrough-mlp-from-scratch
- Markdown 来源: floors_fallback

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## Introduction: Manually Building an MLP with a Calculator—Deep Dive into the Core Mechanisms of Neural Networks

Clippie released the project *Walkthrough_MLP_from_Scratch* on GitHub. Its core is to use a digital notepad and TI-84 calculator to manually perform the forward and backward propagation calculations of a Multilayer Perceptron (MLP), unveiling the mystery of neural network internal mechanisms from first principles and addressing the vague understanding of underlying principles caused by over-reliance on deep learning framework APIs.

## Project Background and Motivation

Modern deep learning frameworks (such as PyTorch and TensorFlow) are convenient, but they often lead practitioners to only know how to call APIs without understanding the internal logic of models. The original author adopted an extreme manual calculation approach (without Python/NumPy) to gain a solid understanding of neural network mechanisms and form a reliable intuitive foundation when dealing with complex models. The author said: 'This process helped me unveil the mystery of AI and also made me have deeper respect for the computing power of modern computers.'

## Neural Network Basics and Architecture Design

### Basic Concepts
Artificial neural networks are inspired by biological neurons. A single perceptron is the basic unit (weighted sum of inputs + bias + activation function), and stacking multiple layers forms an MLP.
### Architecture Selection
Due to manual calculation constraints, the project uses an extremely simple but complete architecture: 2 neurons in the input layer, 2 in the hidden layer, and 1 in the output layer (functionally a two-layer trainable network). Weights and biases are initialized to random values between 0 and 1 to simplify calculations.

## Manual Calculation Methods for Forward and Backward Propagation

### Forward Propagation
1. Hidden layer: Input × weight + bias → Sigmoid activation;
2. Output layer: Weighted sum of hidden layer outputs + bias → Sigmoid activation to get the predicted value.
### Backward Propagation
1. Loss calculation: Mean Squared Error (MSE) = (predicted value - true value)² / 2;
2. Gradient calculation: Use the chain rule to find the gradient of the loss with respect to each parameter. The derivative of Sigmoid can be directly calculated from the output (σ’(x) = σ(x) × (1 - σ(x)));
3. Parameter update: New weight = old weight - learning rate × gradient.

## Deep Value of Manual Calculation

1. **Demystify Frameworks**: Automatic differentiation is essentially a systematic implementation of the chain rule, and GPU acceleration comes from parallelization of matrix operations;
2. **Cultivate Numerical Intuition**: Observe the impact of weight initialization, learning rate, and activation functions on the model;
3. **Understand Computational Complexity**: A minimal 2-2-1 network requires dozens of manual steps, while modern models have millions/billions of parameters, leading to enormous computational load.

## Project Implementation Details

### Tool Selection
- TI-84 calculator: Supports scientific computing and basic programming;
- Digital notepad: Records intermediate results and parameter states.
### Training Loop Process
Initialization → Forward propagation → Loss calculation → Backward propagation → Parameter update → Repeat.
### Visualization and Documentation
The repository contains detailed calculation screenshots showing each step, making it easy for learners to follow and practice.

## Advice for Deep Learning Learners

### Suitable Scenarios
Building intuition in the introductory stage, interview preparation (handwriting backward propagation), improving understanding in teaching scenarios, and returning to basics when debugging model anomalies.
### Learning Path
First understand the principles (manual calculation) → then use framework tools → finally debug and optimize (principles + tools). It is opposed to skipping principles and directly using frameworks.

## Conclusion: The Attitude of Slow Learning

The value of the project is not in solving practical problems, but in demonstrating the attitude of pursuing a thorough understanding of principles. It reminds us to think about the mechanisms behind multiplication, addition, weight updates, etc., when calling `model.fit()`, to become better deep learning practitioners. In today's era of rapid AI development, this learning attitude that is not satisfied with API calls is particularly precious.