# Building Neural Networks from Scratch: Deep Learning Practice Without PyTorch or TensorFlow

> A complete handwritten neural network implementation project covering core components such as neurons, activation functions, loss functions, backpropagation, and optimizers

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-02T17:14:25.000Z
- 最近活动: 2026-06-02T17:23:22.111Z
- 热度: 157.8
- 关键词: neural-network, deep-learning, from-scratch, backpropagation, activation-functions, optimizers, educational
- 页面链接: https://www.zingnex.cn/en/forum/thread/pytorch-tensorflow
- Canonical: https://www.zingnex.cn/forum/thread/pytorch-tensorflow
- Markdown 来源: floors_fallback

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## Introduction: Practical Value and Core Content of Building Neural Networks from Scratch

This article introduces the open-source project *Neural-Network-From-Scratch* published by Meraj-coding21 on GitHub. It aims to solve the black-box problem in using deep learning frameworks by implementing core neural network components (neurons, activation functions, backpropagation, etc.) from scratch, helping developers deeply understand underlying principles, improve debugging skills, and lay a foundation for innovation. The project only relies on basic Python libraries (such as NumPy) and is suitable for learners who want to solidly grasp the essence of deep learning.

## Background: The Black-Box Problem Behind Framework Convenience

Current deep learning frameworks (PyTorch, TensorFlow, etc.) simplify model building, but they also lead many practitioners to become 'framework callers' who have only a superficial understanding of underlying principles: unable to debug when models behave abnormally, and at a loss when customizing functions. This project was created to solve this problem—by hand-writing each component, developers can truly understand how deep learning works.

## Project Overview: Educational Open-Source Implementation

*Neural-Network-From-Scratch* is an educational open-source project whose goal is to implement all core neural network components using only basic Python libraries, without relying on any deep learning frameworks. The project includes implementations from a single neuron to a complete training process, along with detailed study notes.

## Implementation of Core Components: Hand-Writing Process from Neurons to Optimizers

### Implementation of Core Components
1. **Single Neuron**: Based on the mathematical formula `output = activation(dot(input, weights) + bias)`, one needs to understand weight initialization, the role of bias, and dimension matching.
2. **Activation Functions**: Implement ReLU, Sigmoid, Tanh, Softmax and their derivatives (key for backpropagation).
3. **Loss Functions**: Implement MSE (regression) and cross-entropy (classification), and understand their mathematical definitions and gradient derivation.
4. **Backpropagation**: Calculate gradients via the chain rule, steps include forward propagation, loss gradient calculation, layer-by-layer gradient propagation, and parameter update.
5. **Optimizers**: Implement SGD, Momentum SGD, Adam; understand first/second moment estimation and bias correction.

## Learning Value: In-Depth Understanding and Skill Improvement

### Learning Value
- **Understand the Essence**: Master gradient descent principles, the necessity of activation functions, the impact of weight initialization, and the role of hyperparameters.
- **Develop Debugging Skills**: Quickly locate issues like gradient vanishing/explosion, loss non-convergence, overfitting, etc.
- **Foundation for Innovation**: Provide support for designing new activation functions, optimization algorithms, and regularization techniques.

## Practical Suggestions: Step-by-Step Learning Path

### Practical Suggestion Path
1. Start with linear regression to understand the basic principles of gradient descent;
2. Implement logistic regression, introducing Sigmoid and classification problems;
3. Build a simple neural network (single hidden layer) to solve the XOR problem;
4. Expand to deep networks and try MNIST classification;
5. Implement convolutional layers and understand parameter sharing and local connections.
Each step requires verifying intermediate results and comparing with framework implementations to ensure correctness.

## Relationship with Frameworks: Understanding the Underlying Layer Aids Efficient Use

Implementing from scratch does not mean rejecting frameworks; instead, it enables better use of frameworks:
- Understand the design logic of framework APIs;
- Clarify when to use advanced features and when to customize;
- Be able to read framework source code and learn best practices.
PyTorch/TensorFlow developers themselves are experts in underlying implementations, and their work improves learning and research efficiency.

## Conclusion: Underlying Principles Are a Key Investment for Long-Term AI Development

This project represents one of the optimal paths for deep learning learning—'first understand deeply, then use efficiently'. In today's rapidly developing AI field, understanding underlying principles has more lasting value than proficiency in APIs. For learners who want to develop long-term in the AI field, spending time understanding the internal mechanisms of neural networks is a worthwhile investment, and this project provides a good starting point and reference.