Perfect ✅ Here’s a well-structured README.md that introduces your project, explains why it’s useful, links to docs, and guides users on running examples.

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🧠 deeplearn_numpy

A NumPy-only deep learning mini-framework in a single file.

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🌟 Introduction

deeplearn_numpy is a minimal deep learning framework written entirely with NumPy. It is designed to be:

• Educational — understand how neural networks work under the hood (forward pass, backward pass, gradients, optimizers).
• Lightweight — a single Python file (deeplearn_numpy.py) with zero dependencies beyond NumPy.
• Practical — despite its simplicity, it supports configurable layers, activations, losses, optimizers, regularization, serialization, and evaluation.

This makes it a great companion for:

• Students learning the fundamentals of deep learning.
• Teachers who want to show step-by-step mechanics without hiding them in big libraries.
• Practitioners who want a lightweight sandbox to prototype and understand internals.

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🚀 Features

• Layers: Dense, Conv2D (im2col), MaxPool2D, AvgPool2D, Flatten, Dropout, BatchNorm.
• Activations: ReLU, Sigmoid, Tanh, LeakyReLU, Softmax.
• Losses: MSE, CrossEntropy (stable), BinaryCrossEntropy.
• Optimizers: SGD (momentum, Nesterov), Adam.
• Metrics: accuracy, binary accuracy, top-k accuracy.
• Regularization: L1, L2.
• Serialization: save & load weights.
• Config Builder: define models in JSON-like configs.

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📖 Documentation

We provide multiple developer resources:

• document.md — Full developer guide with detailed explanations.
• reference_guide.md — API reference (methods, classes, signatures).
• cheatsheet.md — Quick syntax lookup.
• examples/ — Example projects with code and tutorials.

🔎 Start with the moons example if you want a guided walkthrough with visualization.

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🛠️ Installation

Clone the repo:

git clone https://github.com/yourusername/deeplearn_numpy.git
cd deeplearn_numpy

No extra dependencies required beyond NumPy and Matplotlib (for examples).

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▶️ Running Examples

We recommend running examples from the repo root (so Python can find deeplearn_numpy.py). For example:

python -m examples.moon_boundry_learn.moon_boundry_learn

This runs the moon boundary learning example, where a small MLP learns to separate two crescent-shaped clusters of points.

👉 You’ll see:

• The dataset plotted.
• The initial random decision boundary.
• The trained decision boundary after learning.

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📂 Repository Structure

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├── cheatsheet.md            # Quick syntax reference
├── deeplearn_numpy.py       # Core framework (single file)
├── document.md              # Full developer guide
├── examples/                # Example projects
│   └── moon_boundry_learn/  # Moons dataset classification example
│       ├── document.md
│       └── moon_boundry_learn.py
├── index.html               # Project landing page
└── reference_guide.md       # API reference

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🎓 Why Use This Framework?

• Transparency: Unlike TensorFlow or PyTorch, you can open the file and read every line. No magic.
• Learning-first: Perfect for teaching neural nets in classrooms or self-study.
• Extendable: Add your own layers, losses, or optimizers by registering them.
• Real-world capable: Despite being small, it supports Conv2D, BatchNorm, Adam, etc. — so you can solve real problems on small datasets.

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❤️ Contributing

Contributions are welcome! If you want to add examples, new layers, or improve docs, open a pull request.

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📜 License

GPLv3

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this repo and all files in it are created by AI (chatGPT) thus use it responsibly.