Metadata-Version: 2.4
Name: synapse-nn
Version: 0.1.0
Summary: A pure Python neural network framework built from scratch.
Author: Yashdeep
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# Synapse-nn

A pure Python neural network framework built from scratch, featuring a custom matrix engine, dense layers, and activation functions. Synapse is designed as an educational project to explore how modern deep learning systems work under the hood.

## Features

- Custom Matrix implementation
- Dense (Fully Connected) Layers
- ReLU Activation
- Sigmoid Activation
- Tanh Activation
- No external deep learning frameworks

## Installation

```bash
pip install synapse-nn
```

## Quick Example

```python
from synapse import Matrix, Dense, ReLU

x = Matrix([[1, 2]])

layer = Dense(2, 4)
activation = ReLU()

output = activation.forward(layer.forward(x))

print(output)
```

## Project Roadmap

### Current
- [x] Matrix Engine
- [x] Dense Layers
- [x] ReLU
- [x] Sigmoid
- [x] Tanh

### Upcoming
- [ ] Loss Functions
- [ ] Backpropagation
- [ ] Optimizers
- [ ] Sequential API
- [ ] XOR Training Example
- [ ] Iris Dataset Training
- [ ] Model Saving & Loading

## Why Synapse?

Neural networks are inspired by biological neurons connected through synapses. This project aims to recreate the core building blocks of modern deep learning frameworks from first principles, providing a deeper understanding of the mathematics and implementation behind AI systems.

## Repository Structure

```text
synapse/
├── matrix.py
├── layers.py
├── activations.py
├── losses.py
├── optimizers.py
└── examples/
```

## License

MIT License
