forgeNN 1.0.1

A From Scratch Neural Network Framework with Educational Purposes

forgeNN## Table of Contents

• Installation
• Overview
• Quick Start
• Educational Examples
• Architecture
• Performance
• Complete Example
• Links
• Contributing
• Acknowledgments

Installationtch Neural Network Framework with Educational Purposes*

� Table of Contents

• 📦 Installation
• 🚀 Overview
• 🔥 Quick Start
• 🎓 Educational Examples
• 🏗️ Architecture
• ⚡ Performance
• 📈 Complete Example
• 🔗 Links
• 🤝 Contributing
• 🌟 Acknowledgments

�📦 Installation

pip install forgeNN

Overview

forgeNN is a modern neural network framework that is developed by a solo developer learning about ML. Features vectorized operations for high-speed training.

Key Features

• Vectorized Operations: NumPy-powered batch processing (100x+ speedup)
• Dynamic Computation Graphs: Automatic differentiation with gradient tracking
• Complete Neural Networks: From simple neurons to complex architectures
• Production Loss Functions: Cross-entropy, MSE with numerical stability

Quick Start

Installation

pip install forgeNN

High-Performance Training

import forgeNN
from sklearn.datasets import make_classification

# Generate dataset
X, y = make_classification(n_samples=1000, n_features=20, n_classes=3)

# Create vectorized model  
model = forgeNN.VectorizedMLP(20, [64, 32], 3)
optimizer = forgeNN.VectorizedOptimizer(model.parameters(), lr=0.01)

# Fast batch training
for epoch in range(10):
    # Convert to tensors
    x_batch = forgeNN.Tensor(X)
    
    # Forward pass
    logits = model(x_batch)
    loss = forgeNN.cross_entropy_loss(logits, y)
    
    # Backward pass
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    acc = forgeNN.accuracy(logits, y)
    print(f"Epoch {epoch}: Loss = {loss.data:.4f}, Acc = {acc*100:.1f}%")

Educational Examples

Legacy Examples

# For learning automatic differentiation
from forgeNN.legacy import Value, MLP

x = Value(2.0)
y = x**2 + 3*x + 1
y.backward()
print(f"dy/dx = {x.grad}")  # 7.0

# Simple neural network
model = MLP(2, [4, 1])
output = model([Value(1.0), Value(2.0)])

Architecture

• Main API: forgeNN.Tensor, forgeNN.VectorizedMLP (production use)
• Legacy API: forgeNN.legacy.* (educational purposes)
• Functions: Complete activation and loss function library
• Examples: example.py - Complete MNIST classification demo

Performance

Implementation	Speed	MNIST Accuracy
Vectorized	38,000+ samples/sec	93%+ in <2s

Highlights:

• 100x+ speedup over scalar implementations
• Production-ready performance with educational clarity
• Memory efficient vectorized operations

Complete Example

See example.py for a full MNIST classification demo achieving professional results.

Links

• PyPI Package: https://pypi.org/project/forgeNN/
• Documentation: See guides in this repository
• Issues: GitHub Issues for bug reports and feature requests

Contributing

I am not currently accepting contributions, but I'm always open to suggestions and feedback!

Acknowledgments

• Inspired by educational automatic differentiation tutorials
• Built for both learning and production use
• Optimized with modern NumPy practices
• Available on PyPI: pip install forgeNN

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Made with care for the ML community. Happy learning and building!