Building Neural Networks
from Scratch
Comprehensive documentation for NeuNet - a complete neural network framework built with NumPy. Learn the mathematical foundations, implementation details, and development journey.
Documentation Overview
Explore comprehensive guides covering every aspect of neural network development
Implementation Guide
A comprehensive technical walkthrough showing how to build a complete neural network implementation from mathematical foundations to advanced optimization techniques.
Read the guide →Development Timeline
Explore the commit-by-commit progression of NeuNet's development. Understand architectural decisions and how complexity evolved organically.
View timeline →API Reference
Complete reference documentation for all NeuNet classes, methods, and utilities. Perfect for developers integrating NeuNet into their projects.
Browse API →Project Overview
Learning Paths
Choose your learning journey based on your experience level
Beginners Start Here
- → Begin with Implementation Guide Phase 1
- → Study dense layer and activation functions
- → Understand the loss function implementation
- → Learn the basic training loop concepts
Developers Intermediate
- → Review development timeline for insights
- → Examine advanced optimization techniques
- → Study modular design patterns
- → Analyze design decisions
ML Engineers Advanced
- → Focus on optimizer implementations
- → Study regularization techniques
- → Examine high-level design
- → Review performance benchmarks
What You'll Learn
Master both the theory and practice of neural network development
Modular Architecture
Clean, extensible design with interchangeable components for maximum flexibility and maintainability.
Advanced Optimizers
State-of-the-art Adam and SGD with momentum optimizers for faster convergence and better performance.
Regularization
Built-in batch normalization, dropout, and L1/L2 regularization to prevent overfitting.
Visualization Tools
Interactive network visualization and comprehensive metrics for understanding model behavior.
Rich Layer Support
Dense layers, multiple activation functions, and advanced regularization techniques.
Pure Python
Built with NumPy and Python for educational clarity and ease of understanding.