This repository provides a comprehensive, from-scratch implementation of neural networks, targeting beginners in machine learning. It aims to demystify neural network concepts by offering detailed explanations alongside practical code examples, enabling a deeper understanding of the underlying mechanics.

How It Works

The project implements neural network concepts from first principles, avoiding high-level libraries like TensorFlow or PyTorch for core operations. This approach allows users to grasp the fundamental mathematical operations and data flow within neural networks, fostering a solid foundational knowledge.

Quick Start & Requirements

• Primary install / run command: pip install -r requirements.txt followed by running Python scripts (e.g., python main.py).
• Prerequisites: Python 3.x, NumPy, Matplotlib.
• Setup time: Minimal, assuming Python and pip are installed.

Highlighted Details

• Covers fundamental neural network architectures and algorithms.
• Provides step-by-step code implementations for each concept.
• Focuses on building intuition through practical application.

Maintenance & Community

Information regarding maintenance, community channels, or notable contributors is not present in the provided README.

Licensing & Compatibility

The repository does not explicitly state a license. Users should exercise caution regarding usage and distribution.

Limitations & Caveats

The project is primarily educational and may not be optimized for performance or scalability compared to established deep learning frameworks. It lacks explicit licensing information, which could impact commercial use.