🚀 A Beginner-Friendly Guide to Neural Networks
This repository provides a clear and concise introduction to the fundamentals of neural networks, specifically focusing on forward pass and backward pass calculations. Whether you're new to machine learning or looking for a simple yet effective way to understand the core concepts, this guide will help you connect the dots.
✔️ Step-by-Step Forward and Backward Pass – Learn how data flows through a neural network and how gradients are computed.
✔️ Single Data Point Example – Understand how neural networks process one example at a time.
✔️ Multiple Data Points Example – See how batch processing works for better generalization.
✔️ Feature Representation Variations – Some textbooks represent features as rows, while others use columns—this repo covers both!
