Building Neural Networks with Python and NumPy

Introduction

This repository provides a comprehensive guide to building neural networks from scratch using Python and NumPy. It covers essential topics such as:

• Coding Neurons: Understand the fundamental building blocks of neural networks.
• Layering: Learn how to stack neurons into layers to create complex architectures.
• Activation Functions: Explore different activation functions and their impact on model performance.
• Calculating Loss: Gain insights into how to measure the performance of your neural network.
• Backpropagation: Master the technique for training neural networks by adjusting weights based on error gradients.
• Optimizing Parameters: Discover methods to improve the efficiency and accuracy of your models.

This project is ideal for developers and data scientists looking to deepen their understanding of machine learning and neural networks, providing practical coding examples and theoretical insights.