Introduction

Building your own neural network from scratch is an enlightening exploration into the concepts of machine learning. While libraries like TensorFlow and PyTorch have made the process more accessible, understanding the underlying mechanics can enrich your problem-solving skills and foster a deeper appreciation for AI technologies. In this article, we will build a basic neural network in Python, explore its various components, and understand how it learns from data.

Prerequisites

Before diving into the coding aspects, make sure you have the following software and libraries installed:

• Python 3.x: The language of choice due to its simplicity and rich libraries.
• NumPy: A fundamental package for numerical computations, providing support for arrays and matrices.

You can install NumPy using pip:

• Input Layer: Receives input data, where each neuron corresponds to one input feature.
• Hidden Layers: Intermediate layers that perform computations and extract features from the data. The number can vary.
• Output Layer: Produces the final output, the prediction or classification label.
• Neuron: A basic unit in neural networks that computes weighted sums of inputs.
• Weights: `Parameters` that are adjusted during training.
• Bias: Additional parameter which allows models to shift outputs.
• Activation Function: Introduces non-linearities into the model, critical for learning complex patterns.