Building a Neural Network from Scratch

Neural networks are a cornerstone of modern machine learning. Understanding how to build one from scratch can provide a deeper insight into how they work. In this tutorial, we will walk through the process of creating a simple neural network without using any external libraries.

Introduction to Neural Networks

A neural network is a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

Setting Up

Before we dive into the code, let's set up our environment. We will be using Python and NumPy for our calculations.

import numpy as np

The Basic Structure

A neural network consists of layers, each containing neurons. Neurons are the basic computational units that process the input data.

Input Layer

The input layer is where the data enters the network. Each neuron in this layer receives a single input from the previous layer.

Hidden Layers

Hidden layers are in between the input and output layers. Each neuron in a hidden layer receives inputs from the previous layer and produces an output that is passed to the next layer.

Output Layer

The output layer is where the final predictions are made. The number of neurons in this layer depends on the type of problem we are solving.

Training the Network

Training a neural network involves adjusting the weights and biases of the neurons based on the input data. We use a process called backpropagation to do this.

def train_network(network, training_data, learning_rate):
    # Code for training the network
    pass

Evaluation

After training the network, we evaluate its performance using a separate set of test data. This helps us understand how well our network generalizes to new, unseen data.

def evaluate_network(network, test_data):
    # Code for evaluating the network
    pass