Building a Neural Network from Scratch in Python

Learn how to build a neural network from scratch in Python using NumPy

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Building a neural network from scratch in Python can seem like a daunting task, but with the right tools and understanding, it can be quite simple. In this article, we will walk through the process of building a basic neural network in Python using the popular library NumPy.

Let us begin.

First, we will start by importing the necessary libraries:

import numpy as np

Next, we will define our neural network class, which will contain the methods for training and making predictions. We’ll start by defining the initialization method, which will take in the number of input and output nodes, as well as the number of hidden layers and nodes in the hidden layers:

class NeuralNetwork:
    def __init__(self, input_nodes, hidden_layers, hidden_nodes, output_nodes):
        self.input_nodes = input_nodes
        self.hidden_layers = hidden_layers
        self.hidden_nodes = hidden_nodes
        self.output_nodes = output_nodes

We will then define a method for initializing the weights of our network. The weights will be stored in a list of matrices, with each matrix representing the weights between a layer…