Getting Started with Neural Networks

Learn the basics of neural networks with this simple tutorial. We’ll create a basic feedforward neural network using Python’s built-in math capabilities.

Understand the basic structure of a neural network
Implement a simple neuron with sigmoid activation
Create a network layer
Train with basic gradient descent
Visualize the learning process

import math

def sigmoid(x):
    return 1 / (1 + math.exp(-x))

class Neuron:
    def __init__(self, weights, bias):
        self.weights = weights
        self.bias = bias
    
    def feedforward(self, inputs):
        total = sum(w*i for w,i in zip(self.weights, inputs)) + self.bias
        return sigmoid(total)

class NeuralNetwork:
    def __init__(self):
        self.hidden_layer = [Neuron([0.5, -0.6], 0.1)]
        self.output_layer = [Neuron([0.1], -0.3)]
    
    def train(self, inputs, targets, learning_rate=0.1):
        # Training logic here
        pass

To better understand neural networks, let’s break down the key components:

Input Layer: Receives the initial data
Hidden Layers: Perform computations and feature extraction
Output Layer: Produces the final result
Weights: Determine the strength of connections
Bias: Allows shifting the activation function