Intro to Neural Networks

Neural networks are the cornerstone of modern AI. This tutorial will guide you through the basic concepts, key components, and a simple implementation in Python.

What is a Neural Network?

A neural network is a computational model inspired by the human brain. It consists of layers of interconnected neurons that transform input data into meaningful output.

How It Works

Each neuron computes a weighted sum of its inputs, adds a bias, and passes the result through an activation function:

output = activation( Σ (weight_i * input_i) + bias )

Training a network means adjusting the weights and biases to minimize the error between predictions and true values using backpropagation and gradient descent.

Simple Python Example

Below is a minimal implementation of a single‑layer perceptron that learns the logical AND function.

import numpy as np

# Training data for AND
X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([0,0,0,1])

# Initialize weights and bias
w = np.random.rand(2)
b = np.random.rand(1)

def sigmoid(z):
    return 1/(1+np.exp(-z))

def predict(x):
    return sigmoid(np.dot(x,w)+b)

# Training
lr = 0.1
for epoch in range(1000):
    for xi, yi in zip(X,y):
        y_pred = predict(xi)
        error = yi - y_pred
        w += lr * error * xi
        b += lr * error

print("Trained weights:", w)
print("Trained bias:", b)
print("Predictions:", [predict(xi) for xi in X])

Try It Yourself

What activation function is most commonly used in deep neural networks?

Next Steps