Neural Networks Basics

Introduction

A neural network is a stack of math functions loosely inspired by neurons in the brain. Each 'neuron' takes numbers in, multiplies them by learned weights, adds a bias, runs the result through a non-linear function, and passes it on. Stack thousands of these and you can learn almost any pattern.

Beginner analogy: Imagine a giant set of dials. Training is the process of slowly turning each dial so the network gets better at its task. With billions of dials and trillions of examples, you get GPT-4.

Understanding the topic

Core concepts to understand:

• Neuron = output = activation(weights · inputs + bias).
• Layer = many neurons stacked in parallel.
• Deep network = many layers stacked in series.
• Training = adjust weights via backpropagation + gradient descent.
• Loss function measures how wrong the prediction was — minimised over time.

Syntax reference

Visual workflow / architecture:

Input layer        Hidden layers       Output layer
   o ─┐               o   o   o            ┌─ o
   o ─┼─── weights ──>o   o   o ─── ... ───┤
   o ─┘               o   o   o            └─ o
   ↑                   ↑                    ↑
 numbers          learned features      prediction

Training loop:
  forward pass → loss → backward pass → update weights
  (repeat millions of times on GPUs)

Real-world use

GPT-4 is rumoured to have ~1.7 trillion parameters across many transformer layers. Tesla Autopilot uses convolutional neural networks (CNNs) for vision. AlphaFold uses neural networks to predict protein structure — winning a Nobel Prize.

Best practices

• Start with small networks; scale only when needed.
• Always normalise inputs — networks hate raw, varying ranges.
• Use GPU/TPU for anything beyond toy problems.

Common mistakes

• Overfitting — a network can memorise training data but fail on new data.
• Vanishing/exploding gradients in very deep networks (solved by residual connections).

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What is a neuron in a neural network?
• Q2. What does backpropagation do?
• Q3. Why do we need non-linear activation functions?
• Q4. How do you prevent overfitting?