🧠 Basics of Neural Networks: How Machines Learn Like Humans

August 06, 2025

Have you ever wondered how machines recognize faces, understand speech, or recommend what to watch next?

The answer lies in Neural Networks — the foundation of modern Artificial Intelligence (AI) and Deep Learning.

At Qualithought Training Institute, we break down complex concepts like Neural Networks into easy, career-ready learning paths.

🤖 What is a Neural Network?

A Neural Network is a machine learning model inspired by the human brain. Just like our brain uses neurons to process information, a neural network uses artificial neurons (nodes) to understand data.

It’s the core of many advanced AI systems today — from chatbots and translators to self-driving cars and medical diagnosis tools.

🏗️ Structure of a Neural Network

A basic neural network has three layers:

1️⃣ Input Layer

Takes raw data (like an image or text).

2️⃣ Hidden Layers

These layers process the data using mathematical functions and patterns.

3️⃣ Output Layer

Gives the final result (like a prediction or classification).

Each neuron in a layer is connected to the neurons in the next layer. These connections have weights, and learning means adjusting these weights to minimize errors.

🔄 How Do Neural Networks Learn?

Neural networks learn through a process called backpropagation using training data.

Steps:

The input passes through the network (forward pass).
An error is calculated by comparing the output with the correct answer.
The error is sent backward (backpropagation) to adjust the weights.
This cycle is repeated over thousands of examples until the network performs accurately.

🧪 Real-World Applications

📸 Face recognition on phones

🧬 Disease detection from X-rays

🎧 Voice assistants like Siri

🛍️ Product recommendations

🎮 Game playing AI (like AlphaGo)