Neural Networks Explained: From Basics to Deep Learning

Neural networks are the engine behind modern AI. This guide explains how they work, from the basics to advanced architectures.

What is a Neural Network?

A neural network is inspired by the human brain. It consists of layers of artificial neurons that process and pass on information.

Core Components

Input Layer: receives the data
Hidden Layers: process and transform data
Output Layer: produces the prediction
Weights & Biases: parameters the network learns
Activation Functions: introduce non-linearity

How Does a Neural Network Learn?

The learning process happens in steps:

1
Forward Propagation
Data goes through the network, each layer applies transformations.
2
Loss Calculation
Calculate how far the prediction is from reality.
3
Backward Propagation
Calculate how each parameter contributes to the error (gradients).
4
Weight Update
Adjust weights in the direction that reduces error (gradient descent).
5
Repeat
Repeat this process thousands of times until the network predicts well.

Analogy

Imagine learning to ride a bike. Every time you fall (error), you learn what to do differently (weight update). After lots of practice (training), you can balance perfectly (well-trained model).

Types of Neural Networks

1. Feedforward Networks (FNN)

The simplest form. Data flows in one direction from input to output.

Use case: Classification and regression problems
Example: Email spam detection
Advantage: Simple and fast
Disadvantage: No memory of previous inputs

2. Convolutional Networks (CNN)

Specialized in image processing and pattern recognition.

Key Features

Convolutional layers
Pooling layers
Feature extraction
Translation invariance

Applications

Image classification
Object detection
Face recognition
Medical imaging

3. Recurrent Networks (RNN/LSTM)

Have "memory" and are suitable for sequential data.

Perfect For

Time series forecasting
Natural language processing
Speech recognition
Video analysis
Music generation

4. Transformers

The most recent and powerful architecture, basis for GPT and BERT.

Common Challenges & Solutions

Overfitting: Model learns training data too well, doesn't generalize
Vanishing Gradients: Gradients become too small in deep networks
Exploding Gradients: Gradients become uncontrollably large
Mode Collapse: GAN produces limited variation

Solutions for these problems:

Practical Example

Let's look at a simple image classification problem:

224x224

Input

Pixel images

1000

Classes