Neural Networks (Basics)

In the previous lesson, we learned about Reinforcement Learning, where an agent learns by interacting with an environment.

In this lesson, we begin a new and very important topic: Neural Networks.

Neural networks are the foundation of modern deep learning systems used in image recognition, speech processing, recommendation engines, and artificial intelligence applications.

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What Is a Neural Network?

A neural network is a machine learning model inspired by the way the human brain works.

Just like the brain has neurons connected together, a neural network consists of artificial neurons connected in layers.

Each neuron receives input, performs a small computation, and passes the result forward.

When many neurons work together, they can learn complex patterns from data.

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Why Neural Networks Are Needed

Traditional models like linear regression work well for simple relationships.

However, real-world problems are rarely linear.

Neural networks can model complex, non-linear relationships that simpler models cannot handle.

This makes them powerful for tasks such as image classification and natural language processing.

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Basic Structure of a Neural Network

A basic neural network has three main parts.

The input layer receives raw features such as income, age, or credit score.

The hidden layer processes information by applying weights and activation functions.

The output layer produces the final prediction.

Data flows from input to output step by step.

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Understanding a Single Neuron

A single neuron performs three actions.

First, it multiplies each input by a weight.

Second, it adds all weighted inputs together and includes a bias value.

Finally, it passes the result through an activation function.

This simple operation allows the network to learn non-linear patterns.

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Activation Functions (Conceptual)

Activation functions decide whether a neuron should be activated.

Without activation functions, neural networks would behave like linear models.

Common activation functions include ReLU, Sigmoid, and Tanh.

We will study them in detail in later lessons.

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Relating Neural Networks to Our Dataset

Using our Dataplexa ML dataset, a neural network can learn complex interactions between income, credit score, and employment years.

Unlike logistic regression, a neural network can capture hidden patterns that are not obvious.

This is why neural networks are preferred for large and complex datasets.

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Simple Neural Network Example (Conceptual)

Imagine predicting loan approval.

Instead of one equation, a neural network uses many neurons working together.

Each neuron learns a small pattern, and together they form a powerful model.

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When Should You Use Neural Networks?

Neural networks are best used when:

• Data size is large • Relationships are complex • Simpler models fail to perform well

For small datasets, simpler models are often better.

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Mini Practice

Think about which features in our dataset might interact in a non-linear way.

This helps you understand why neural networks can outperform linear models.

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Exercises

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Quick Quiz

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In the next lesson, we move deeper into this topic by introducing Deep Learning and how neural networks scale with multiple layers.