Neural Network Architecture
In the previous lesson, we understood how artificial neurons are inspired by biological neurons and how they make decisions using numbers.
Now we take the next big step: How do multiple neurons work together?
This lesson explains the structure — or architecture — of a neural network.
Why One Neuron Is Not Enough
A single artificial neuron can solve only very simple problems.
For example, it can decide whether a number is greater than a threshold. But real-world problems are never that simple.
Recognizing faces, understanding speech, translating languages, or driving a car requires multiple levels of understanding.
This is why neurons must be organized into structured layers.
The Three Core Parts of a Neural Network
A neural network is made up of three main components:
Input layer, hidden layers, and an output layer.
Each layer has a specific role in transforming raw input into meaningful output.
Input Layer – Where Information Enters
The input layer is the entry point of the network.
It does not make decisions. Its job is simply to receive raw data and pass it forward.
If we are working with images, the input layer receives pixel values. If we are working with numbers, each input neuron receives one feature.
Think of the input layer as a receptionist who collects information but does not judge it.
Hidden Layers – Where Learning Happens
Hidden layers are the heart of Deep Learning.
These layers transform inputs step by step, extracting patterns that are not obvious at first glance.
Early hidden layers learn simple patterns. Deeper layers learn more abstract concepts.
For example, in image recognition:
- The first hidden layer may learn edges
- Next layers may learn shapes
- Deeper layers may learn objects like eyes or faces
This layered learning is what gives Deep Learning its power.
Real-World Example: Reading Handwritten Digits
Imagine a neural network that reads handwritten numbers.
The input layer receives pixel brightness values.
The first hidden layer may detect lines and curves. The next layer may detect circles or intersections.
By the time the signal reaches the final layers, the network understands whether the image is a 2, 5, or 8.
No single neuron understands the digit. Understanding emerges from the architecture as a whole.
Output Layer – Making the Final Decision
The output layer produces the final result of the network.
In classification problems, it may output probabilities for each class.
In regression problems, it may output a single numeric value.
This layer converts learned patterns into an actionable answer.
Depth Is the Key Difference in Deep Learning
Traditional Machine Learning models usually have no hidden layers or just one.
Deep Learning models have many hidden layers.
This depth allows the model to learn:
- Complex relationships
- Hierarchical patterns
- Abstract representations
Depth is what separates Deep Learning from shallow models.
Why Architecture Matters More Than You Think
Choosing the right architecture is as important as choosing the right algorithm.
Too few layers may lead to underfitting. Too many layers may lead to overfitting or training instability.
Architecture design is a balance between complexity and generalization.
Mini Practice (Think Like a Designer)
Ask yourself:
- Why do deeper networks learn better representations?
- Can more layers ever hurt performance?
Exercises
Exercise 1:
What is the main role of hidden layers?
Exercise 2:
Why is depth important in Deep Learning?
Exercise 3:
Does the input layer perform learning?
Quick Quiz
Q1. Where does most learning occur in a neural network?
Q2. What determines the final prediction?
In the next lesson, we will explore activation functions and understand how neurons decide when to activate or stay silent.