When people talk about Deep Learning, they’re usually referring to training Neural Networks — sometimes very large ones.
But what exactly is a neural network? And how does it actually work?
Let’s break it down with a simple, real-world example that almost everyone can relate to — predicting house prices.
🏠 Example: Predicting House Prices
Imagine you have data about a few houses.
For each house, you know:
- Its size (in square feet or square meters)
- Its price
Your goal: predict the price of a new house based on its size.
If you’ve studied linear regression before, you might think,
“Let’s just fit a straight line through the data!”
So you draw a line that roughly shows how price increases with house size.
That’s simple linear regression — a straight line predicting price from size.
🚫 But Prices Can’t Be Negative!
Here’s a small problem:
If you extend that straight line backward, it might go below zero.
And, of course, a house price can’t be negative!
To fix this, you might say,
“Let’s bend the line — make it flat at zero, and then let it rise after a certain point.”
So you create a new curve:
- It stays at 0 for very small houses.
- Then increases linearly as the house gets bigger.
That’s your prediction function for house prices.
Surprisingly, this simple curve is already behaving like a tiny neural network!
🧩 A Single Neuron Explained
Let’s think of this setup as a little machine:
- Input: the size of the house (x)
- Output: the predicted price (y)
Between input and output, we have a small circle — a neuron.
This neuron does three simple things:
- Takes the input (house size).
- Applies a linear function (like multiplying by a number and adding something).
- Then makes sure the output is never below zero by using a function called ReLU.
⚙️ What Is ReLU?
ReLU stands for Rectified Linear Unit.
It’s a simple mathematical function that looks like this:
[
ReLU(x) = max(0, x)
]
That means:
- If the value is positive, keep it as it is.
- If it’s negative, make it 0.
Imagine a door that only opens one way — if you push from the wrong side, it stays closed (that’s the “rectify” part).
So in our house price example, ReLU ensures prices never drop below zero.
🧱 Building Bigger Neural Networks
Okay, so one neuron is like a single LEGO block.
What if we stack many of these blocks together?
That’s how we build larger neural networks.
Instead of using just one input (house size), let’s add more features:
- Size of the house
- Number of bedrooms
- Zip code or postal code
- Average wealth of the neighborhood
Each of these can affect the house price.
🧠 How Layers Work
Now, each small circle (neuron) in our network can represent an idea:
- Some neurons might focus on “family size” (based on number of bedrooms and size).
- Others might figure out “walkability” (based on zip code).
- Another might guess “school quality” (based on neighborhood wealth and postal area).
Finally, these features combine to predict the final house price.
So, instead of us manually writing formulas for these factors,
we just give the neural network the inputs (x) and desired outputs (y).
The network learns everything in between — automatically!
🔗 What Does “Densely Connected” Mean?
In a neural network, we often connect every input to every neuron in the next layer.
That’s called a densely connected layer (or a fully connected layer).
It means:
- Each neuron can see all the input features.
- It can decide on its own which combinations of inputs are most useful.
Think of it like a team of detectives — each detective looks at all the clues and comes up with their own theory before combining them to reach a final verdict.
📈 Why Neural Networks Are So Powerful
The amazing thing about neural networks is this:
Given enough data (x and y), they can learn very complex relationships between inputs and outputs.
That’s why they’re used in so many real-world applications:
- Predicting house prices 🏡
- Recognizing faces in photos 📸
- Translating languages 🌍
- Diagnosing diseases from X-rays 🩻
In all of these, you feed in some inputs (x), and the network learns to produce the correct output (y).
🏁 Summary: Key Takeaways
Here’s what you learned:
- Deep Learning = training neural networks (sometimes very large ones).
- A neuron takes inputs, applies a function, and produces an output.
- The ReLU function ensures outputs don’t go below zero.
- Stacking neurons together builds bigger, more powerful networks.
- Neural networks learn complex patterns automatically from data — especially in supervised learning, where you know both inputs and outputs.
In short:
A neural network is like a team of smart mini-calculators that learn together to understand complex relationships — from predicting house prices to powering self-driving cars.
In the next lesson, we’ll explore more real-world examples of where neural networks shine in supervised learning.
Top comments (0)