Neural Networks: How they work and why they are so powerful

If you've spent any time in the tech world lately, you've heard the term "Neural Network" a thousand times. It’s the engine behind ChatGPT, Midjourney, and self-driving cars.

But for many developers, they remain a "black box"—a complex web of math that somehow produces magic.

Today, we are going to crack that box open. We’ll look at how they actually work, from the perspective of an engineer who cares about logic and implementation, not just theory.

Opening Context

We are currently in a massive transition. A few years ago, "AI" for most developers meant a collection of if/else statements or simple regression models. Now, we are expected to understand high-dimensional vector spaces and backpropagation.

The debate right now isn't about if neural networks are powerful, but how we can make them more efficient and interpretable. Why use a trillion-parameter model when a smaller, specialized network might do? To answer that, you have to understand the fundamental unit: the Artificial Neuron.

Mental Model: The Committee of Experts

Think of a Neural Network not as a single brain, but as a hierarchical committee of experts.

Imagine you are trying to decide if a photo is of a "Dog."

• Layer 1 (The Juniors): These experts only look at tiny pixels. One looks for vertical lines, another for curves, another for color gradients.
• Layer 2 (The Managers): They listen to the Juniors. If enough Juniors report "vertical lines" and "curves" in a specific pattern, the Manager concludes, "I see a floppy ear."
• Layer 3 (The Executives): They listen to the Managers. If one Manager says "floppy ear" and another says "wet nose" and a third says "wagging tail," the Executive shouts, "It’s a Dog!"

This is the Universal Function Approximator in action. By stacking simple decisions on top of each other, you can model incredibly complex reality.

Hands-On Example: A Simple Network in Python

Let's build a minimal neural network using PyTorch. We’ll create a network that learns to solve a simple XOR-like problem (something a simple linear model can't do).

import torch
import torch.nn as nn
import torch.optim as optim

# 1. Define the Architecture
class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        # Input Layer (2) -> Hidden Layer (4)
        self.hidden = nn.Linear(2, 4)
        # Activation Function (ReLU)
        self.relu = nn.ReLU()
        # Hidden Layer (4) -> Output Layer (1)
        self.output = nn.Linear(4, 1)
        # Final Activation (Sigmoid for probability)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.hidden(x)
        x = self.relu(x)
        x = self.output(x)
        x = self.sigmoid(x)
        return x

# 2. Setup Data
X = torch.tensor([[0,0], [0,1], [1,0], [1,1]], dtype=torch.float32)
y = torch.tensor([[0], [1], [1], [0]], dtype=torch.float32)

# 3. Training Loop
model = SimpleNet()
criterion = nn.BCELoss() # Binary Cross Entropy
optimizer = optim.SGD(model.parameters(), lr=0.1)

for epoch in range(1000):
    optimizer.zero_grad()
    outputs = model(X)
    loss = criterion(outputs, y)
    loss.backward() # This is the "Learning" part
    optimizer.step()

print("Training Complete. Model can now solve non-linear problems.")

Design Choice Note: We used a "Hidden Layer." Without it, the model is just a linear regression. The hidden layer is where the "feature extraction" happens—where the committee looks for those ears and tails.

Under the Hood: The Calculus of Correction

What happens during loss.backward()? This is Backpropagation.

Neural networks don't "know" anything at first. They start with random weights (guesses). When the network makes a mistake, we calculate the "error" (Loss).

The math then works backward from the output to the input, asking: "How much did this specific connection contribute to the mistake?" We then nudge that connection's weight to be slightly better next time.

• Performance: Training is computationally expensive (Matrix multiplications). Prediction (Inference) is much faster.
• Latency: As you add more layers (Deep Learning), latency increases. This is why "tiny" models are trending for edge devices.
• Scaling: The "Power Law" of scaling suggests that more data + more compute + more parameters almost always leads to better performance. We haven't hit the ceiling yet.

Author’s Take

I've seen too many developers jump straight to "LLMs for everything."

LLMs are amazing, but they are overkill for many tasks. If you are predicting churn, or detecting fraud in structured data, a small, custom-trained Neural Network (or even a Gradient Boosted Tree) will be 100x faster, 1000x cheaper, and much easier to secure.

I would not ship a generative AI solution for a classification problem that a 4-layer MLP (Multi-Layer Perceptron) could solve. Engineering is about picking the right tool, not the most famous one.

Conclusion

Neural Networks are powerful because they are flexible. They don't require you to manually define what a "dog ear" looks like; they learn it from the data themselves.

Next time you see a "black box" AI, remember the committee. It’s just a lot of simple experts working together, refined by a lot of calculus.

Next Step: Try modifying the SimpleNet code above. Add another layer. Change the ReLU to a Tanh. Watch how the training speed changes. That is where the real learning begins.