How a Neural Network *Actually* Works (A Developer's Mental Model)

Stop Thinking of Neural Networks as a "Brain" (A Developer's Mental Model)

We’ve been told a neural network is like a brain. Let’s be clear: it’s not. For a developer, that analogy is confusing, inaccurate, and frankly, not very useful.

As an engineer who builds production-grade ML systems, I don’t think of neural networks as magic. I think of them as what they really are: a chain of simple, programmable functions, stacked one after another, using the same matrix math we've been covering.

In this post, we'll dismantle the biology myth and give you a practical, developer-centric mental model for how these powerful systems *actually* work.

Watch the video first for the full visual explanation, then scroll down for the key concepts and code analogies.

The Big Secret: It's Just a Chain of Functions

The secret to demystifying neural networks is this: a "deep" network is just a function calling a function, calling another function. It's a call stack.

Each "layer" in the network is a function that performs a simple task: it takes an input vector and performs a matrix multiplication with its own internal "weight" matrix.

output_of_layer_1 = process_layer_1(input_vector)
output_of_layer_2 = process_layer_2(output_of_layer_1)

A 100-layer "deep learning" model is just this concept, nested 100 times. It's not a brain; it's composable engineering.

A Developer's Mental Model: The API Analogy

The most practical way to think about a neural network is as if you were designing an API:

• A "Layer" is an API Endpoint: It accepts a specific input (a vector) and returns a specific output (another vector). The logic inside is just a dot product, a bias addition, and an activation "filter".
• "Weights" are the Config File: The weights and biases are simply the `config.json` for your endpoint. They're the numbers that configure the function's behavior.
• "Learning" is a Brute-Force Optimization Problem: Training is just an iterative process of finding the right values for your config files. You pass data through, check how wrong the output is, and use an algorithm (like backpropagation) to "nudge" the weights in the right direction, repeating millions of times.

Here is what the pseudo-code for a single layer might look like:

def process_layer(input_vector, config):
  # Core logic is just a dot product
  output = np.dot(input_vector, config.weights) + config.bias
  
  # Apply a simple non-linear "filter"
  final_output = activation_function(output)
  
  return final_output
    

A Principal Engineer's Insight: When to Use the Sledgehammer

The golden rule of a practitioner is to **always start with the simplest model possible.** A neural network is a powerful, high-complexity tool—a sledgehammer.

You only reach for the sledgehammer when you've proven that a simpler tool, like a regular hammer (e.g., Linear Regression), can't solve your problem. This power comes at a cost: neural networks are often a "black box," hard to debug, and require massive amounts of clean data.

Remember, the model itself is often just 10% of the work. The other 90%—the real engineering challenge—is building the infrastructure around it:

• Data Pipelines
• Feature Stores
• Monitoring and Alerting
• Scalable Serving Infrastructure

Conclusion

Forget the brain. A neural network is a chain of functions, configured by weights, and optimized through training. This developer-centric mental model is the key to moving from simply *using* ML libraries to truly *understanding* the systems you build.

Your Turn...

Did this "API" or "Lego block" analogy help make neural networks click for you? What's the most confusing part of neural networks you'd like to see decoded next? Let me know in the comments!

This post is part of the "The Foundations: Linear Algebra for Developers" series. For the previous part, check out: Matrix Inverse Explained.