Craftsman Leadership

Introduction

Neural networks are the backbone of modern AI, powering everything from image recognition to language models. In this guide, you’ll understand what neural networks are, how they’re inspired by the brain, and how they actually work under the hood.

Prerequisites

Basic understanding of AI and machine learning concepts is helpful. Familiarity with the idea that computers can learn from data will make this easier to follow.

The Brain Analogy

Neural networks are loosely inspired by how our brains work. Your brain contains billions of neurons—cells that pass electrical signals to each other. When you learn something new, the connections between neurons strengthen or weaken.

Similarly, artificial neural networks consist of interconnected nodes (artificial neurons) that pass signals. When the network learns, it adjusts the strength of these connections.

Important Note: While inspired by biology, artificial neural networks are much simpler than real brains. They’re a useful mathematical tool, not a recreation of biological intelligence.

The Basic Structure

Layers

A neural network is organized in layers:

Input Layer: Receives the raw data (pixels in an image, words in a sentence, etc.)
Hidden Layers: Process the data through multiple transformations
Output Layer: Produces the final result (a prediction, classification, etc.)

Think of it like an assembly line: raw materials enter, go through multiple processing stations, and come out as a finished product.

Neurons (Nodes)

Each “neuron” in the network:

Receives inputs from neurons in the previous layer
Multiplies each input by a “weight” (importance factor)
Adds all these weighted inputs together
Applies an “activation function” (more on this later)
Sends the result to neurons in the next layer

Weights and Biases

Weights determine how much influence one neuron has on another. A high weight means “this connection is important,” while a low weight means “this connection doesn’t matter much.”

Biases help the network make decisions even when all inputs are zero. Think of them as the neuron’s “default setting.”

How Neural Networks Learn

The Training Process

Learning happens through a process called backpropagation:

Forward Pass: Feed data through the network and get a prediction
Calculate Error: Compare the prediction to the correct answer
Backward Pass: Go backwards through the network, adjusting weights to reduce the error
Repeat: Do this thousands or millions of times with different examples

A Simple Analogy: Tuning a Recipe

Imagine you’re trying to bake the perfect chocolate chip cookie. Your recipe has various “parameters”: amount of flour, sugar, butter, baking time, temperature, etc.

You bake cookies with your initial guess at measurements
You taste them (measure the error)
Too sweet? Reduce sugar. Too flat? Add more flour.
You adjust the recipe and try again
Repeat until the cookies taste perfect

Neural networks do exactly this, but with thousands or millions of parameters instead of just a few recipe measurements.

Types of Neural Networks

Feedforward Networks

The simplest type—information flows in one direction from input to output. Good for straightforward classification tasks.

Convolutional Neural Networks (CNNs)

Specialized for processing images. They look at small patches of an image at a time, learning to recognize edges, then shapes, then objects. This is how your phone recognizes faces.

Recurrent Neural Networks (RNNs)

Designed for sequential data like text or time series. They have “memory” of previous inputs, making them good for tasks like language translation or predicting stock prices.

Transformers

The newest architecture, used by ChatGPT and similar models. They can pay attention to different parts of the input simultaneously, making them very powerful for language tasks.

What Makes Neural Networks Powerful?

1. Learning Hierarchies

Early layers learn simple patterns (edges in an image), middle layers learn combinations (shapes), and later layers learn complex concepts (faces, objects).

2. Handling Complexity

Neural networks can learn patterns too complex for humans to explicitly program. You couldn’t write rules for recognizing every possible dog, but a neural network can learn this from examples.

3. Generalization

A well-trained network doesn’t just memorize—it learns general patterns that work on new, unseen data.

Real-World Applications

Image Recognition: A CNN trained on millions of labeled photos learns to identify objects, animals, and people with high accuracy.

Language Translation: Transformer networks learn patterns in how languages correspond, enabling them to translate text they’ve never seen before.

Voice Assistants: Neural networks convert your speech to text, understand your intent, and generate appropriate responses—all using different specialized networks.

Game Playing: Neural networks combined with reinforcement learning have mastered games like Chess, Go, and even complex video games.

Limitations and Challenges

Data Hungry

Neural networks typically need lots of training data—sometimes millions of examples—to work well.

Black Box Nature

It’s often hard to understand exactly why a neural network made a particular decision. The weights and patterns are too complex for humans to interpret easily.

Brittleness

Networks can be fooled by inputs that humans would easily understand. For example, adding specific noise to an image can make a network misclassify it completely.

Computational Cost

Training large neural networks requires significant computing power and energy, which can be expensive and environmentally impactful.

Try It Yourself

Visualize It: Visit TensorFlow Playground (an interactive tool) to see a neural network learn in real-time. You can adjust layers, neurons, and watch how the network learns to classify data.
Think About Weights: Consider a simple decision, like “Should I bring an umbrella?” based on inputs like cloud coverage, humidity, and weather forecast. Which input would you give the highest “weight” to?
Spot the Networks: For a day, try to identify where neural networks might be working when you use technology. Smartphone camera features? Autocomplete? Email filtering?

Key Takeaways

Neural networks are layers of interconnected nodes inspired by (but much simpler than) brain neurons
They learn by adjusting weights and biases to minimize errors on training data
Different architectures (CNNs, RNNs, Transformers) are specialized for different types of tasks
Their power comes from learning hierarchical patterns and handling complexity beyond explicit programming
They require substantial data and computing power, and their decisions can be hard to interpret
Neural networks are the foundation of most modern AI applications