Neural Networks Explained: A Beginner’s Guide

If you've ever felt overwhelmed by the term "neural networks", you're not alone. These systems are at the heart of modern artificial intelligence, yet they can seem mysterious or overly technical at first glance.

This guide—based on insights from the MIT xPRO Artificial Intelligence curriculum—is designed to help beginners build an intuitive understanding of artificial neural networks (ANNs): what they are, how they work, and why they matter.

What Is a Neural Network?

An artificial neural network is a computer-based system inspired by the way the human brain processes information. Just like our brains use neurons to transmit signals and make decisions, ANNs use artificial neurons connected in a network to interpret and learn from data.

These systems are capable of recognizing patterns, making predictions, and improving their performance over time—all by adjusting internal parameters during training.

What Is an Artificial Neuron?

An artificial neuron is a simple mathematical unit. Here’s how it works:

1. It takes multiple inputs (like features of an image or sentence).
2. Each input is multiplied by a weight, representing its importance.
3. These weighted inputs are summed and a bias is added.
4. This result is passed through an activation function (like ReLU, sigmoid) to produce the neuron’s output.

This process allows neurons to decide whether to activate or not, just like neurons in our brain!

Artificial Neurons vs. Biological Neurons

To understand neural networks, it helps to look at their biological inspiration.

In the human brain, neurons receive signals through their dendrites, process that information in the cell body, and pass signals along via axons to other neurons. Learning happens as the brain strengthens or weakens the connections between neurons—a process called synaptic plasticity.

Similarly, artificial neurons receive numerical inputs, process them using mathematical functions, and pass outputs to other neurons in the network. These connections have weights that determine their influence. Learning occurs by adjusting these weights based on the errors made during prediction, allowing the network to improve over time.

Both systems are designed to:

• Process inputs and produce outputs
• Learn from experience
• Recognize patterns
• Adapt to new information

Anatomy of an Artificial Neural Network

A neural network is made up of layers of connected neurons. Here’s a breakdown of its key components in simple terms:

• Input Layer: The entry point for data (e.g., image pixels, numerical values). Each neuron represents a feature or variable.
• Hidden Layers: Layers between the input and output where real computation happens. These layers extract and transform patterns in the data. The number of hidden layers depends on how complex the task is.
• Weights: Numbers that represent the strength of the connection between neurons. These get adjusted as the network learns.
• Bias: An extra value added to help the network make better decisions—even when inputs are zero.
• Activation Function: A mathematical function that decides whether a neuron should "activate" or not. This adds non-linearity, allowing the network to solve more complex problems.
• Output Layer: The final layer that produces the result (e.g., predicting “cat” or “not cat”).
• Loss Function: This compares the network’s prediction to the correct answer. The goal of training is to minimize this loss.

How Does a Neural Network Learn?

Learning in a neural network involves adjusting the weights so that predictions get closer to the actual answers. This is done through a process called gradient descent.

Here’s how it works:

1. The network makes a prediction using the current weights.
2. It calculates the error (how far off the prediction was).
3. It computes the gradient, which tells each weight how to change to reduce the error.
4. The weights are updated slightly.
5. This process is repeated many times until the network becomes accurate.

This method is used in tools like stochastic gradient descent and Adam optimizers, which help the network learn faster and more efficiently

Why Non-Linearity Matters

Without non-linear activation functions, no matter how many layers the network has, it would still behave like a simple linear model. Non-linearity—introduced through activation functions like ReLU or sigmoid—allows the network to model complex patterns such as handwriting, speech, or image features.

Key Takeaways for Beginners

• Neural networks mimic the brain, but work with numbers and functions instead of biology.
• A single neuron is a simple unit—it multiplies, sums, and activates. But when connected in layers, neurons can solve very complex tasks.
• Training is a trial-and-error process—make a guess, check it, and improve.
• The activation function is crucial—it brings flexibility and power to the system.

Final Thoughts

Neural networks are no longer just for data scientists or AI researchers. They're becoming essential knowledge for anyone exploring the world of artificial intelligence. With the right foundational understanding—like the one taught in MIT xPRO’s AI course—these concepts become much more accessible.

If you're just starting out, take your time and focus on understanding how one neuron works before moving on to full networks. With practice, the pieces will start to come together.

Let’s continue learning together.

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