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:
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:
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:
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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:
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
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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Assoc. Prof., Dept. of Electronics Communication and Instrumentation Engg. (ECIE) Former HoD-EIE & Programme Head-ECI, KITS Warangal
2wWell put, Sucharitha