A Beginner’s Guide to Artificial Neural Networks – Part 1

August 17, 2020 - 7 minutes read

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The rapid rise of artificial intelligence (AI) development has made neural networks more ubiquitous than ever before. Even if you don’t work in data science or software engineering, you encounter this innovation regularly. Whether it’s chatbots, e-commerce platforms, personal finance, or medical imaging, artificial neural networks are now everywhere.

But what exactly is an artificial neural network? And what does it do? By now, you’ve probably heard that artificial neural networks are loosely based on how the human brain functions. While true, this only scratches the surface.

To help you understand what makes artificial neural networks so special, we’ve put together a comprehensive three-part series on this subject. In this first chapter, we’ll define what an artificial neural network is and delve into its relationship with our own natural neural networks.

What’s an Artificial Neural Network?

With terms like ‘artificial intelligence,’ ‘machine learning,’ and ‘deep learning,’ it’s easy to get confused. Fortunately, distinguishing between these different areas of study isn’t too complex once you get familiar with them. Let’s start with AI. This subset of computer science deals with the creation and operation of smart computer systems that can perform tasks as well as or better than humans.

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Machine learning’s a subset of AI and is somewhat self-explanatory; it focuses on teaching machines how to learn (you probably saw that one coming)! Essentially, it’s a data analysis method that automates the building of analytical models. If you feed a machine system the correct data in the correct way, it will learn from this information, recognize patterns, and can even make decisions without any human needed.

If we go further down the rabbit hole, we encounter deep learning. This subset of machine learning relies on the application of neural network architectures to generate outputs, make decisions, or solve problems. Basically, deep learning uses multiple algorithm layers to analyze a problem. It then produces a “probability vector,” which might declare something like this: “75% confident the object is a dog, 25% confident the object is a cat.”

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Deep learning has grown immensely in popularity over the last few years and is behind a resounding number of recent AI achievements. But it couldn’t have accomplished all of this without artificial neural networks. So, what are artificial neural networks? They’re algorithms comprised of interconnected, conceptualized “artificial neurons” that pass information between each other to process it and generate outputs which elucidate non-linear relations in the dataset.

The Relationship Between Our Brains and Artificial Neural Networks

The definition of artificial neural networks above probably leaves you with more questions than answers. To better understand it, let’s examine how natural neural networks in the human brain function. Neurons represent are a fundamental element of our brains; their coordinated activation makes learning, thinking, and every task you can imagine or do possible.

Think of neurons as the human brain’s processing unit. They take all of the sensory input data that your brain collects in real-time, process it, and output information that’s then used by other neurons. This data keeps getting passed to other neurons in the form of an electrical signal until a definitive outcome is obtained.

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Natural neural networks are connected by synapses; think of these as the end-points of a bridge between two neurons. The strength of a synapse determines your retention or understanding of information. When you practice a specific activity, such as walking, you strengthen the synaptic connections related to this action.

Our brains are remarkably efficient at processing micro-information thanks to numerous layers of neurons. Artificial neural networks strive to replicate this ability with their own layers of neurons. Each neuron has an associated weight that gets recalibrated based on the previous layer’s activity and experience. When the right combination of weight and data hits a neuron, its activation threshold is surpassed, and it fires. This combination of fired neurons is the process of learning.

Intelligence Is a Multi-Layered Phenonemon

An artificial neural network relies on layers of neurons to decipher large datasets and get more sophisticated outputs. The output is also causally related to the variables entered as input. This is why data cleaning is such an integral part of the process of creating good AI.

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Now that you’re more familiarized with artificial neural networks, let’s revisit our definition and make it a little more technical: An artificial neural network is a network that mimics the human brain by creating an artificial neural system through pattern-recognizing algorithms that learn from and interpret sensory data.

We hope you’ve enjoyed this brief overview of what an artificial neural network is and its relation to our own brains! From San Francisco to Beijing, artificial neural networks are one of the hottest topics in technology research. On top of the amazing innovations they’ve already brought us, neural networks are bound to play a vital role in shaping our future.

Stay tuned for the next two installments in this series, where we’ll dive deeper into how artificial neural networks function and examine a few of the most common applications for this technology. In the meantime, please let us know your thoughts on this article in the comments below!

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