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Deep Learning vs. Neural Networks: What Are the Differences and What Do They Do?

April 7, 2023 - Emily Newton

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Machine learning vs. artificial intelligence. Deep learning vs. neural network. It seems like technology is now advancing faster than we can wrap our heads around all the emerging concepts and terms. You may have wondered yourself about the difference between deep learning vs. neural networks. Are they separate technologies or do they work together? In this guide, you’ll learn the differences and what deep learning and neural networks are both used for.

Differences Between Deep Learning and Neural Networks

You can think of deep learning as a component of neural networks, although that’s still not a precise definition.

Picture a set of Russian nesting dolls. Label the largest one “artificial intelligence” (AI). AI is the endgame of machine learning, neural networks and deep learning. These components all support the eventual creation of a truly “alive” digital consciousness. Granted, it’ll be some time before technologists and philosophers agree on a crowd-pleasing definition of the word “alive.”

We’ll label the next nesting doll as “machine learning” (ML). If AI is supposed to be a machine made in the likeness of humankind, then machine learning is the process of how that machine learns from its past successes and failures.

The last two nesting dolls are “neural networks” and “deep learning.” Neural networks and deep learning, together, have a similar relationship to that of artificial intelligence and machine learning. Let’s dig into it a little deeper.

What Is a Neural Network?

The full name for this concept is “artificial neural network” or “ANN.” This technology is intended to mimic how the human brain functions using collections of algorithms. There are four fundamental components of artificial neural networks:

  • Inputs
  • Weights
  • Bias (or threshold)
  • Output

Neural networks take their name from neurons, the basic “computational unit” within the human brain. As the brain receives input from outer senses, it filters this information through layers of neurons to “process” it and produce useful output. Neurons respond to different kinds of stimuli and then release various neurotransmitters to elicit specific reactions.

This simplified explanation of how neurons work in the brain is a good visual of how neural networks operate.

Computing neural networks resemble those layers of neurons in the brain. However, each neuron is replaced by an algorithm to sift and interpret incoming stimuli instead of a biological construct.

What Is Deep Learning?

According to computing pioneers IBM, any neural network is engaging in deep learning if it’s “more than three layers” deep. The simplest possible deep-learning neural network would contain an input layer, one “hidden layer” of algorithms to process incoming information and an output layer.

The word “deep” in “deep learning” is a reference to the number of aforementioned “hidden layers” within a neural network. These nodes and layers are, collectively, referred to as a deep learning system. It also may be called a “deep learning system” or go by the name “hierarchical learning.”

How Are Neural Neural Networks Different From Deep Learning?

There are some additional distinctive characteristics of deep learning vs. neural networks worth unpacking. Here are a few areas where each type of structure excels and why one is sometimes a better fit for a particular application than the other:

  • Time to train: Neural networks typically take less time to train.
  • Accuracy: Advantage: Deep learning systems tend to be more accurate.
  • Intuition of user intent: Advantage: Deep learning systems have a better grasp of a user’s intentions.

So what are the applications of both neural networks and deep learning systems? While there’s some overlap, in many cases it’s clear one excels over the other or they need each other to work together. Typically, deep learning systems enhance the capabilities of an artificial neural network.

Here’s a quick breakdown of some of the industries and research-and-development areas where both computing models are helping humans make massive technological strides forward.

Neural Networks

  • Neural network applications: Excels at non-linear processes like pattern recognition, data mining, object inspection and classification, analysis, prediction of future events and clustering.
  • More neural network real-world examples: Virtual assistants, chatbots, real-time language translation, machine-vision systems, image colorization, facial recognition, personalization, quantum chemistry, weather and geoscience modeling and medical diagnoses.

Deep Learning Systems

  • Deep learning system applications: These also excel at pattern recognition, but they take neural networks’ capacity for non-linear processing further with natural language processing (NLP), pathfinding for robots and self-driving cars, logic and physics within video games, filtering and feed generation for social networks.
  • More deep learning system real-world examples: Facial recognition, stock market and trading predictions, defensive systems, including drone control, medical imaging analysis, handwriting analysis or signature verification, weather forecasting and machine condition analysis.

Deep Learning vs. Neural Networks: Valuable Together and Apart

While these systems are closely related, ultimately deep learning software is the more consequential and powerful innovation. A neural network can duplicate the computing process and structure of the human brain, but the quality of its conclusions will only be as accurate and relevant as the neural network is deep.

Deep learning systems, as they become more complex and sophisticated, will result in more complex neural networks. As these artificial neural networks grow deeper and “smarter” through the addition of more hidden layers, our capacity to create truly lifelike artificial intelligence will grow. One computing model empowers the other.

Can a computing model learn from past experiences — both triumphs and mistakes — just like a human brain? This is the very heart of humanity’s quest for artificial intelligence — to use machine learning concepts to better understand how the brain works and vice versa.

Revolutionized is reader-supported. When you buy through links on our site, we may earn an affiliate commision. Learn more here.


Emily Newton

Emily Newton is a technology and industrial journalist and the Editor in Chief of Revolutionized. She manages the sites publishing schedule, SEO optimization and content strategy. Emily enjoys writing and researching articles about how technology is changing every industry. When she isn't working, Emily enjoys playing video games or curling up with a good book.

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