Artificial neural networks (ANN) are inspired by the human brain and are built to simulate the interconnected processes that help humans reason and learn. They become smarter through back propagation that helps them tweak their understanding based on the outcomes of their learning as well as from the enormous amount of data available today.

1. What Are Artificial Neural
Networks
A Super-Simple
Explanation For Anyone

2. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Title
Text
IntroductionIntroduction
What Are Artificial Neural Networks
A Super-Simple Explanation For Anyone
There are many things computers can do better than humans—calculate
square roots or retrieve a web page instantaneously—but our incredible
brains are still a step ahead when it comes to common sense, inspiration
and imagination. Inspired by the structure of the brain, artificial neural
networks (ANN) are the answer to making computers more human like
and help machines reason more like humans.

3. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
What are artificial neural networks (ANN)?
Human brains interpret the context of real-world situations in a way that
computers can’t. Neural networks were first developed in the 1950s to
address this issue. An artificial neural network is an attempt to simulate the
network of neurons that make up a human brain so that the computer will
be able to learn things and make decisions in a humanlike manner. ANNs
are created by programming regular computers to behave as though they
are interconnected brain cells.

4. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
How do artificial neural networks work?
Artificial neural networks use different layers of mathematical processing
to make sense of the information it’s fed. Typically, an artificial neural
network has anywhere from dozens to millions of artificial neurons—called
units—arranged in a series of layers. The input layer receives various forms
of information from the outside world. This is the data that the network
aims to process or learn about. From the input unit, the data goes through
one or more hidden units. The hidden unit’s job is to transform the input
into something the output unit can use.

5. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
The majority of neural networks are fully connected from one layer to
another. These connections are weighted; the higher the number the
greater influence one unit has on another, similar to a human brain. As the
data goes through each unit the network is learning more about the data.
On the other side of the network is the output units, and this is where the
network responds to the data that it was given and processed.
Cognitive neuroscientists have learned a tremendous amount about the
human brain since computer scientists first attempted the original artificial
neural network. One of the things they learned is that different parts of the
brain are responsible for processing different aspects of information and
these parts are arranged hierarchically. So, input comes into the brain and
each level of neurons provide insight and then the information gets
passed on to the next, more senior level. That’s precisely the mechanism
that ANNs are trying to replicate.

6. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
In order for ANNs to learn, they need to have a tremendous amount of
information thrown at them called a training set. When you are trying to
teach an ANN how to differentiate a cat from dog, the training set would
provide thousands of images tagged as a dog so the network would begin
to learn. Once it has been trained with the significant amount of data, it
will try to classify future data based on what it thinks it’s seeing (or
hearing, depending on the data set) throughout the different units.
During the training period, the machine’s output is compared to the
human- provided description of what should be observed. If they are the
same, the machine is validated. If it’s incorrect, it uses back propagation to
adjust its learning—going back through the layers to tweak the
mathematical equation. Known as deep learning, this is what makes a
network intelligent.

7. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
What are artificial neural networks used for?
There are several ways artificial neural networks can be deployed including
to classify information, predict outcomes and cluster data. As the networks
process and learn from data they can classify a given data set into a
predefined class, it can be trained to predict outputs that are expected
from a given input and can identify a special feature of data to then
classify the data by that special feature. Google uses a 30-layered neural
network to power Google Photos as well as to power its “watch next”
recommendations for YouTube videos. Facebook uses artificial neural
networks for its DeepFace algorithm, which can recognize specific faces
with 97% accuracy. It’s also an ANN that powers Skype’s ability to do
translations in real-time.
Computers have the ability to understand the world around them in a very
human-like manner thanks to the power of artificial neural networks.

8. © 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website
© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website

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