Everything You Need to Know About Neural Networks and More

Human beings are unique. We have the capacity for imagination, abstract concepts, and ability to empathize with others. The human brain is the powerhouse behind all of this. Because of its great power, technology has tried to emulate its capabilities, leading to artificial intelligence (AI) and neural networks. 

What is a Neural Network?

A neural network (also called an artificial neural network or ANN) is a mathematical attempt to simulate how the brain works, specifically in regard to neurons. “A neural network is created by programming regular computers to behave as though they are interconnected brain cells,” describes one Forbes article. This is accomplished through algorithms that “teach” computers by incorporating new data.

To be clear, neural networks are not a model of the human brain. It is an attempt to simulate how the brain works. Machines right now cannot compete with the inner workings of the human brain. Experts  surmise that the human brain houses approximately 86 billion neurons. These neurons transmit information from every part of the body, filling the brain with important information about the surrounding environment. A neural network is similar. It’s connected by many algorithmically-constructed “neurons” that make connections across a database or network. 

The primary goal of neural networks is to generate accurate predictions. These networks are trained to complete tasks, analyze information, label data, and make connections between sets of information received. In order for this to happen, developers must carefully take the time and effort to train the network and provide significant input for the network to learn. 

How do Neural Networks Work?

At the basic level, a neural network clusters information. The network generates an output based on an input. The output produced by a fully trained neural network is based on the conclusions the ANN reaches as it learns from similarities with the training data. 

In order for a neural network to generate an output, it must first be trained. This training allows the network to develop connections and eventually allows neural networks to self-learn based on the training data. Neural networks are trained in three stages: supervised learning, unsupervised learning, and reinforced learning. 

Supervised learning is the first stage. In supervised learning, the developer provides the network with the correct answer ahead of time, allowing  the network to arrive at the right answer on its own. For instance, one frequent use is handwriting recognition. In order to train a network to recognize various types of handwriting via this type of learning, a developer would provide the neural network with many samples of handwriting and identify the right answer ahead of time. This way, the network “learns” by examining the sample, drawing connections between variables, looking for similarities, and understanding the defining characteristics of each letter. 

The next step of training a neural network is unsupervised learning. In this stage, the network is no longer provided with the right answer or distinguishing labels ahead of time. Unsupervised learning forces the neural network to learn the inherent structure of the data. Returning to the handwriting example, during this stage a developer would give the neural network a large set of data and let it reach its own conclusions. This indicates to the developer whether the network needs improvement or a more diverse training data set to arrive at the proper conclusion. This approach is important because unlabeled data makes up the majority of data. According to the International Data Corporation (IDC), by 2025 80% of data throughout the world will be unstructured. The need for solutions that can analyze unstructured data will grow and as time progresses. 

The final step in training a neural network is reinforced learning. Here, the developer “rewards” the neural network for desirable outcomes and “punishes” it for incorrect outcomes. This solidifies accuracy and avoids blackbox AI solutions. The neural network can now apply what it has learned about a set of data and make predictions. This kind of network is employed in the music platform, Spotify. Spotify gathers user information as they listen to music. If a user listens to a particular artist, the streaming platform will use this data to suggest similar songs or artists the subscriber might also enjoy, automatically aggregating the collection into a Discover Weekly playlist. In order to do this, Spotify looks at the data on two levels: what the user has listened to historically and what other users with similar tastes listen to. While it’s difficult to account for personal taste, Spotify uses neural networks to provide subscribers with a variety of tailored music.

Wait, Isn’t This Just Like Deep Learning? What’s the Difference?

It’s true, neural networks and deep learning sound very similar, but they have a few key differences.

Both are subsets of AI and forms of machine learning. 


In deep learning, computers teach themselves to process and learn from information within databases. However, neural networks are trained to reach conclusions in light of training data. Deep learning resembles a neural network, but is slightly more complex in its architecture. To better understand the differences between the two, we must examine the differences in their architecture. 

The architecture of both neural networks and deep learning consist of multiple layers. In neural networks, the first layer sorts raw data into groups- collectively these groupings make up the input layer. The input layer leads to at least one hidden layer. Hidden layers form connections between the raw data groupings. A typical neural network will be comprised of only one or two hidden layers. Hidden layers result in an output layer that generates a single product or answer. Each layer consists of nodes (the “neurons”) that combine each input with a set of predetermined coefficients to “weigh,” or assign a significance to individual inputs. 

The structure for deep learning is similar, however deep learning incorporates more hidden layers. Deep learning uses its training data to make connections, but eventually draws conclusions on its own. These connections extend beyond the associations formed from training data. Neural networks have fewer hidden layers in their architecture and make connections based only on the training data. 

Structures of ANNs and deep learning programs can vary from the above outline based on the goals of the program and how many variables need to be coordinated. The Asimov Institute offers other  examples of neural network structures. 

What are Neural Networks Used For?

As we mentioned earlier, a big one is handwriting-to-text recognition. This helps businesses sort paperwork filled out by hand with greater ease. Human resources departments, among others, can use the network to translate handwriting from paperwork into text,  save the information, and file the paperwork accordingly.

LinkedIn is one major company that has implemented neural networks.  The networks detect potential abusive and spam content, and assist the company in understanding the types of content posted- advertisements, news, social updates, etc. This allows LinkedIn to generate recommendations for its users by identifying an individual’s preferred  content categories and relating it to additional similar content. 

Neural networks have applications in a variety of industries. In manufacturing, neural networks identify defects in products and optimize the supply chain. In the banking industry, these networks  detect fraudulent account activity, keeping patron information and assets safe. 

Neural networks also lend advantages in the retail industry. As Information Week points out, retailers use this type of network to track which products are purchased during a given time frame, which are commonly purchased together, etc, ultimately gaining an expanded product overview. This allows retailers to tailor product inventory and adjust marketing campaigns in light of these  connections. 
The field of AI continues to advance. If you want to learn more about the basics of AI, check out our blog post titled “Everything AI – The Basics and how to Integrate it into Your Business.” It provides a great overview of the field and addresses fears that many have about the future of AI. Shoot us a message with any questions and our expert team would love  to chat with you.

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