neural networks abstract

Everything You Need to Know About Neural Networks

December 21, 2020 - Emily Newton

Social media posts, high-res images, sound bites and sales figures. Thanks to the internet and new technology, like IoT devices, we generate and record data at a higher volume than ever before. Neural networks make it easier to process that vast amount of information and use it effectively.

For businesses and researchers, this data can be one of the most valuable resources available. The volume of information could help create new pattern-finding algorithms, optimize marketing strategies,  and generally provide a better understanding of the world. 

However, the collection of data at machine scale is too large for human analysts or conventional analytics to process. It’s typically unlabeled and unorganized. It could take a team of scientists years to sift through, prepare and analyze.

Machine learning allows developers to train an algorithm to produce an output similar to the training set.

Neural networks are a specific approach to machine learning. They train a model to analyze a data set and then generate new information or label a new input. 

These networks offer some major benefits and are some of the best available options for managing unstructured data. Here’s what they are, how they work and where businesses are using them right now.

Neural Network Principles — and Why Data Scientists Use Them

Neural networks group and classify information. They identify patterns to organize unstructured data or generate new info.

An artificial neural network isn’t really that similar to an organic one — like a brain — except in a somewhat superficial way. They also aren’t intended to replicate or simulate biological networks. It’s best to think about them as behaving similarly to the evolution of plants and animals.

By feeding new input data through multiple layers of neurons, the algorithm can effectively “learn” to do tasks. They can translate chunks of text, identify the subject of new images or even generate entirely new content, like music, writing and pictures. These networks do this without internal programing containing specific rules or frameworks.

When learning from a data set, the neural network attempts to find the configuration that will provide results in line with expected outputs. Over time, it will create its own rule sets or functions. It will modify and adapt its process until it can reliably produce more-or-less accurate input data results.

An example data set may include thousands of images of labeled objects — like cars, mugs, tables and people — at different angles, sizes and orientations. A neural network trained on this information would suggest potential labels for new images.

People can also use neural networks to uncover patterns in just about any kind of sufficiently large data set.

The Drawbacks of Neural Networks

Neural networks are too demanding for some applications. Even simpler ones may need significant time and computer resources to learn and produce new outputs. 

These networks are also sometimes referred to as “black box” models. While they are excellent at finding patterns, studying the structure of a trained system can’t explain why or how it arrived at the output it did. Fortunately, researchers are developing tools that make it easier to analyze how they mathematically create and represent concepts.

The quality of training data can also create problems. When a set isn’t comprehensive and representative of real-world conditions, it can lead a neural network to make mistakes or replicate bias. Imagine a collection of animal images that only contained birds and insects or a tissue sample database with only a few representatives of certain diseases.

Amazon ran into this problem with a machine learning algorithm that showed bias against women. It penalized resumes that included words like “women’s” or listed education at all-female colleges. This was likely due to the over-representation of men’s resumes in Amazon and Silicon Valley employees’ existing data.

The Construction of a Neural Network

Every neural network has three types of layers — the input, one or more hidden intermediates and an output.

  • The input layer contains the initial data for the network. 
  • The hidden layers are found between the input and output. This is where the actual computation is done. The work they do is not always apparent or distinct in the output, especially in more complex networks.
  • The output layer is made up of one or more nodes. They produce a result for the given input data — often, in the form of the likelihood of a certain label or outcome.

Each of these layers consists of multiple nodes, also called neurons or perceptrons. Each one is a distinct computational unit, joined to each node in the next layer via weighted input connections. This weight is how the neural network will lend more or less importance to a particular hidden calculation or piece of input data.

A single-layer neural network has, just as the name implies, one layer. Each of the nodes here is linked to outputs by weighted connections.  

Multi-layer neural networks generally provide more accurate predictions than simpler ones. However, they are also more expensive to train and run in terms of time and compute power.

How Neural Networks Find Patterns

Each part of a multi-layer network builds on and integrates the features from the previous one, generating more complex features. 

For example, let’s say a neural network starts generating an image of an animal. The first layer may start by creating individual clusters of pixels, similar to those found in pictures from the original data set. The next layer may use those clusters of pixels to recreate individual features. Next, it may move on to faces, then entire animals and backgrounds. 

Each node attempts to recreate the data set. Over time, the network learns more and creates rules and patterns that eventually allow it to make new, original data. For example, it can form a demand forecast or unique image — or label new inputs accurately.

The network learns the process of creating a hierarchy of features all on its own. It can do this by turning individual nodes on and off as needed and adjusting the weight of connections between them.

Every neural network has an activation function that determines if a given node will work based on how relevant it is to the input data. These functions are typically non-linear curves. They behave like logic gates that turn the node on or off. The function it chooses depends on a rule or threshold the network identified to help produce the desired output.

Certain special nodes are always active. These bias nodes allow a developer to effectively shift the activation function left or right so it can better fit the data.

Current and Future Applications of Neural Networks

Right now, businesses and researchers are already using a wide variety of neural networks and creating new ones to tackle emerging problems.

For example, Google researchers created a neural network to look through CT scans, searching for malignant tissues that can signal lung cancer. They said the algorithm was about as effective as radiologists at finding them.

Businesses also use neural networks for condition monitoring of heavy machinery in factories, as well as real-time translation of text and spam filtering.

Commercially available self-driving cars and autonomous drones may soon rely on neural network tech for navigation. Image-recognition networks can analyze video from onboard cameras in real-time — identifying what’s in front of the vehicle and telling the difference between road markings, other cars and pedestrians. 

The pace at which we create and collect data is likely to only accelerate in the future. Approaches to machine learning that handle unorganized, unlabeled information — like neural networks — will be key for putting that data to use.

Author

Emily Newton

Emily Newton is a technology and industrial journalist. She is the Editor in Chief of Revolutionized.

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