Understanding Deep Neural Networks

May 21, 2019 • Shannon Flynn


People often hear about pioneering types of technologies that came about with help from deep neural networks, and how such technology has immense potential. But what is a deep neural network?

Getting to the heart of that matter requires learning about neural networks first. The content below refers to artificial neural networks (ANN), rather than the biological neural networks that exist in the brain.

Neural networks also facilitate machine learning, which allows a computerized system to learn from experience rather than from explicit and ongoing human programming.

What Is a Neural Network?

A neural network is a type of computer algorithm loosely inspired by a human or animal brain. Neural networks receive sensory data, then label it or arrange it into clusters.

Neural networks learn to interpret patterns and classify data accordingly. For example, a neural network could get trained on a data set containing labeled information.

Many neural networks get used to classify data. Then, the humans that teach neural networks how to operate do so with a process called supervised learning. It involves applying human knowledge to the dataset’s labels, thereby helping the neural network understand how to interpret them.

Some examples of data classification people have achieved with neural networks include identifying stop signs or license plates in images or recognizing sentiment in emails. But, as mentioned earlier, neural networks can also cluster data.

During clustering, neural networks handle unlabeled data, which comprises the majority of data in the world. Unlabeled data allows unsupervised learning to occur.

In that case, the neural network acts without guidance. Additionally, the more data a neural network gets during clustering training, the higher the potential accuracy.

Clustering looks for similarities within data. For example, people might use it to compare versions of documents. It can also spot anomalies. That’s one reason why banks often rely on neural network clustering for fraud detection. The neural network learns the typical characteristics of information, then gives alerts about differences.

What About a Deep Neural Network?

A neural network has a layer containing a node. The node is where computing happens, and a neural network has connected nodes. Some people call nodes neurons to further the connection between ANN and the human brain.

Deep learning neural networks got their name because they have several layers, and therefore, greater depth than conventional neural networks. A node activates in response to sufficient stimuli, and each connection between nodes has a “weight” associated with it that determines the value of the input information.

Deep neural networks function when information passes through several layers of nodes to achieve a multistep process of pattern recognition.

More Details About the Nodes of Deep Neural Networks

As mentioned in the previous section, deep neural networks require several layers to work correctly. If a neural network has at least three input or output layers, it’s of the deep neural variety.

The input layer receives input data, then progresses to the next layer, called the hidden layer. The hidden layers do mathematical computations on the data taken from the input layer. Finally, the output layer returns the results of the hidden layer’s findings.

A neural network for deep learning gets trained when the input layer recognizes a specific set of features. Next, a hidden layer’s conclusions help determine the training for the next layer’s input. That’s because all the information from the layers gets aggregated throughout the process. Nothing passes through the output layer until the data goes through all the hidden layers.

So, the deeper a neural network goes, the more features the nodes could recognize. And, when people refer to deep learning, that term concerns neural networks with two or more hidden layers.

Practical Applications for Deep Neural Networks

An earlier section gave some broad examples of how deep neural networks could apply to real-world needs. Looking closer at how people use deep neural networks helps explain why so many people find them fascinating.

Plus, during tasks like object or voice recognition, the artificial neural networks used for deep learning can perform better than humans.

During Hurricane Florence, weather experts relied on deep learning to predict the storm’s path. The system gave updates every hour instead of in the usual six-hour increments, which allowed weather forecasters to provide more accurate information and help residents prepare.

Deep neural networks can also assist physicians in diagnosing illnesses they may otherwise miss. Using technology like that helps patients start appropriate treatments faster.

People also interact with deep neural networks when they use YouTube. The company uses that technology to make content suggestions based on what individuals watched earlier.

Deep Learning Neural Networks Matter

Scientists are still investigating to what extent deep neural network progress could impact humanity and change time-tested processes.

One thing’s certain, however: Some impacts are already apparent.