Deep Neural Network (DNN)

What Is a Deep Neural Network (DNN)?

A deep neural network is a neural network with three or more layers. The network is made up of artificial neurons connected by weights, which are numerical values determining how much influence one neuron has on another.

These neurons are designed to emulate the human brain and communicate with each other to process information in an attempt to build an artificial intelligence (AI) solution.

Table of Contents Table of Contents

1. What Is a Deep Neural Network (DNN)?
2. Key Takeaways
3. How Deep Neural Networks Work
4. Key Features of Deep Neural Networks
5. Types of Deep Neural Networks
6. Neural Network vs. Deep Neural Network
7. Deep Neural Network Uses
8. DNN Real-World Applications
9. Deep Neural Network Pros and Cons
10. The Bottom Line
11. FAQs

Show Full Guide

Table of Contents

1. What Is a Deep Neural Network (DNN)?
2. Key Takeaways
3. How Deep Neural Networks Work
Show Full Guide
4. Key Features of Deep Neural Networks
5. Types of Deep Neural Networks
6. Neural Network vs. Deep Neural Network
7. Deep Neural Network Uses
8. DNN Real-World Applications
9. Deep Neural Network Pros and Cons
10. The Bottom Line
11. FAQs

How Deep Neural Networks Work

Now we’ve explained what a deep neural network is, let’s look at how it works. DNNs are networks of neurons connected together by weights. Weights are parameters that define what type of influence the results or predictions of one neuron will have on another.

These networks consist of an input layer, multiple hidden layers, and an output layer, which perform tasks to process and extract data.

The input layer receives data as input, processes it, and passes it to the hidden layer(s), which in turn passes it to the output layer, where the final result or prediction is given to the end user.

Researchers will often train deep neural networks on large data sets, such as articles and books, to help build a deep learning model that can recognize patterns and use them to predict future outcomes.

Key Features of Deep Neural Networks

One of the key features of a deep neural network is its high level of accuracy. In machine learning (ML), a well-built deep neural network can identify patterns and model non-linear relationships between structured or unstructured data that a person or more rudimentary system cannot.

At the same time, an effective DNN will have the ability to learn from its mistakes, adjusting parameters to return more accurate outputs. While this doesn’t result in flawless decision-making (e.g. chatbots will hallucinate information,) it does help increase reliability.

Types of Deep Neural Networks

There are a range of deep neural networks in use today.

Some of these are as follows:

Neural Network vs. Deep Neural Network

Neural networks and deep neural networks have a distinct difference. A deep neural network is essentially a neural network with a larger number of hidden layers. For this reason it is considered to be “deep” in comparison to a shallow, less complex neural network.

Training deep neural networks is thus a more intensive process than training a standard neural network.

It’s worth noting that the added complexity of deep neural networks makes them much more computationally intensive than smaller neural networks. Though this can be advantageous because decisions made up of deeper neural networks can be more accurate.

Deep Neural Network Uses

Deep neural networks are used to enable a range of technologies including natural language processing, natural language generation (NLG), image generation, and computer vision.

These kinds of technologies are foundational to popular consumer services including ChatGPT, Google Search, Siri, and Amazon Alexa.

Virtually anything that involves processing a large data set and extracting insights from it can be automated with a neural network.

If we take ChatGPT as an example, you can ask the chatbot a question, and then a neural network will process your input and predict a relevant response based on its training data to provide you with an accurate answer to your question.

On the other end of the spectrum, cybercriminals can also use neural networks to conduct adversarial attacks on human targets, automating the creation of phishing emails and other threats.

DNN Real-World Applications

Some of the top real-world applications for deep neural networks include:

Deep Neural Network Pros and Cons

Using a deep neural network has a number of pros and cons:

The Bottom Line

Now we’ve outlined the definition of a deep neural network above, it’s important to note that these networks can come in all different shapes and sizes, depending on the use case they are designed for.

Neural nets designed to help a chatbot answer questions won’t be the same as one that’s used to help a robot navigate its environment. That being said, we can expect to see neural networks become more complex as a whole as AI researchers look to better equip their models to perform a wider range of tasks.

FAQs