- Neural networks are a subset of machine learning, which is a technique used to help computers learn using training that is modeled on results gleaned from large data sets. As such, neural networks are an attempt mimic human thinking, specifically how biological neurons are thought to signal to one another. The Google search engine, with its many interrelated nodes, is a good best example of a neural network. It is probably the largest in existence as it has the task of providing the instant and accurate results that users demand.
- Deep learning could be defined as a form of machine learning that is firmly based on AI neural networks. In some ways, deep learning can be viewed as advanced neural networks as it takes the basic capabilities of neural networks to a whole new level.
Also see: The Pros and Cons of Deep Learning
Neural Networks vs. Deep Learning
Neural networks and deep learning are often confused; the terms are sometimes used interchangeably in general AI discussion.
A good way to differentiate them: deep learning goes deeper than standard neural networks – hence its name. How? By implementing more layers within a neural network. This depth of analysis, then, involves more time, training, and investment.
Neural networks requires less time than deep learning
Neural networks, while powerful in synthesizing AI algorithms, typically require less resources. In contrast, as deep learning platforms take time to get trained on complex data sets to be able to analyze them and provide rapid results, they typically take far longer to develop, set up and get to the point where they yield accurate results.
Deep learning goes deeper
Many applications don’t need deep learning, with its ability to plumb the depths of AI’s capacity. Neural networks also offer powerhouse performance, though less so; yet increasingly, even basic AI tools are harnessing relatively simple neural networks as part of their operations. But as complexity rises, deep learning must be introduced to provide the expected level of performance and accuracy.
Neural networks need less investment
Basic neural networks require less financial outlay than deep learning, which needs far more processing power (such as Graphics Processing Units, often supplied by Nvidia), more expensive hardware and more advanced software.
Neural networks represent a major advance in AI technology. They are increasingly used in almost all the latest AI applications. Deep learning steps it up further but has a more limited set of applications. It typically requires more time and resources to set up and analyze but provides deeper and better conclusions, whereas neural networks solutions can often be arrived at faster as they are more narrowly defined and apply to a smaller data set.
Now let’s take a deeper look at neural networks and deep learning:
What is a Neural Network?
Neural networks are software constructs that are comprised of various layers such as input layers, hidden layers and output layers. Each node functions like an artificial neuron. It connects to other nodes and sends data to other layers of the network. They must be trained, must have their accuracy tuned, but offer the ability to classify data rapidly.
Neural networks are trained on data as a way of learning and improving their conclusions over time. As with all AI deployments, the more data it’s trained on the better. Neural networks must be fine-tuned for accuracy over and over as part of the learning process to transform them into powerful artificial intelligence tools. Fortunately for many businesses, plenty of neural networks have been trained for years – far before the current craze inspired by ChatGPT – and are now powerful business tools.
Once trained and tuned, neural networks can classify data and cluster it at incredible velocities. As a result, neural networks can take on complex AI tasks such as in speech and image recognition and complete them in minutes. Significantly, this rapid speed is still increasing, suggesting this already brief time period will fall to seconds.
Also see: Top Generative AI Apps and Tools
Neural Network Use Cases
Neural networks are now involved in more and more types of AI. This includes speech and image recognition, advanced search, and generative AI. In particular, the generative AI use cases are being adopted at a rapid pace by businesses.
There are so many accents, languages, idioms and dialects that the area of speech recognition has been a problematic area of technology for many years. AI backed by neural networks provides the processing and differentiating capabilities needed to minimize these challenges. What’s required is training, across time and many dialects; you’ll notice that more voice-based chatbots are now able to recognize more idioms and dialects.
Similarly, image recognition poses real difficulties due to the myriad of objects potentially on display. Neural networks help to increase accuracy and speed of recognition. Neural networks in combination with computer vision is fast becoming one of the most common uses of AI, as monitoring/safety devices are used in many public places.
Google has been using neural networks in search for ages. Others are adopting it due to its ability to provide answers rapidly and predict requests as a person is typing or speaking. The advantage here is that each node of a neural network is involved with the creation of a response, which sums the power of all the nodes to create a more powerful AI application.
When it comes to generating content, neural networks provide the support needed to provide articles, documentation and papers that will provide a good starting point for a human to then create a valuable piece of content. Neural networks are included in an overall generative AI architecture that enables remarkably fast and very powerful use of a large language model – this LLM is the source, but it’s the neural network that does a key part of the heavy lifting to create generative AI’s output. Specifically, neural networks identify the logical structures within a vast storehouse of data.
As an additional note about neural network use cases, realize that there are many different types of neural networks, of varying capability and scope and focus. But regardless of this enormous variety, all neural networks are applied to solving user problems and making the predictions needed by a wide range of uses cases.
Also see: 100+ Top AI Companies 2023
What is Deep Learning?
Deep learning systems use multiple processing layers to extract progressively better and more high-level insights from data. The key point is the “multiple processing layers,” which enables deep learning software architecture to provide far more robust compute capability.
Deep learning applications can be viewed as a more sophisticated deployment of basic neural networks that make heavy use of machine learning algorithms, are inspired by the human mind, can keep learning from their mistakes and solve highly complex problems.
Machine learning algorithms
Deep learning systems make use of complex machine learning techniques and can be considered a subset of machine learning. But in keeping with the multi-layered architecture of deep learning, these machine learning instances can be of various types and various strategies throughout a single deep learning application.
The mathematical structures that comprise deep learning have been loosely inspired by the structure and function of the brain. Meaning that humans employ a complex array of variables to make decisions, instead of the usual “on or off” nature of machine computing. The layered mathematical structures in a typical deep learning deployment are an attempt to build a system that can mimic the complexity and nuance of human decision making.
As they can learn by example and correct their actions based on errors detected, they keep learning and improving their level of accuracy. In the world of artificial intelligence, this is not unique to deep learning; by its very nature, AI is trained and “learns” as it is fed more data, and/or is in active use across time. Consequently, a deep learning application will be much higher performing six months from now than it is now.
Deep learning allows machines to tackle problems of similar complexity to those humans can solve.
Thus, deep learning has enabled researchers to scale up the AI models they use in a way that goes well beyond traditional neural networks. By utilizing multiple forms of machine learning systems, models, neural networks and algorithms, deep learning opens many new doors for analysis and problem solving.
“Deep learning can be leveraged to analyze the exceptions when a human intervenes with AI decisions,” said Rick Wagner, Senior Director, Product Management, SailPoint. “Those exceptions can ultimately be analyzed for patterns which ultimately will improve the effectiveness of AI.”
Also see: Generative AI Examples
Deep learning Use Cases
Deep learning use cases go way beyond those of machine learning and simple neural networks. Machine learning is broadly applicable to a huge range of tasks. As the name implies, deep learning is harnessed to solve problems at a deeper and more complex level. Deep learning is being used to generate text, automatically deliver meeting transcripts, capture data from documents and generate video content from text.
Deep learning-based, large language models can generate legible text on various topics or generate realistic images from text prompts. The use of LLM and generative AI application involving text creation is now being widely adopted and therefore is a key driver of deep learning adoption. Deep learning is also being used to provide high-accuracy text transcripts from audio recordings of business meetings and phone calls.
Automatic data capture
Deep learning can be deployed to automatically capture data from business documents with high accuracy. This can be an important part of how deep learning boosts the performance of data analytics, which is happening with increasing frequency in the enterprise. Indeed, while deep learning in data analytics is still on the forward edge, it will certainly gain full saturation as AI gains more adoption.
“Deep learning is being used to automatically capture data from business documents with high accuracy,” said Petr Baudis, CTO and chief AI architect at Rossum. “This can save businesses a lot of time and money, as it eliminates the need for manual data entry.”
Deep learning models are being employed to solve the many challenges inherent in autonomous operation of vehicles. Whether it is self-driving vehicles, vehicle driver assist, obstacle avoidance or equipment that moves around industrial and commercial operations, deep learning is being deployed to ensure safety and improve accuracy.
In sum, deep learning use cases provide multi-faceted answers to complex situations and problems. It elevates traditional machine learning and basic neural networks in terms of scale and depth of analysis.
For more information: AI vs. ML
Bottom Line: Neural Networks vs. Deep learning
There are many similarities between neural networks and deep learning. They each comprise algorithms that are addressed to decode complex challenges.
Deep learning, though, utilizes more sophisticated models than do neural networks and takes longer to set up. Deep learning requires more time to crunch through the much larger data sets and more nuanced problems they typically analyze or address.
As such, deep learning is deployed among a much smaller user base due to the time and cost required to build and run its systems.
In sum, neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced use cases.
With generative AI very much in the spotlight, it should be pointed out that new applications like ChatGPT and others make heavy use of neural networks. In an increasing number of cases, this involves the very advanced neural networks that can be classified as deep learning. To create content that passes muster, after all, a vast amount of compute resources are required to develop something that is even vaguely comparable to the work of a skilled human.
“When it comes to AI in general, neural networks and deep learning go together, the deeper the learning, the more layers of neurons, the more trained a model can be to enable deployment for different purposes,” said Greg Schulz, an analyst at StorageIO Group. “Think of it as a hierarchy of cognitive computing: basic AI involves relatively simple rules and reasoning; more advanced machine learning adds to the knowledge basis and deep learning takes you to the creation, training and testing of new or enhanced models.”
Also see: Generative AI Startups