Machine Learning vs Deep Learning: A Guide to Understanding the Differences and Applications
By now, it’s no secret that many consider the terms “artificial intelligence” and “machine learning” some of the most overused buzzwords in the industry — and with good reason. News about their capabilities — and related use cases — seems to emerge on an almost minute-by-minute basis. Without a doubt, artificial intelligence and machine learning are the driving forces behind a vast amount of innovation today, with applications that span across a wide range of industries. Thus, it can be difficult to keep up with ancillary terms — like deep learning — and distinguish the difference or see where exactly these technologies fit in a larger, digital advertising schema.
Adding to that growing list, deep learning is another term that’s on the rise this year. So what does that mean for you and your SEM program? This article provides a clear picture of the differences, and applications that distinguish machine learning and deep learning, and the unique capabilities they each bring to your digital advertising strategy.
What Is Machine Learning?
While it’s often used in conjunction, and sometimes interchangeably, with the term AI, machine learning is a subset of AI that comes with a slew of extremely valuable, sophisticated capabilities. In the most basic sense of the word, machine learning is exactly what its name implies: a computer with the ability to learn from whatever task it performs.
Artificial intelligence uses preprogrammed software or algorithms to deliver smart responses to queries and tasks, such as with popular voice assistants like Siri and Alexa. But here’s the distinction: machine learning has the ability to learn and improve at whatever task it’s assigned. Conversely, regular AI can never grow beyond what it’s preprogrammed to do. Thus, AI doesn’t include machine learning until the computer has the ability to process data inputs, learn from said data, and make changes to its responses based on related insights.
For example, you have a smart coffee maker that will automatically brew coffee when you say “Make coffee.” What if that coffee maker could learn from the things you tell it? Say you always tell it to “Make coffee” at 7 am Monday through Friday and at 9:30 am on the weekends. Thus, the coffee maker would be leveraging machine learning if it could use this information to change its programming and automatically start making coffee at those desired specific desired times of the day.
Perhaps surprisingly, most people don’t realize they interact with machine learning in their everyday lives. Take video streaming services for example. Netflix uses machine learning algorithms to analyze users’ viewing behavior, tailoring its offerings to the types of shows people like, then leveraging that information to recommend shows to similar users. Similarly, music streaming services like Spotify and Amazon’s recommended products are also examples of machine learning technology at work.
Altogether, there are four main types of machine learning possible today:
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforced Learning
That said, machine learning can only serve the same functions for which it was designed. A coffee maker that uses machine learning can get better at brewing coffee and anticipating when to brew that coffee. But it can’t spontaneously learn how to do something else, like automatically order your groceries when you run out. A different AI machine must be programmed to do that. From that perspective, the growth potential of basic machine learning computers is still fairly limited.
Now, enter deep learning.
What Is Deep Learning?
While deep learning may be a fresh technology buzzword, it corresponds to related machine learning and AI technologies. As mentioned, machine learning is a subset of AI. By the same token, deep learning is a subset of machine learning — specifically, a special way of implementing it that entails various unique capabilities.
At its core, deep learning uses a specific subset of machine learning algorithms designed to mirror the performance of a real human brain. Specifically, it leverages a structure of algorithms called an Artificial Neural Network (ANN), which incorporate three main layers — the Input Layer, the hidden layer and the output layer — designed to mimic human neural activity.
Thus, because of these capabilities, deep learning can draw correlations between data points and forming data clusters that inform understanding of the task at hand. Instead of making binary decisions based on basic data inputs, its sophisticated algorithms can draw conclusions from the data.
When applied correctly, deep learning has the potential to solve complex problems that would otherwise require human thought. With its ability to draw accurate conclusions, deep learning has the potential to successfully make difficult decisions that many engineers have tried to master with AI for years.
Machine Learning and Deep Learning: A Comparison
As we previously mentioned, machine learning and deep learning are not mutually exclusive. However, the algorithms and execution of deep learning equip it with features that are distinct from the rest of machine learning applications.
Here are some key differences:
General machine learning requires more human guidance
General machine learning often involves basic algorithmic processing guided by human input. Data provided is often structured and labled, and the processing task preassigned. A classic example of basic machine learning is image recognition. Google Image Search uses machine learning to identify web images and associate them with relevant keywords. Some images on the web already come with descriptive phrases. Basic machine learning uses this labeled data as a base to identify and categorize images with no description (unlabeled data).
Deep learning, on the other hand, is able to work entirely with unlabeled data, making relevant clusters and associations without being specifically preprogrammed to do so. Whereas a machine learning algorithm needs an engineer to make adjustments when it returns inaccurate results, deep learning has the potential to self-correct.
General machine learning requires less processing power
Even some of the most basic computers are capable of handing machine learning processing. Machine learning doesn’t have to be complex, it can use simple algorithms such as decision trees, random forests, and find-S to draw conclusions and achieve a task. The Artificial Neural Networks that make up deep learning algorithms are a different story. Deep learning involves large volumes of matrix multiplication operations, requiring sophisticated machines with lots of processing power for operation. Computers capable of deep learning also require a graphics processing unit (GPU) — a specialized electronic circuit designed to rapidly alter memory to accelerate the creation of images and results.
Deep learning offers more nuanced results
The layered algorithm structure of deep learning is what makes it possible to elicit nuanced decisions that are similar to those made by a human. What do people do when presented with new information? They refer back to their previous knowledge and experience to make sense of it. The layers of deep learning function in a similar way. The first layer processes a large data set related to a certain topic, drawing correlations and associations between a wide range of data points. It then uses this “past knowledge” to inform interpretation and action on data presented in subsequent layers.
Here’s an example: As mentioned, machine learning can look at a database of images (e.g. images of road signs) and use this information to identify other similar images. The layered structure of deep learning makes it possible to look at images of road signs and cluster them by type (e.g. stop signs, yield signs, speed limit signs, etc.). It can then use this back knowledge to identify blurry or partial images of road signs that regular machine learning might not be able to accurately categorize.
And another: Machine learning can analyze current news and categorize it by type or other factors. Deep learning can developing nuanced understanding of the qualities of current news and use this knowledge to accurately identify fake news designed to deceive readers. That’s something a lot of people can’t successfully discern!
Deep learning requires much more data
As mentioned, deep learning requires an initial layer of data analysis in order to deliver nuanced results. But in order to make accurate decisions, it requires more initial data to analyze than machine learning.
Both machine learning and deep learning have the potential to analyze enormous data sets to inform results. But machine learning is able to make sense of small sets of data as well — although the smaller a dataset, the more likely deep learning will make inaccurate associations and deliver poor results.
Applications of Machine Learning and Deep Learning
At this point, hopefully you have a better understanding of how machine and deep learning relate to each other and how they differ. That said, understanding the nuances of AI is not required to apply and benefit from these technologies. While AI and machine learning are already revolutionizing a wide variety of industries, deep learning touts its own potential to reshape how businesses and societies approach and execute on critical tasks.
Here are a few of the many current applications:
Machine learning and deep learning are uniquely suited to help automate and improve various cyber security processes. For example, computers can learn what normal website traffic looks like and effectively identify malicious traffic. Convolutional Neural Networks can also detect and classify malicious code, which can be incorporated as a critical component of a multi-layered strategy to improve the security posture of organizations.
Machine learning is a key component for driver-assisted or self-driving cars. By training algorithms using huge data sets, these machines have the ability to react to potential risks in their surroundings or avoid accidents. These computers are then able to replicate driver behavior based on previous “experiences.”
Probably one of the most impressive applications of machine learning technology is in the healthcare industry, which is already leveraging intelligent computers to help doctors accurately diagnose patients. These fast processing capabilities lead to quicker, better medical care for people, while lowering costs for hospitals.
Artificial Intelligence makes it possible to analyze large consumer datasets and drive unique audience insights, and businesses are already using it to discover new potential leads and deliver personalized marketing messages. Taking that up a notch, machine learning can cluster consumers based on demographic data, interests, and online behavior to help marketers identify new potential audiences to target.
Deep learning is already helping advertisers optimize their marketing spend and increase the relevancy of their ads, among other things. Deep learning algorithms can also be used to predict long-term advertising performance, and make automated adjustments to bidding strategies based on these insights. For businesses, this means they can automatically ensure they’re only bidding as much as they need to meet their advertising goals.
Other applications include:
- Image recognition
- Voice-activated assistants
- Machine translation
- Text generation
- Handwriting generation
- Adding sounds to silent movies
- Colorizing black and white film
- Predicting earthquakes
- Financial forecasts
The Bottom Line
Understanding the capabilities of machine learning and its subset deep learning is now more important than ever in digital advertising. Both are integral in achieving predictive advertising performance and automating bidding strategies based on these insights. As its name implies, machine learning can learn and adapt to the tasks it regularly performs. Deep learning takes that a step farther, emulating human brain activity that can not only make decisions based on provided data, but draw logical conclusions that inform next steps.
As technologies become more intelligent, the possibilities for their applications infinitely expand. But while marketers can leverage these technologies for a myriad of functions such as bidding, targeting, and optimization endeavors, so too can the competition. Thus, it’s likely that in the near future, machine and deep learning won’t be a luxury, but a necessity for digital marketers and advertisers to stay competitive and relevant with their audiences — paving the way for an even more intelligent and sophisticated set of technologies down the road.