Understanding the latest advancements in artificial intelligence (AI) can seem overwhelming, but if it’s learning the basics that you’re interested in, you can boil many AI innovations down to two concepts: machine learning and deep learning.
Examples of machine learning and deep learning are everywhere. It’s what makes self-driving cars a reality, how Netflix knows which show you’ll want to watch next, and how Facebook recognizes whose face is in a photo.
Machine learning and deep learning often seem like interchangeable buzzwords, but there are differences between them. So, what exactly are these two concepts that dominate conversations about AI, and how are they different? Read on to find out.
Deep learning vs. machine learning
The first step in understanding the difference between machine learning and deep learning is to recognize that deep learning is machine learning.
More specifically, deep learning is considered an evolution of machine learning. It uses a programmable neural network that enables machines to make accurate decisions without help from humans.
But for starters, let’s first define machine learning.
What is machine learning?
Machine learning definition: An application of artificial intelligence that includes algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions.
How does machine learning work?
An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener’s preferences with other listeners who have similar musical tastes. This technique, which is often simply touted as AI, is used in many services that offer automated recommendations.
Machine learning involves a lot of complex math and coding that, at the end of the day, serves the same mechanical function that a flashlight, car, or computer screen does. When we say something is capable of “machine learning,” it means it performs a function with the data given to it and gets progressively better over time. It’s like if you had a flashlight that turned on whenever you said, “It’s dark;” it would recognize different phrases containing the word “dark.”
Machine learning fuels all sorts of automated tasks that span across multiple industries, from data security firms that hunt down malware to finance professionals who want alerts for favorable trades. The AI algorithms are programmed to constantly learn in a way that simulates a virtual personal assistant—something they do quite well.
The way machines can learn new tricks gets really interesting (and exciting) when we start talking about deep learning and deep neural networks.
What is deep learning?
Deep learning definition: A subfield of machine learning that structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own.
How does deep learning work?
A deep learning model is designed to continually analyze data with a logical structure similar to how a human would draw conclusions. To complete this analysis, deep learning applications use a layered structure of algorithms called an artificial neural network. The design of an artificial neural network is inspired by the biological network of neurons in the human brain, leading to a learning system that’s far more capable than that of standard machine learning models.
It’s a tricky prospect to ensure that a deep learning model doesn’t draw incorrect conclusions—like other examples of AI, it requires lots of training to get the learning processes correct. But when it works as it’s intended, functional deep learning is often received as a scientific marvel that many consider to be the backbone of true artificial intelligence.
A strong example of deep learning is Google’s AlphaGo. Google created a computer program with its own neural network that learned to play the abstract board game Go, which is known for requiring sharp intellect and intuition. By playing against professional Go players, AlphaGo’s deep learning model learned how to play at a level never seen before in AI and did so without being told when it should make a specific move (as a standard machine learning model would require).
It caused quite a stir when AlphaGo defeated multiple world-renowned “masters” of the game—not only could a machine grasp the complex techniques and abstract aspects of the game, it was also becoming one of the greatest players. It was a battle of human intelligence and artificial intelligence, and the latter came out on top.
For more practical use cases, imagine an image recognition app that can identify a type of flower or species of bird based on a photo. That image classification is powered by a deep neural network. Deep learning also guides speech recognition and translation and literally drives self-driving cars.
The difference between machine learning and deep learning
In practical terms, deep learning is just a subset of machine learning. In fact, deep learning is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different.
While basic machine learning models do become progressively better at performing their specific functions as they take in new data, they still need some human intervention. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine whether or not a prediction is accurate through its own neural network—no human help is required.
CX Maturity among SMBs of North America
Zendesk partnered with ESG Research to build a framework around CX maturity and CX success to help leaders at small and mid-sized businesses (SMBs) identify where they stand and build a roadmap for the future.
Let’s go back to the flashlight example: It could be programmed to turn on when it recognizes the audible cue of someone saying the word “dark.” As it continues learning, it might eventually perform that task when it hears any phrase containing that particular word. But if the flashlight had a deep learning model, it could figure out that it should turn on with the cues “I can’t see” or “The light switch won’t work,” perhaps in tandem with a light sensor.
A deep learning model is able to learn through its own method of computing—a technique that makes it seem like it has its own brain.
To recap, the key differences between machine learning and deep learning are:
- Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned.
- Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own.
- Deep learning is a subset of machine learning. While both fall under the broad category of artificial intelligence, deep learning is what powers the most human-like AI.
What are the different types of machine learning?
To dive a bit deeper into the weeds, let’s look at the three main types of machine learning and how they differ from one another.
1. Supervised learning
As you might have guessed from the name, this subset of machine learning requires the most supervision. A computer is given training data and a model for responding to data.
As new data is fed to the computer, a data scientist “supervises” the process by confirming the computer’s accurate responses and correcting the computer’s inaccurate responses.
For example, imagine a programmer is trying to “teach” a computer how to tell the difference between dogs and cats. They would feed the computer model a set of labeled data; in this case, pictures of cats and dogs that are clearly identified. Over time, the model would start recognizing patterns—like that cats have long whiskers or that dogs can smile. Then, the programmer would start feeding the computer unlabeled data (unidentified photos) and test the model on its ability to accurately identify dogs and cats.
2. Unsupervised learning
Supervised learning involves giving the model all the “correct answers” (labeled data) as a way of teaching it how to identify unlabeled data. It’s like telling someone to read through a bird guide and then using flashcards to test if they’ve learned how to identify different species on their own.
By contrast, unsupervised learning entails feeding the computer only unlabeled data, then letting the model identify the patterns on its own. This machine learning method is usually used in cases where it’s unclear what the results will look like, so you need the computer to dig through the hidden layers of data and cluster (or group) data together based on the similarities or differences.
For example, say your business wants to analyze data to identify customer segments. But you don’t know what segments exist yet. You’ll have to feed the unlabeled input data into the unsupervised learning model so it can act as its own classifier of customer segments.
3. Reinforcement learning
The reinforcement learning method is a trial-and-error approach that allows a model to learn using feedback from its own actions. The computer receives “positive feedback” when it correctly understands or classifies data and “negative feedback” when it fails. By “rewarding” good behavior and “punishing” bad behavior, this learning method reinforces the former. (And it differentiates reinforcement learning from supervised learning, in which a data scientist simply confirms or corrects the model rather than rewarding or punishing it.)
Reinforcement learning is used to help machines master complex tasks that come with massive datasets, such as driving a car. Through lots of trial and error, the program learns how to make a series of decisions, which is necessary for many multi-step processes.
What are the different types of deep-learning algorithms?
Machine learning can enable computers to achieve remarkable tasks, but they still fall short of replicating human intelligence. Deep neural networks, on the other hand, are modeled after the human brain, representing an even more sophisticated level of artificial intelligence.
There are several different types of deep-learning algorithms. We’ll examine the most popular models.
Convolutional neural networks
Convolutional neural networks (CNNs) are algorithms specifically designed for image processing and object detection. The “convolution” is a unique process of filtering through an image to assess every element within it.
CNNs are often used to power computer vision, a field of AI that teaches machines how to process the visual world. Facial recognition technology is a common use of computer vision.
Recurrent neural networks
Recurrent neural networks (RNNs) have built-in feedback loops that allow the algorithms to “remember” past data points. RNNs can use this memory of past events to inform their understanding of current events or even predict the future.
A deep neural network can “think” better when it has this level of context. For example, a maps app powered by an RNN can “remember” when traffic tends to get worse. It can then use this knowledge to recommend an alternate route when you’re about to get caught in rush-hour traffic.
Data as the fuel of the future
With the massive amount of new data being produced by the current “Big Data Era,” we’re bound to see innovations that we can’t even imagine yet. According to data science experts, some of these breakthroughs will likely be deep learning applications.
Andrew Ng, former chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project, shared a great analogy for deep learning models with Wired:
“I think AI is akin to building a rocket ship—you need a huge engine and a lot of fuel,” he told Wired journalist Caleb Garling. “If you have a large engine and a tiny amount of fuel, you won’t make it to orbit. If you have a tiny engine and a ton of fuel, you can’t even lift off. To build a rocket, you need a huge engine and a lot of fuel. The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.”
What machine learning and deep learning mean for customer service
Many of today’s AI applications in customer service utilize machine learning algorithms. They’re used to drive self-service, increase agent productivity, and make workflows more reliable.
The data fed into those algorithms comes from a constant flux of incoming customer queries, including relevant context into the issues that buyers are facing. Aggregating all that information into an AI application, in turn, leads to quicker and more accurate predictions. This has made artificial intelligence an exciting prospect for many businesses, with industry leaders speculating that the most practical use cases for business-related AI will be for customer service.
For example, machine learning and deep learning are both used to power natural language processing (NLP), a branch of computer science that allows computers to comprehend text and speech. In the CX world, Amazon Alexa and Apple’s Siri are two good examples of “virtual agents” that can use speech recognition to answer a consumer’s questions.
AI-powered customer service chatbots also use the same learning methods to respond to typed text. A great real-world example is Zendesk’s AI chatbot, Answer Bot, which incorporates a deep learning model to understand the context of a support ticket and learn which help articles it should suggest to a customer.
Machine Learning means computers learning from data using algorithms to perform a task without being explicitly programmed. Deep learning uses a complex structure of algorithms modeled on the human brain. This enables the processing of unstructured data such as documents, images, and text.What is the difference between machine learning and deep learning with example? ›
Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own. Deep learning is a subset of machine learning.Which is better ML or deep learning? ›
Machine learning applications are simpler compared to deep learning and can be executed on standard computers. Deep learning systems utilize much more powerful hardware and resources. 15. The results of an ML model are easy to explain.Which is harder deep learning or machine learning? ›
Machine learning programs tend to be less complex than deep learning algorithms and can often run on conventional computers, but deep learning systems require far more powerful hardware and resources.What is an example of deep learning? ›
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.Is CNN machine learning or deep learning? ›
A CNN is a kind of network architecture for deep learning algorithms and is specifically used for image recognition and tasks that involve the processing of pixel data. There are other types of neural networks in deep learning, but for identifying and recognizing objects, CNNs are the network architecture of choice.Should I learn machine learning before deep learning? ›
But, there are some machine learning concepts that you should be aware of before you jump into deep learning. It is not mandatory that you should learn these concepts first. You can also learn the majority of things on the go while doing deep learning. But having some machine learning experiences will help a lot.Why is it called deep learning? ›
Deep Learning is called Deep because of the number of additional “Layers” we add to learn from the data. If you do not know it already, when a deep learning model is learning, it is simply updating the weights through an optimization function. A Layer is an intermediate row of so-called “Neurons”.Is deep learning part of machine learning? ›
Deep learning is a subset of machine learning. It still involves letting the machine learn from data, but it marks an important milestone in AI's evolution. Deep learning was developed based on our understanding of neural networks.Why is deep learning not machine learning? ›
Machine learning algorithms work only on structured data. If the data is unstructured then humans have to perform the step of feature engineering. On the other hand, Deep learning has the capability to work with unstructured data as well.
Yes, if you're looking to pursue a career in artificial intelligence and machine learning, a little coding is necessary.What is deep learning in simple words? ›
Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data.How much math is required for machine learning? ›
Machine learning is powered by four critical concepts and is Statistics, Linear Algebra, Probability, and Calculus. While statistical concepts are the core part of every model, calculus helps us learn and optimize a model.Can you do machine learning in Excel? ›
Excel's data chart tool can create a machine learning model to predict changes in the values of data. It also has a feature called Trendline that can create a regression model from data. The trendline can be set to different types of regression algorithms like linear, logarithmic, exponential, and polynomial.What should I learn first AI or ML? ›
So, should I learn machine learning or artificial intelligence first? If you're looking to get into fields such as natural language processing, computer vision or AI-related robotics then it would be best for you to learn AI first.What are the disadvantages of deep learning? ›
It also falls short of general intelligence and multiple domain integration. Deep learning algorithms also counter the opacity or black box problem, making them hard to debug or understand how they make decisions. It also leaves users at a loss when it comes to understanding why certain parts fail.What is the examples of machine learning? ›
Image recognition is a well-known and widespread example of machine learning in the real world. It can identify an object as a digital image, based on the intensity of the pixels in black and white images or colour images.Why is CNN called convolutional? ›
Convolution is a mathematical operation that allows the merging of two sets of information. In the case of CNN, convolution is applied to the input data to filter the information and produce a feature map. This filter is also called a kernel, or feature detector, and its dimensions can be, for example, 3x3.Is CNN supervised or unsupervised? ›
2. Convolutional Neural Network. CNN is a supervised type of Deep learning, most preferable used in image recognition and computer vision.Can I do deep learning without machine learning? ›
1 Answer. Yes ,you can directly dive to learn Deep learning ,without learning Machine Learning but to make the process of understanding deep Learning at ease ,the knowledge of Machine learning will help you to have an upper hand in the field of Deep Learning.
If you want to go for research work then preferably the field of data science is the one for you. If you want to become an engineer and want to create intelligence into software products then machine learning or more preferably AI is the best path to take.How difficult is deep learning? ›
Understanding why/how those work, and how to generalise/build on them is real hard - the deep learning bit is easy. Similarly, there is a lot of exciting research on understanding why and how these deep neural networks really work. There is also a feeling in the field that low-hanging for deep learning is disappearing.How many layers is deep learning? ›
More than three layers (including input and output) qualifies as “deep” learning.Is deep learning considered AI? ›
Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling.What is CNN in deep learning? ›
Within Deep Learning, a Convolutional Neural Network or CNN is a type of artificial neural network, which is widely used for image/object recognition and classification. Deep Learning thus recognizes objects in an image by using a CNN.What are the three types of machine learning? ›
The three machine learning types are supervised, unsupervised, and reinforcement learning.Why is it called machine learning? ›
The term “machine learning” was coined by Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming. Samuel designed a computer program for playing checkers. The more the program played, the more it learned from experience, using algorithms to make predictions.What is the best language for machine learning? ›
- Python. Over the years, the use of Python has been growing steadily, overtaking popular languages like Java, C, C++, and C#. ...
- R. ...
- Java. ...
Deep learning is a subset of machine learning. It still involves letting the machine learn from data, but it marks an important milestone in AI's evolution. Deep learning was developed based on our understanding of neural networks.Is machine learning necessary for deep learning? ›
But, there are some machine learning concepts that you should be aware of before you jump into deep learning. It is not mandatory that you should learn these concepts first. You can also learn the majority of things on the go while doing deep learning. But having some machine learning experiences will help a lot.
An “intelligent” computer uses AI to think like a human and perform tasks on its own. Machine learning is how a computer system develops its intelligence. One way to train a computer to mimic human reasoning is to use a neural network, which is a series of algorithms that are modeled after the human brain.How does Netflix use machine learning? ›
Netflix uses machine learning algorithms to predict the viewer patterns and understand when there will be general increases and decreases in viewers of spikes in viewing a certain movie or show.What are the three types of machine learning? ›
The three machine learning types are supervised, unsupervised, and reinforcement learning.Why is it called deep learning? ›
Deep Learning is called Deep because of the number of additional “Layers” we add to learn from the data. If you do not know it already, when a deep learning model is learning, it is simply updating the weights through an optimization function. A Layer is an intermediate row of so-called “Neurons”.What is deep learning in simple words? ›
Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data.What comes first deep learning or machine learning? ›
In broad terms, deep learning is a subset of machine learning, and machine learning is a subset of artificial intelligence. You can think of them as a series of overlapping concentric circles, with AI occupying the largest, followed by machine learning, then deep learning.Can I study deep learning before machine learning? ›
Deep learning is a subset of machine learning so technically machine learning is required for machine learning. However, it is not necessary for you to learn the machine learning algorithms that are not a part of machine learning in order to learn deep learning.What are the disadvantages of deep learning? ›
It also falls short of general intelligence and multiple domain integration. Deep learning algorithms also counter the opacity or black box problem, making them hard to debug or understand how they make decisions. It also leaves users at a loss when it comes to understanding why certain parts fail.Does machine learning require coding? ›
Yes, if you're looking to pursue a career in artificial intelligence and machine learning, a little coding is necessary.What AI is not machine learning? ›
Machine learning is a subset of AI. That is, all machine learning counts as AI, but not all AI counts as machine learning. For example, symbolic logic – rules engines, expert systems and knowledge graphs – could all be described as AI, and none of them are machine learning.
Machine learning is only as good as the data it is given and the ability of algorithms to consume it. Going forward, basic levels of machine learning will become a standard requirement for data scientists. This being said, one of the most relevant data science skills is the ability to evaluate machine learning.Is machine learning AI? ›
Machine learning is an application of AI. It's the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own, based on experience.Does Siri use machine learning? ›
Siri relies on natural language generation, natural language processing, and machine learning in order to effectively operate and improve its performance over time.