Deep Learning vs Machine Learning: An Explanatory StudyOfficial
Machine learning and deep learning are two subsets of artificial intelligence which have garnered a lot of attention over the past two years. If you’re here looking to understand both the terms in the simplest way possible, there’s no better place to be. So if you’ll stick with me for some time, I’ll try to explain really is the difference between deep learning vs machine learning, and how can you leverage these two subsets of AI for new and exciting business opportunities.
Most people don’t realize that machine learning, which is a type of artificial intelligence (AI), was born in the 1950s. Arthur Samuel wrote the first computer learning program in 1959, in which an IBM computer got better at the game of checkers the longer it played. Fast-forward to today, when AI isn’t just cutting-edge technology; it can lead to high-paying and exciting jobs. Machine learning engineers are in high demand because, as upsaily MLE Tomasz Dudek says, neither data scientists nor software engineers have precisely the skills needed for the field of machine learning. Companies need professionals who are fluent in both of those fields yet can do what neither data scientists nor software engineers can. That person is a machine learning engineer.
The terms “artificial intelligence,” “machine learning” and “deep learning” are often thrown about interchangeably, but if you’re considering a career in AI, it’s important to know how they’re different. According to the Oxford Living Dictionaries, artificial intelligence is “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” Although they might be called “smart,” some AI computer systems don’t learn on their own; that’s where machine learning and deep learning come in. Let’s dive into our discussion of exactly what machine learning and deep learning are, and the ins and outs of machine learning vs. deep learning.
What is Machine Learning?
With machine learning, computer systems are programmed to learn from data that is input without being continually reprogrammed. In other words, they continuously improve their performance on a task—for example, playing a game—without additional help from a human. Machine learning is being used in a wide range of fields: art, science, finance, healthcare—you name it. And there are different ways of getting machines to learn. Some are simple, such as a basic decision tree, and some are much more complex, involving multiple layers of artificial neural networks. The latter happens in deep learning. We’ll get to that more in a minute.
Machine learning was made possible not just by Arthur Samuel’s breakthrough program in 1959—using a relatively simple (by today’s standards) search tree as its main driver, his IBM computer continually improved at checkers—but by the Internet as well. Thanks to the Internet, a vast amount of data has been created and stored, and that data can be made available to computer systems to help them “learn.”
Machine learning with R and machine learning with Python are two popular methods used today. While we won’t be discussing specific programming languages in this article, it’s helpful to know R or Python if you want to delve more deeply into machine learning with R and machine learning with Python.
What Is Deep Learning?
Some consider deep learning to be the next frontier of machine learning, the cutting edge of the cutting edge. You may already have experienced the results of an in-depth deep learning program without even realizing it! If you’ve ever watched Netflix, you’ve probably seen its recommendations for what to watch. And some streaming-music services choose songs based on what you’ve listened to in the past or songs you’ve given the thumbs-up to or hit the “like” button for. Both of those capabilities are based on deep learning. Google’s voice recognition and image recognition algorithms also use deep learning.
Just as machine learning is considered a type of AI, deep learning is often considered to be a type of machine learning—some call it a subset. While machine learning uses simpler concepts like predictive models, deep learning uses artificial neural networks designed to imitate the way humans think and learn. You may remember from high school biology that the primary cellular component and the main computational element of the human brain is the neuron and that each neural connection is like a small computer. The network of neurons in the brain is responsible for processing all kinds of input: visual, sensory, and so on.
With deep learning computer systems, as with machine learning, the input is still fed into them, but the info is often in the form of huge data sets because deep learning systems need a large amount of data to understand it and return accurate results. Then the artificial neural networks ask a series of binary true/false questions based on the data, involving highly complex mathematical calculations, and classify that data based on the answers received.
So although both machine and deep learning fall under the general classification of artificial intelligence, and both “learn” from data input, there are some key differences between Machine Learning and Deep Learning.
If you’d like to learn more specifically about deep learning, by the way, you can check out this Introduction to Deep Learning tutorial. It’s also worth learning separately about deep learning with TensorFlow, as TensorFlow is one of the most popular libraries for implementing deep learning.
5 Key Differences Between Machine Learning and Deep Learning
1. Human Intervention
Whereas with machine learning systems, a human needs to identify and hand-code the applied features based on the data type (for example, pixel value, shape, orientation), a deep learning system tries to learn those features without additional human intervention. Take the case of a facial recognition program. The program first learns to detect and recognize edges and lines of faces, then more significant parts of the faces, and then finally the overall representations of faces. The amount of data involved in doing this is enormous, and as time goes on and the program trains itself, the probability of correct answers (that is, accurately identifying faces) increases. And that training happens through the use of neural networks, similar to the way the human brain works, without the need for a human to recode the program.
Due to the amount of data being processed and the complexity of the mathematical calculations involved in the algorithms used, deep learning systems require much more powerful hardware than simpler machine learning systems. One type of hardware used for deep learning is graphical processing units (GPUs). Machine learning programs can run on lower-end machines without as much computing power.
As you might expect, due to the huge data sets a deep learning system requires, and because there are so many parameters and complicated mathematical formulas involved, a deep learning system can take a lot of time to train. Machine learning can take as little time as a few seconds to a few hours, whereas deep learning can take a few hours to a few weeks!
Algorithms used in machine learning tend to parse data in parts, then those parts are combined to come up with a result or solution. Deep learning systems look at an entire problem or scenario in one fell swoop. For instance, if you wanted a program to identify particular objects in an image (what they are and where they are located—license plates on cars in a parking lot, for example), you would have to go through two steps with machine learning: first object detection and then object recognition. With the deep learning program, on the other hand, you would input the image, and with training, the program would return both the identified objects and their location in the image in one result.
Given all the other differences mentioned above, you probably have already figured out that machine learning and deep learning systems are used for different applications. Where they are used: Basic machine learning applications include predictive programs (such as for forecasting prices in the stock market or where and when the next hurricane will hit), email spam identifiers, and programs that design evidence-based treatment plans for medical patients. In addition to the examples mentioned above of Netflix, music-streaming services and facial recognition, one highly publicized application of deep learning is self-driving cars—the programs use many layers of neural networks to do things like determine objects to avoid, recognize traffic lights and know when to speed up or slow down. To learn more about machine learning applications, check out this article.
A comparative analysis of Machine Learning and Deep Learning
Before I start, I hope you would be familiar with a basic understanding of what both the terms deep learning and machine learning mean. If you don’t, here are a couple of simple definitions of deep learning and machine learning for dummies:
A subset of artificial intelligence involved with the creation of algorithms that can modify itself without human intervention to produce desired output- by feeding itself through structured data.
A subset of machine learning where algorithms are created and function similar to those in machine learning, but there are numerous layers of these algorithms- each providing a different interpretation to the data it feeds on. Such a network of algorithms are called artificial neural networks, being named so as their functioning is an inspiration, or you may say; an attempt at imitating the function of the human neural networks present in the brain.
To help the ML algorithm categorize the images in the collection according to the two categories of dogs and cats, you will need to present it these images collectively. But how does the algorithm know which one is which?
Deep learning networks would take a different approach to solve this problem. The main advantage of deep learning networks is that they do not necessarily need structured/labeled data of the pictures to classify the two animals. The artificial neural networks using deep learning send the input (the data of images) through different layers of the network, with each network hierarchically defining specific features of images. This is, in a way similar to how our human brain works to solve problems- by passing queries through various hierarchies of concepts and related questions to find an answer.
Note: This is just an example to help you understand the differences in the way how machine learning basics and deep learning networks work. Both deep learning and machine learning are not actually simultaneously applicable to most cases, including this one. The reason for the same will be explained later as you read. So in that example, we saw that a machine learning algorithm required labeled/structured data to understand the differences between images of cats and dogs, learn the classification, and then produce output. On the other hand, a deep learning network was able to classify images of both the animals through the data processed within layers of the network. It didn’t require any labeled/structured data, as it relied on the different outputs processed by each layer which amalgamated to form a unified way of classifying the images.
- The key difference between deep learning vs machine learning stems from the way data is presented to the system. Machine learning algorithms almost always require structured data, whereas deep learning networks rely on layers of the ANN (artificial neural networks).
- Machine learning algorithms are built to “learn” to do things by understanding labeled data, then use it to produce further outputs with more sets of data. However, they need to be retrained through human intervention when the actual output isn’t the desired one.
- Deep learning networks do not require human intervention as the nested layers in the neural networks put data through hierarchies of different concepts, which eventually learn through their own errors. However, even these are subject to flawed outputs if the quality of data isn’t good enough.
- Data is the governor here. It is the quality of data that ultimately determines the quality of the result.
What we didn’t see in the example, but are important points to note:
- Since machine learning algorithms require labeled data, they aren’t suitable to solve complex queries which involve a huge amount of data.
- Though in this case, we saw the application of deep learning networks to solve a minor query such as this one. The real application of deep learning neural networks is on a much larger scale. In fact, considering the number of layers, hierarchies, and concepts that these networks process, they are only suited to perform complex calculations rather than simple ones.
- Both these subsets of AI revolve around data in order to actually deliver any form of “intelligence”. However, what should be known is that deep learning requires much more data than a traditional machine learning algorithm. The reason for this being that it is only able to identify edges (concepts, differences) within layers of neural networks when exposed to over a million data points. Machine learning algorithms, on the other hand, are able to learn through pre-programmed defined criteria.
So with that example and subsequent explanation of deep learning vs machine learning basics, I hope you would have understood the differences between both of them. Since these are layman explanations, I try my best to not introduce technical terms which are mostly incomprehensible to those looking to leverage AI and machine learning development for their business.
- If you’re a firm with boatloads of data to derive interpretations from.
- If you have to solve problems too complex for machine learning.
- If you can spend a lot of computational resources and expenses to drive hardware and software for training deep learning networks.
When to use Machine learning development for your business?
- If you’ve data that can be structured and used to train machine learning algorithms.
- If you’re looking to leverage benefits to AI to surge ahead of the competition.
- The best machine learning solutions can help in the automation of various business operations, including identity verification, advertising, marketing, and information gathering and help leverage great opportunities for the future.
Machine Learning and Deep Learning Future Trends
The possibilities for machine learning and deep learning in the future are nearly endless! The increased use of robots is a given, not just in manufacturing but in ways that can improve our everyday lives in both major and minor ways. The healthcare industry also will likely change, as deep learning helps doctors do things like to predict or detect cancer earlier, which can save lives. On the financial front, machine learning and deep learning are poised to help companies and even individuals save money, invest more wisely, and allocate resources more efficiently. And these three areas are only the beginning of future trends for machine learning and deep learning. Many areas that will be improved are still only a spark in developers’ imaginations right now.
So hopefully this Machine Learning Vs. Deep Learning article has given you all the basics regarding machine learning versus deep learning, and a glimpse at machine learning and deep learning future trends. As you may have figured out by now, it’s an exciting (and profitable!) time to be a machine learning engineer. In fact, according to PayScale, the salary range of a machine learning engineer (MLE) is $100,000 to $166,000. So there has never been a better time to begin studying to be in this field or deepen your knowledge base. If you want to be a part of this cutting-edge technology, check out Simplilearn’s Deep Learning course. And if you’d like a résumé-boosting credential to further your career in AI, sign up for the Machine Learning certification course.
You can also take-up the AI and Machine Learning certification courses in partnership with Purdue University collaborated with IBM. This program gives you an in-depth knowledge of Python, Deep Learning with the Tensor flow, Natural Language Processing, Speech Recognition, Computer Vision, and Reinforcement Learning.