If what you do is in any way connected to artificial intelligence (AI), then you must have come across the terms Deep Learning and Machine Learning quite often. But unless you are an AI engineer or data scientist, you most likely have no idea what these terms mean, or how they differ from each other. The fact that people have a tendency to use them interchangeably doesn’t help either. In this article, we are going to explore what deep learning and machine learning are, as well as some related terms, and explain why they are different.
Topics covered
- What is Machine learning?
- What is Deep Learning?
- How are they different?
- Associated terms
What is Machine learning?
Machine learning (ML) is a type of artificial intelligence programming that was created in the 1950s. It focuses on building applications that can use data to learn and become more accurate over time, without additional programming. To put it another way, machine learning is the science of getting computers to process data, make assumptions and act on them, without explicitly being told what to do.
Machine learning programs are developed by machine learning engineers. It is their job to create programs and algorithms that teach machines to take action independently. Machine learning engineers are some of the most sought-after professionals in IT today—and some of the highest-paid—which should tell you something about how important machine learning is to our modern world.
To know more about machine learning engineers and other AI-related jobs, check out our other article titled 5 Popular Artificial Intelligence Jobs and the Companies that Offer Them.
Where is Machine Learning used?
One of the most popular uses of machine learning is in recommender systems, which is used extensively by Google. You must have noticed that whenever you search for something on Google, this has an impact on your recommendations on YouTube and other Google services. This happens because Google’s AI can learn from your searches and recommend content that would be suited to your interests. Amazon also uses a similar machine learning-based AI to tailor your shopping recommendations.
Machine learning is also used extensively by speech-to-text converters, computer games, image recognition software, and for medical diagnosis and predictive analytics. It is a building block of any AI-driven consumer product today.
RELATED: Difference between Artificial intelligence and Machine Learning
What is Deep Learning?
Deep Learning is a subset of machine learning that can be considered an advancement in the field. As IBM puts it, all deep learning is machine learning but not all machine learning is deep learning. Deep learning is about using multiple layers of analysis to extract higher levels of understanding from data.
Deep learning algorithms use what is known as an artificial neural network (ANN), that imitates the human mind, and helps computers process information the way a human brain would.
Where is Deep Learning used?
The most successful application of deep learning so far has been in large-scale automated speech recognition. Even though speech recognition software has been in development for decades, it was the advent of deep learning that helped it enter the mainstream. Alexa, Siri, Google Assistant, and pretty much every other AI assistant operate on speech recognition software developed using deep learning.
Outside of speech recognition, deep learning is also used in drug research, mobile advertising, computer gaming, robotics, and banking.
Deep Learning vs Machine Learning: How Are They Different?
When we talk about machine learning vs. deep learning, what we are really comparing is two different methods of reaching the same end goal. Hence, the frequent synonymization. That being said, there are some key differences between deep learning and machine learning:
Method of operation
Machine learning systems require human intervention to classify and hand-code data types. Though a machine learning program can learn to spot patterns within the data set, the same program cannot learn to identify data types as well.
A deep learning system can teach itself to identify data type as well, though this requires enormous amounts of data. It does this by using neural networks.
Hardware requirements
Machine learning systems have been around for over sixty years now, in which time the hardware capabilities of computers have become exponentially more advanced. But the hardware they require to function is still very minimal.
Deep learning systems, on the other hand, are a product of this advancement in hardware capabilities. Because they are required to process large amounts of data in order to function, the hardware also has to be much more powerful.
Learning methodology
Machine learning systems digest data in parts, creating multiple data points, and then combine these parts to come up with a solution. For example, if you wanted to identify a cat in a hat using machine learning you would have to do it in two stages—first, object detection, i.e., is there a cat; second, object recognition, i.e., wherein the hat is the cat?
A deep learning system would use neural net learning and data to return both cat and hat in one result.
Training Time
Machine learning systems can be trained in a short amount of time, a few seconds even!
Because deep learning systems require large amounts of data to be processed before they can learn to function independently; it takes longer to train them.
Associated terms
Since we are discussing deep learning vs machine learning, why not also take a look at some closely associated terms that get thrown around in such conversations:
Pattern recognition: Pattern recognition is a term that rose from electrical engineering in the 1970s and 1980s, to become an integral part of computer science. Pattern recognition is required to make computers perform intelligent and human-like tasks such as recognizing an object from an image.
Data mining: Data Mining, also known as Knowledge Discovery Process, is the process used to discover the properties of large sets of data. Data is examined and correlations are found within it. Based on these correlations, a hypothesis can be created to explain the data’s properties.
Artificial Neural Networks: As discussed earlier ANN, or simply neural nets are used in deep learning processes. Neural nets are inspired by human biology and the way neurons in the human brain interact with and pass information to each other. Neural networks are a big part of the science behind self-driving cars.
Conclusion
The modern world runs on machine learning. We depend on intelligent machines to assist us with everything from online shopping to healthcare. Traffic lights, internet searches, our national defense systems; all of them require some form of machine learning to stay effective. Deep learning represents the next big step in the evolution of our machine learning systems. Human beings generate staggering amounts of data every day and we need deep learning systems to process all of this data, leading to the development of increasingly more capable artificial intelligence. If you are interested in investing in the future of AI (and you really ought to be), this would be the place to start.
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