Machine learning and deep learning are two subsets of artificial intelligence that have received a lot of attention over the past two years. If you are looking to understand the two terms here in the simplest possible way, there is no better place than this.
So if you've been with me for a while, I'll try to explain what's the difference between deep learning and machine learning, and how you can leverage these two subsets of AI for new and exciting business opportunities.
Deep Learning vs Machine Learning
Before I get started, I hope you have a basic understanding of what the terms deep learning and machine learning mean. If you don't, here are some general definitions of deep learning and machine learning for dummies:
Machine Learning for Dummies:
A subset of artificial intelligence associated with the creation of algorithms can modify itself without human intervention to produce the desired product - by feeding through structured data.
Deep learning for dummies:
A subset of machine learning, where algorithms are created and operated similar to machine learning, but there are many layers of these algorithms — each providing a different interpretation of the data it feeds. Such a network of algorithms are called artificial neural networks, you name them because their performance is an impulse; An attempt to mimic the function of human neural networks in the brain.
I've tried to put those definitions in the simplest way possible, but if this doesn't help you make any difference, here's an example.
Look at the picture above. What you see is a collection of pictures of cats and dogs. Now, you want to isolate images of dogs and cats with the help of machine learning algorithms and deep learning networks.
Deep Learning vs. Machine Learning Basics: When the problem is solved by machine learning:
To help classify the images in the collection according to the two categories of ML algorithm dogs and cats, you need to display these images collectively. What algorithm does it know?
The answer to this question, like the above definition of machine learning for dummies, is structured data. Label images of dogs and cats as you define specific characteristics of animals. The data is sufficient to learn the machine learning algorithm, and then it works on the labels it understands and classifies millions of other images of two animals according to the properties learned by the labels mentioned.
Deep Learning vs. Machine Learning: When the Problem is Solved by Deep Learning:
Deep learning networks take a different approach to addressing this issue. The main advantage of deep learning networks is that there is no need for structured/labeled data of images to classify the two animals. Using deep learning, artificial neural networks send input (image data) through different layers of the network, each network periodically defining the specific properties of the images. This is similar to how our human brain works to solve problems - by sending questions through different series of concepts and related questions to find answers.
Once the data is processed through layers in deep neural networks, the system will find enough identifiers to classify the two animals from their images.
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.
What have we learned here
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 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 that 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.
Now its time to hammer the final nail. When should you actually use Deep learning or machine learning in your business?
When to use deep learning?
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.
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