What is the Difference Between Deep Learning and Machine Learning?
Published on May 19, 2020
While the terms deep learning and machine learning may seem interchangeable, there are some rather intricate and subtle differences between them. And these differences are worth understanding in an age of increasing technological dependence. An age in which companies need to know some lingo so they can begin to work with a 3PL such as Redwood Logistics to help them improve their tech integrations.
In fact, the two are distinctive enough to warrant labeling them separately. Yet another thing they have in common is that they both fall under the umbrella of artificial intelligence.
With that in mind, let's look at each term to get a wider understanding of what they mean before delving into the main variations.
The principals of basic machine learning date back to the proclaimed father of digital computing, Alan Turing, and were crucial in his success with electronic code-breaking. Peace-time computers then began to use similar concepts to aid the workforce of a multitude of industries.
The concept is simple; a technician sets the decision path for the computer, as well as instructing it on how to achieve the desired outcome by analyzing the data and making a decision. Over time, and with a lot of repetition, the computer can begin utilizing the data it has gathered to help it make new decisions that may simplify the task. Essentially, the computer or "machine" begins to make its own decisions.
Giving a strictly defined way to interpret data means that there is a focus on only the information the machine is instructed to see. This works best in situations where there is less data to sort and differences are essentially binary.
As technology advanced, so did the amount of processing power in cutting edge hardware. This advancement now allows computers to perform more intricate calculations at much higher speeds, which increases the overall potential of artificial intelligence.
Deep learning is the product of the development of increasingly better machines and almost magical programs that allow digital "brains" to have an understanding based on context. Where machine learning deals with rather strict "on rails" decision-making paths, deep learning is more about understanding a variety of content.
When it comes to deep learning, the only input needed from technicians is a large portion of information for the computer to sort through. With just a start and finish point designated, the machine is allowed to decide the best way to determine what is and isn't within those parameters with a complex arrangement of nodes and ACO style pathing. This network of nodes follows the design of a natural brain and the function is not much different... meaning that every machine learns differently.
What's the Difference
Now that we have looked at both systems, let's see the things that make each unique...
The two largest differences between machine learning and deep learning are:
the amount of human input needed to function
the equipment required to complete that function
Traditional machine learning processes can run effectively on most consumer-level devices without too much stress on the hardware. Deep learning, however, calls for much more memory and processing power depending on the complexity of the system.
Then there is the total amount of human interaction with the way the program filters data and ultimately reaches the desired outcome. Advanced deep learning can handle a vast amount of data with multiple variables while needing to be told only what result is expected of it, the process to achieve that goal is left up to the device. In contrast, machine learning systems must be told exactly how to make the correct decisions and will not deviate from the set path.
All Parts of the Whole
From hardware to the number of required man-hours and beyond, the differences between machine learning and deep learning certainly earn each a place of honor in the environments for which they are built.
That said, the new heights of understanding that deep learning can achieve is only possible because of the pioneering work performed via machine learning. In turn, deep learning is already beginning to inspire the next generation of advancement and pushing the envelope of artificial intelligence. So, while the two are not truly interchangeable terms, they compliment each other.