Machine Learning (ML) vs Deep Learning (DL)

The Best Explanation: Machine Learning vs Deep Learning

We’ve been tackling buzz words in the tech industry recently because there is a certain trend that occurs once a term is coined. Everyone uses the term without fully getting it and that causes misinformation, confusion, and sometimes fake news. In this piece we are looking at the terms machine learning and deep learning.

Every time a new tool or app is invented, a new word follows. So, let’s tackle two that have been flying around our heads for the past few years: Machine Learning (ML) and Deep Learning (DL). Techies, business gurus, and marketers love these words and throw them around whether or not they understand the differences.

The 100 Word Explanation

ML and DL have one core thing in common, they both relate to Artificial Intelligence (AI). Let’s start with simple definitions:

  • Machine Learning: Allows computers to learn on their own.
  • Deep Learning: Algorithms attempting to model high level abstractions in data to determine a high level meaning.

Going Deeper

The explanation above is the over simplified explanation of the three and helps those new to tech or confused with the jargon get it. In reality, it’s way more complicated than that and deep learning is by far the most confusing as it works with data, neural networks, and math.

“You should use a picture of Johnny 5 from Short Circuit, with the words ‘NEED INPUT’” — a Slack message I received when telling the team I was writing this piece.

Machine Learning

Machine learning analyzes data and crunches numbers, learns from it, and uses that to make a prediction/truth/determination depending on the scenario. The machine is essentially being trained, or really training itself, on how to perform a task correctly after learning from all the data it has analyzed. It’s building its’ own logic and solutions.

  • Linear Regression: Predicts the value of a categorical outcome with limitless outcomes, like figuring out how much you can sell a car for based on the market.
  • Logistic Regression: Predicts the value of a categorical outcome with a limited number of possible values, like figuring out if you can sell a car for a certain cost.
  • Classification: Puts data into different groups, like filing documents or emails.
  • Naive Bayes: A family of algorithms that all share a common principle, that every feature being classified is independent of the value of any other feature, like predicting happiness in photos of children.
“MFW I think about explaining Deep Learning” — Probably most of the Internet.

Deep Learning

Deep learning crunches more data than machine learning, that is the biggest difference. So, if you have a little bit of data, machine learning is the way to go but if you’re drowning in data deep learning is your answer. Deep learning algorithms are powerful and they need a lot of data to give you the best solution/outcome, but buyer beware. Deep learning algorithms need powerful machines, machine learning algorithms don’t.

The Take-Away

Machine learning and deep learning are two different things composed of the same common core of AI. They’re also good to use in different scenarios yet one should not be used over the other unless there is an absolute need.

“[Deep learning] AI is the new electricity.”

- Andrew Yan-Tak Ng, former chief scientist at Baidu

However, when using deep learning you will use machine learning as they overlap one another. Also, according to some researchers and data scientists once we figure out deep learning beyond the guessing game that it is now, it will most likely solve many of our everyday computer, business, AI, marketing, and other problems.

Kairos’ mission is to make it easy for any business to benefit from face analysis, enriching the experience between humans and machines, and being the premier partner for anything to do with facial recognition.

I simplify technical terms creatively for a living and write stories about dystopian societies for fun. I’m also a millennial writing about millennials.

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