The Plain English Guide to Machine Learning vs. Deep Learning

The Plain English Guide to Machine Learning vs. Deep Learning

The business world often uses the terms machine learning, deep learning, and artificial intelligence as interchangeable buzzwords. How are Machine Learning and Deep Learning Related? According to a recent study, 40 percent of large businesses use NLP for tasks like data analytics and customer service. A deep learning algorithm is a subset of machine learning that stores massive amounts of data and sorts it into the right data set. Deep learning algorithms parse data to make informed decisions, serving as the basis of automation. Google’s AlphaGo Zero is another great example of deep learning. In particular, your marketing team will appreciate the opportunity to spend more time on ideation rather than tedium. There is no shortage of automation in marketing, and there are plenty of tools already available to solve almost any problem. These AI-generated headlines outperform humans 95 percent of the time, and the engagement rate of the content outperforms humans 100 percent of the time. Due to their shared reliance on data, these platforms become smarter, more accurate, and efficient over time while adding more value to the business cycle.

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The business world often uses the terms machine learning, deep learning, and artificial intelligence as interchangeable buzzwords. The problem? Each is uniquely different from its siblings. With so much terminology describing different pieces of the same AI puzzle, it’s easy to misunderstand various components.

AI has been around for decades in business and government, but it’s still a relatively new addition to many sectors. The lines between data science and machine learning begin to blur for those unfamiliar with the sector, but it’s increasingly important for professionals to understand this technology that’s changing our world.

For instance, Facebook uses AI to scan photos (as does Google) to match people and information with advertisers. Netflix uses this technology to recommend programming and drive its content decisions. You’d be hard-pressed to name a major brand that is not at least researching how to leverage and implement AI into its business model.

Before shopping around for solutions, it’s helpful to have some knowledge of the mechanics behind this seemingly magical technology.

How are Machine Learning and Deep Learning Related?

My team has worked with machine learning for the past two years. In fact, we were among the first developers to build an AI chatbot — ours is called ShoutOut — for Google Home. Our bot allows users to utilize verbal cues to dictate birthday cards to family and friends in about 60 seconds.

This software relies on powerful machine learning algorithms. We coded our chatbot to recognize names, phone numbers, and natural language messaging — it understands slang and contextual language, among other things. The more real-world data we fed the bot, the more feedback we could collect. Over time, the software learns and improves upon the results it gives.

Natural language processing (NLP) is a powerful segment of machine learning, enabling software to detect the nuances of human speech, both verbally and in text. According to a recent study, 40 percent of large businesses use NLP for tasks like data analytics and customer service.

Here’s where deep learning comes into play.

A deep learning algorithm is a subset of machine learning that stores massive amounts of data and sorts it into the right data set. As such, pattern recognition falls into the deep learning bucket.

Deep learning algorithms parse data to make informed decisions, serving as the basis of automation. Ever wonder why Netflix seems to predict the shows you’ll enjoy so accurately? Those recommendation…

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