Has Deep Learning Made Traditional Machine Learning Obsolete?

Traditional Machine Learning

In simple terms, ML consists of a set of algorithms that make a machine intelligent by parsing through datasets, learning from them, and applying that knowledge to future processes. A good way to think about ML and deep learning is that all deep learning is ML, but not all ML is deep learning.

  • Netflix content recommendations
  • Siri or Amazon Alexa answering questions
  • GPS app traffic predictions

Today’s Advanced Deep Learning Techniques

On the other hand, deep learning — a subfield of ML — uses neural networks to create algorithms. For an algorithm to be considered deep learning, it must have at least three node layers.

  • Natural language processing (NLP)
  • Speech recognition software
  • Language translation services

3 Examples of Industries Still Using Traditional Machine Learning

Companies are still leveraging traditional ML mainly because these methods are well-understood. It’s easier to diagnose ML problems, train ML, and spend less time working on ML models compared to deep learning models.

Customer Service

Traditional ML became widely used in the customer service sector, especially with the major development of chatbots. It also helped the industry predict user behavior, manage its supply chain, and improve inventory management.

Aviation

Airlines often use traditional ML to gather aircraft performance and operations data and spot poor weather conditions and air traffic fluctuations. Another way ML can help is to ensure safety when shipping potentially hazardous chemicals.

Marketing

Because traditional ML can analyze vast amounts of data, marketers will leverage solutions to guide their marketing strategies. Marketers can now harness the power of big data and ML to learn about consumer behavior and trends, identify patterns in web traffic, and optimize advertising campaigns.

Will Deep Learning Replace Traditional ML?

Traditional ML solutions generally require less computing power and smaller data samples. Humans can understand ML outcomes easier than deep learning. Some experts suggest that deep learning is somewhat of a “black box” — a deep learning model’s results can only be interpreted rather than fully evaluated.

Looking at the Future of AI

Deep learning is a popular approach for many AI developers. However, traditional machine learning is still a modest first choice for many practitioners.

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