Understanding Deep Learning vs. Machine Learning

visual example of machine learning

The continued advancements of artificial intelligence (AI) have brought upon a sweeping question: What is deep learning vs. machine learning?

Is there a difference at all?


Are they closely related?

Double yes.

Deep learning is a specialized type of machine learning. More specifically, it’s an evolution of machine learning. They’re overlapping concepts and subsets of artificial intelligence. But make no mistake, there is a difference, and it’s important to understand the distinction between the two.

Machine learning is a data analytics technique in which the computer learns about the data directly instead of receiving a predetermined set of instructions. Conversely, deep learning is an advanced subset of machine learning. It’s essentially a neural network with three or more layers that allows machines to function similarly to the human brain.

Together, they play an integral role in the creation and innovation of artificial intelligence. Now that you’ve got a quick overview, let’s dig a little deeper into specifics.

Differences between machine learning and deep learning

What is Machine Learning?

Machine learning is a branch of computer science and a subset of artificial intelligence that dates back to the 1950s. It uses statistical strategies and algorithms to make a machine perform tasks without being explicitly programmed to carry them out. Machine learning is setting up a computer to make its own informed decisions.

The process begins with historical data, such as instruction or direct experience. That data is then observed and absorbed to look for patterns that allow the machines to make better decisions in the future. Over time, these machines need to be retained over time to ensure their quality of prediction does not gradually decline. The primary goal is to allow computers to be able to learn from the data without human assistance.

Data, in this context, encompasses a massive amount of stuff — words, images, clicks, numbers, symbols, etc. If something can be digitally stockpiled, it can be subjected to a machine learning algorithm. Without even knowing it, we’d bet you’ve interacted with a system powered by machine learning.

For instance, you most likely interact with Google multiple times daily. Google’s powered by machine learning! And so are recommendation systems like those on streaming services (Hello, Netflix! Hello, Spotify!), social media feeds (#Twitter), and your favorite household voice assistant, Alexa.

While you’re using these platforms, they’re collecting data to enhance your experience. Some “easy” datasets most often collected are the links you click, shows you watch, and common keywords in your conversations with Siri. With this new data, machine learning allows your experience to be more tailored to personal trends. How else do you think your “recommended for you” sections come about?

There are three types of machine learning:

  • Supervised learning
    • The supervised learning employs algorithms that are trained to learn relationships between a set of features (e.g., “red,” “round,” “has a stem”) and labels (“apple”), so they will be able to recognize and correctly label future observations. Supervised learning provides solutions to common issues, such as separating spam emails from your primary inbox.
  • Unsupervised learning
    • Unsupervised learning utilizes algorithms to discover hidden patterns and structures in huge amounts of data more quickly when compared to manual observation. Unsupervised learning is used in customer segmentation and recommender systems.
  • Reinforcement learning
    • Reinforcement learning is a performance-based method where machines operate through trial and error and are rewarded for positive behaviors and punished for negative behaviors. A good example of reinforcement learning in action is when training systems are created to provide personalized instruction and materials based on a student’s needs. There’s also deep reinforcement learning for more complex decisions and when algorithms are overwhelmed.

What is Deep Learning?

Deep learning is an advanced subset of machine learning, which is essentially a neural network comprised of three or more layers. These neural networks attempt to replicate the human brain by recognizing hierarchical patterns in an enormous amount of unstructured data.

While a neural network with a single layer can make predictions based on the data, it’s the additional layers that allow the data prediction to be optimized and filtered for precision. For instance, if you had images of several different types of fruits, and the goal was to categorize them by banana, apple, orange, grape, etc., deep learning determines the distinguishable characteristics of each fruit.

As the data is refined, deep learning algorithms acknowledge the features and filter them accordingly, which allows them to save the compiled data and make future predictions with greater accuracy.

Think of the multi-layered system as your senses: how you see, feel, touch, smell, and hear. The layers are representatives of each. Our brains trigger our sensors to react as an independent variable for one single observation. Parallel to the human process of analysis, neural network layers can be thought of as focusing on a different aspect of the object of study (like the fruit).

Behind every autonomous vehicle, website service chatbot, translation automation services, facial recognition, and the dancing robot dog is deep learning in motion. It’s key to voice control in devices like phones, TVs, hands-free speakers, and gaming systems. As artificial intelligence slowly becomes more relied upon in the modern age, deep learning allows society to achieve results even more impressive than the day before.

There are three deep neural network types most commonly used today:

  • Multilayer Perceptrons (MLP)
    • MLP is the most basic deep neural network. Data is fed to the input layer through hidden layers (neuron network) and into output layers where outcome predictions are made. They’re commonly used when making a person’s fitness approximations.
  • Convolutional Neural Networks (CNN)
    • CNN is designed to map image data to an output layer. For problems involving image data as an input, they are deemed extremely efficient and the preferred method.
  • Recurrent Neural Networks (RNN)
    • RNN uses sequential data or time-series data. These algorithms are commonly used for language translation or speech recognition

Wrapping Up

To recap:

  • Deep learning is a subset of machine learning — think of it as the Mewtwo of the programming world. Though both reside under the umbrella of artificial intelligence, deep learning is the “human brain” powering artificial intelligence.
  • Machine learning uses algorithms to analyze structured data points and make informed decisions independent of human intervention.
  • Deep learning is a network of multilayer algorithms that create an artificial neural network allowing the machine to think, react, and perform consciously and abstractly based on past experiences.
  • Without machine learning and deep learning, you’d never have those “I’ve never heard of this show, but Netflix recommended it and I love it!” moments.

With Our Help

Artificial intelligence is no longer just the exciting plot inspiration for a science fiction story. It’s real, it’s exciting, and it’s quickly becoming a major part of modern society. In addition, the job market for the AI industry is booming and won’t slow down anytime soon. Apply now and let Kenzie Academy set you on the path toward becoming a part of the artificial intelligence community!



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