Machine Learning and Deep Learning: What’s the Difference?

Machine learning and deep learning are artificial intelligence technologies that can be used to process large amounts of data to analyze patterns, make predictions, and take action. They are related but not the same. They differ in important areas, such as how they learn and how much they require human intervention.

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Overall findings

machine learning

Can make low/medium complexity decisions
Data features are defined by humans
Accuracy improvement by system and human
Work with labeled or unlabeled data
does not use neural networks
Moderate computational power required, depending on model complexity and dataset

deep learning

Can make decisions and perform highly complex actions
Ability to independently detect and define data features
Accuracy is improved mainly by the system
Work with labeled or unlabeled data
Use neural networks with 3 or more layers (but often 100 or more layers)
Requires high computing power, especially for systems with more layers

Machine learning and deep learning are similar in that they use computers to classify and analyze data and make predictions based on that analysis. The main areas of differentiation are how it is done and what is required of the people who create it.

Machine learning (ML) and deep learning are two areas of the larger field of artificial intelligence. Machine learning is a subset of AI and deep learning is a subset of ML (in other words, all deep learning is ML, but not all ML is deep learning).

Advantages and disadvantages of machine learning

advantage

Great for tasks that can be predefined and don’t require urgent learning.
Generally faster to set up than deep learning
Accuracy improves with repeated use
Lower processing power requirements than deep learning

Cons

Less powerful than deep learning
Decreased ability to perform complex and ambiguous tasks
More continuous human intervention needed to improve
Can perform less complex actions

Machine learning systems (also called models) are trained by humans to use algorithms to classify and analyze data, make predictions, and perform actions of limited complexity.

ML programmers define the analytical algorithms for data processing that the model performs, the patterns to look for in the data, and the characteristics of the data that the model analyzes. Machine learning systems improve the data they analyze, but the most important improvements require human intervention.

Machine learning has been around for decades and is a mature technology that is widely used, especially in data-intensive industries such as high tech, financial services, e-commerce, and healthcare. Examples of ML models include content and product recommendations based on “people like you”.

Advantages and disadvantages of deep learning

advantage

Can perform much more complex tasks than ML
True Learning: Define your own data characteristics without initial setup
much better without human intervention
Complex actions can be performed independently

Cons

Significant computing power requirements
Difficult to audit, account and regulate due to urgent learning

Deep learning systems use artificial neural networks (ANNs) that consist of multiple nodes or layers. Each node or layer is dedicated to performing a specific function within the system. This structure and specialization makes deep learning systems complex, often containing 100 or more layers.

Humans set up deep learning systems, but unlike ML models, they don’t need to predefine the characteristics of the data they’re looking for. Instead, deep learning systems independently detect and define features in the data they analyze. This makes the discoveries from Deep His Learning even more novel, enabling these systems to find patterns and draw conclusions that their creators never knew to look for in the first place.

The concepts behind deep learning have been around since the 1980s, but it’s only recently that computer processors have become cheap and powerful enough to deliver deep learning systems.

Examples of machine learning and deep learning

To understand how ML and deep learning differ, imagine a system that recognizes a basketball in a picture. To work properly, each system requires an algorithm to perform the detection and a large set of images (both with and without basketball) to analyze.

For machine learning systems, Before image detection can take place, a human programmer needs to define the properties and features of the basketball (relative size, orange color, etc.). Once that’s done, the model can analyze the photo and deliver an image containing the basketball. The more often the model performs this task, the better the model will perform. A human can review the results and even change the processing algorithm to improve accuracy.
For deep learning systems, A human programmer must create an artificial neural network consisting of many layers, each dedicated to a specific task. Programmers don’t need to define basketball characteristics. When an image is input to the system, the neural network layers learn how to uniquely determine basketball features. Then apply that learning to the task of image analysis. Deep learning systems assess the accuracy of results and automatically update to improve over time without human intervention.

This example also helps demonstrate that the technology can be applied correctly to the task. Machine learning is great for image detection, but deep learning is probably too powerful (and complicated to set up and operate) for this kind of use. Deep learning is well suited for more complex tasks. A deep-learning system could be incorporated into a self-driving car’s self-driving system to recognize in real time when the ball is in danger of hitting the road and take action accordingly.

FAQ

Should you use one of these technologies?

You don’t need it, but you probably already do. For example, machine learning works when you search for a person in your phone’s library. Although you haven’t named each person in the photo, your phone’s machine learning algorithms are already working to figure out who is in each photo.

Which method is better?

It depends. If you just have a huge amount of data and enough power, a deep learning model might be a better choice. But if the dataset is finite, machine learning will probably work better (i.e. identifying objects and people in the iPhone Photos app).