Machine Learning and Deep Learning are Artificial Intelligence technologies that can be used to process large volumes of data to analyze patterns, make predictions, and take actions. While they're related to each other, they're not the same thing. They differ in important areas such as how they learn and how much human intervention they require.

Overall Findings

• Can make low/moderate complexity decisions

• Data features are defined by humans

• Accuracy improvements by system and humans

• Uses labeled or unlabeled data

• Does not use neural networks

• Requires moderate computer processing power, depending on model complexity and data set

• Can make decisions and take actions of high complexity

• Can discover and define data features on its own

• Accuracy improvements primarily made by the system

• Uses labeled or unlabeled data

• Uses neural networks of 3+ layers (but often 100+)

• Requires high computer processing 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 major areas of differentiation are how they do that and what is required from the people that create them.

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 (put another way, all Deep Learning is ML, but not all ML is Deep Learning).

Machine Learning Pros and Cons

• Best used for tasks that can be defined up front and don't require emergent learning

• Generally quicker to set up than Deep Learning
  

• Accuracy improves with repeated use
  

• Lower processing-power requirements than Deep Learning

• Less powerful than Deep Learning

• Less able to perform complex, ambiguous tasks

• Needs more ongoing human intervention for improvements
  

• Can take less-complex actions

Machine Learning systems, also called models, are trained by humans to use an algorithm to classify and analyze data, make predictions, and take actions of limited complexity.

ML programmers define the analysis algorithm for data processing the model will do, what patterns they look for in the data, and the characteristics or features, of the data the model will analyze. While Machine Learning systems improve the more data they analyze, the most significant improvements require human intervention.

Machine Learning has existed for decades and is a mature, widely used technology, especially in data-heavy industries like high-tech, financial services, e-commerce, and healthcare. Examples of ML models include content and product recommendations based on "people like you."

Deep Learning Pros and Cons

• Can perform significantly more complex tasks than ML

• Truly learns: Defines data characteristics on its own, without initial set up

• Much more able to improve without human input

• Can take complex actions independently

• Significant computer processing-power requirements
  

• Difficult to audit, explain, or regulate due to emergent learning

Deep Learning systems use an Artificial Neural Network (ANN) composed of multiple nodes or layers, each dedicated to performing a specific function in the system. Because of this construction and specialization, Deep Learning systems are complex and often contain 100+ layers.

Humans set up Deep Learning systems, but unlike ML models, they don't need to have the characteristics of the data they're looking for defined upfront. Instead, Deep Learning systems independently discover and define features in the data they analyze. This makes the findings from Deep Learning more emergent and allows these systems to find patterns or draw conclusions that their creators didn't know to look for in the first place.

While the concepts behind Deep Learning have existed since the 1980s, it's only in recent years that computer processors have become cheap and powerful enough to deliver Deep Learning systems.

An Example of Machine Learning vs Deep Learning

Imagine a system to recognize basketballs in pictures to understand how ML and Deep Learning differ. To work correctly, each system needs an algorithm to perform the detection and a large set of images (some that contain basketballs and some that don't) to analyze.

• For the Machine Learning system,before the image detection can happen, a human programmer needs to define the characteristics or features of a basketball (relative size, orange color, etc.). Once that's done, the model can analyze the photos and deliver images that contain basketballs. The more often the model performs this task, the better it should get. A human can also review the results and modify the processing algorithm to improve accuracy.
• For the Deep Learning system,a human programmer must create an Artificial Neural Network composed of many layers, each devoted to a specific task. The programmer doesn't need to define the characteristics of a basketball. When the images are fed into the system, the neural network layers learn how to determine the characteristics of a basketball on their own. They then apply that learning to the task of analyzing the images. The Deep Learning system assesses the accuracy of its results and automatically updates itself to improve over time without human intervention.

This example also helps demonstrate the correct applicability of technology to a task. Machine Learning is great for image detection, while Deep Learning is probably too powerful (and complex to set up and operate) for this kind of use. Deep Learning is better applied to more complex tasks. A Deep Learning system might be better built into an autonomous car's self-driving system and tasked with recognizing in real-time when balls are at risk of bouncing into the road and taking action in response.

• Do I need to use either of these technologies?

  You don't need to, but you probably do use them already. For example, when you search your phone's library for a person, that's machine learning in action. You didn't specify each person in your pictures, but the phone's machine-learning algorithm already worked to figure out who's in each photo.

• Which method is better?

  It depends on the situation. If there is just an enormous amount of data and plenty of power, the Deep Learning model is likely a better bet. But if the data set is finite, machine learning will likely work fine (i.e., identifying objects and people in the Photos app on an iPhone).