Deep Learning vs. Machine Learning: What’s the Difference?

Deep Learning (DL) is a subset of Machine Learning (ML), and both are subsets of Artificial Intelligence (AI). Many people refer to deep learning and machine learning as AI simply because few people understand the distinctions in the various subsets and types. Think of AI as the machine equivalent to the human brain, and DL and ML as different parts of that brain with deep learning being embedded within machine learning.

All three, AI, ML, and DL are software, but they are unique types of software applications that work in entirely different ways than any other kind of software. People often see AI at work in a corporeal casing such as robots that take human or animal shapes. But AI is only the software running the casing, no matter what form it takes. The rest of the “body” is a casing that makes the AI mobile and gives movement to its functions. Since ML and DL are subsets of AI, they are software too.

ML and DL are becoming crucial in many aspects of human affairs. That’s because data has grown to such enormous scale that human minds are no longer able to sort, organize, and analyze all of it, or even a significant portion of it. By working with machine intelligence, humans can better understand and master even the most complex problems. Knowing the difference between ML and DL enables businesses and researchers to apply the best tool for the task at hand.

DL is a narrower and more specialized software application than ML. Both ML and DL are used in data analytics and automated decision-making. More specifically, DL creates a layered artificial neural network that “learns” from large data sets and is able to make decisions based on what it has learned. The key differences between DL and ML is how they process data and the type of learning of which they are capable. ML requires pre-processed structured data, while DL can ingest unstructured data.

ML uses algorithms and models to complete tasks and to autonomously learn, meaning it learns with no programming and little to no human supervision.

An algorithm is a process used on data to create an ML model. The process is pattern recognition. This is why training data must accurately represent the data the model uses going forward. Otherwise, the patterns it learned to recognize in the training data are not present in the new data set it is tasked to recognize and analyze.

Think of an ML model as the output of an ML algorithm. The model is saved after this process runs on a training data set and it contains the rules, data structures, and data structures that the ML then runs on other data sets.

A machine learning model like a self-improving computer program containing both the data and the procedures it needs to function.

The model can deliver information as its output for a human to use, or it can fuel any number of related automated actions. It is most often used to direct automated decision-making and subsequent actions. One example is how it processes a user’s online loan application and either automatically accepts or denies the application according to the rules the model has.

ML models have to be built anew if data changes so substantially that the model’s rules and functions no longer apply. This is called model drift, but it is more accurate to think of it as data drift.

ML is the most commonly used type of AI in most automation and other software categories.

DL differs from ML in that it more closely mimics how the human mind thinks. DL applications use layers of algorithms that collectively are known as an artificial neural network which render DL models. Similar to ML, DL models are the outputs of DL algorithms.

ML and DL have a common goal: to analyze large volumes of data, make automated decisions and predictions based on that data, and generate outputs that feed large scale automated actions.

The difference between DL and ML begins with the way they analyze data and extends to how much computing power and other logistics are required.

ML uses algorithms (processes) to output models which can be likened to computer programs. By comparison, DL uses a layered structure of algorithms collectively called an artificial neural network to produce DL models which are highly accurate.

DL’s layered structure of algorithms creates a learning system that far exceeds that of ML capabilities. Think of it as the machine mimicking both the human brain neural network and the human ability to keenly focus on the task.

However, some tasks don’t require such an intense and complex focus. For those, ML is more than adequate.

Most organizations are going to find themselves using both ML and DL in their day-to-day operations in much the same way that they find using software for documents and another software for spreadsheets to be equally advantageous, but for entirely different reasons and uses.

Neither ML nor DL require programming in the traditional sense. Developers do play a role, but it’s not similar to the role developers play working on other types of software.

Both ML and DL “learn” from training data which is data that is relative to the task and representative of the data the models use after training. Specifically, both find and note patterns within huge quantities of data. These processes are also incredibly fast, reducing time for such tasks from months, in some cases to mere minutes, or a few days. This creates incredible efficiencies over using human workers to complete the same tasks.

ML requires very little human intervention. DL learns entirely on its own. However, both must be routinely checked for accuracy in their outputs. AI professionals also have to guard against potential errors introduced via data such as unintended biases and poor representation of the problem to be solved.

ML learns over time by refining its pattern recognition capabilities via nuances it detects through repetition in analyzing similar or related data sets. For example, an ML model may continuously run analyses of sales data to produce personalized offers to different customer segments on a daily, weekly, or reward basis.

DL can use more diverse types of data such as videos, images, and unstructured data in ways that ML can’t, or can’t do easily. These capabilities make it ideal for complex use cases such as facial recognition or medical diagnosis from the results of healthcare tests.

Because it mimics human abilities, DL models make mistakes. Sometimes those mistakes are spectacularly wrong. So much so, that a young child could easily spot the error in the machine’s conclusions.

ML also makes mistakes and its models must be checked regularly to ensure that it is adhering to the business rules humans set forth for it, and that the data it uses has not shifted to the point of straying beyond the bounds of the task.

When DL works correctly, it is truly a wonder to behold. Many in the scientific and computing communities consider it a true marvel and the very backbone of AI.

ML scores several wins in business tasks as well. That too is nothing short of amazing in terms of both human and machine accomplishments.

It’s always best to align the tasks at hand with the strengths of specific tools. That applies to ML and deep DL too.

Some examples of applications where ML works well include:

• Voice assistants
• Chat bots
• Fraud detection
• Process automation
• Self-driving vehicles

Some examples of applications where DL works well include:

• Virtual assistants
• Visual recognition
• Natural Language Processing
• Supercomputers
• Vocal AI
• Cybersecurity
• Computer vision
• Facial recognition
• Rescue drones

As a rule, ML requires less computing power than DL. That also has implications on power usage and related resources such as cooling. To ensure you meet the requirements of either ML or DL, consider using high performance computing (HPC) suitable workstations, laptops, and/or cloud services. Standard business issue workstations and laptops are rarely up to the task. You may consider using cloud compute instances that support dedicated CPUs and GPUS and specialized ML and DL cloud services.

Machine Learning and Deep Learning are present in business domains that users interact with on a daily basis. As the disciplines of ML and DL grow, there are more services available that make it viable for businesses of all sizes to take advantage of these technologies.