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Machine Learning vs Deep Learning: Differences and Similarities

When talking about the difference between machine learning and deep learning, it’s best to start with this simple statement: all deep learning is machine learning, but not all machine learning is deep learning.


While there are many differences between the two subsets of AI, these are the biggest ones: time and speed, hardware complexity, amount of data required, and application.

Alexa can order groceries or play your favorite music, and Siri can remind you of important meetings and reply to emails. Although they come from different developers, both are digital voice assistants and advanced artificial intelligence (AI) solutions.

They are also clear proof that technology has become an inseparable part of our lives. It continues to evolve, making everyday activities easier, helping us analyze mountains of information, and speeding up lengthy processes.

But to take full advantage of your business data, it’s vital to invest in systems that can help you process it and produce actionable insights. As a result, data-driven companies – which treat data like a strategic asset – are set to dominate the business world in the coming years.

Read more: How to Build a Data-Driven Company: The Ultimate Guide

But even though many businesses use AI, machine learning, and deep learning as buzzwords meant to showcase their ingenuity, leaders still struggle to understand what they mean and how they bring tangible benefits to their company and customers.

This article will provide an overview of these three areas and highlight the key differences and similarities between machine learning and deep learning. So let’s jump right in.

What is AI?

Artificial intelligence is an area of computer science that aims to find ways to simulate human thinking in machines and computer systems. The goal is to emulate or even improve the thinking process, giving us access to a new level of technological advancement.

However, the story of AI is not a new one. It began after World War II when the British mathematician Alan Turing posed a seemingly simple question: is it possible for machines to think?

Today, there are four types of artificial intelligence: reactive machines, limited memory, theory of mind, and self-awareness. However, the last two are only theoretical and are (at least for now) limited only to the Hollywood big screen.

But how are AI, machine learning, and deep learning connected, and what are their differences?

What is Machine Learning?

Machine learning is a subset of AI with a specific objective: to create systems that can learn over time, based on data that they’re fed. In most cases, computers receive structured data, then use algorithms to analyze it, and act based on the gathered insights.

The structured data is input in the form of rows and columns, categorized in a way computers can work with it. Moreover, machine learning algorithms can instantly sort and respond to incoming data, without needing additional human assistance.

Therefore, machine learning is mainly based on self-reliance, but some subsets – like supervised and semi-supervised learning – need human intervention. Here, systems require data scientists to feed them with training data and provide an explicit model teaching it to respond to the input and classify it.

Unsupervised learning, on the other hand, uses unlabeled data and has the freedom to identify associations and patterns on its own.

Reinforcement learning is another important area of machine learning, where the system learns based on its own past experiences. It is mainly used for complex tasks with flexible, unpredictable, and large datasets. For instance, this subset of machine learning plays a significant role in robotics.

What is Deep Learning?

Deep learning is an advanced area of machine learning which mimics how parts of the human brain work to create layered complex internal representations of the outside world.

For example, deep learning is the basis of the future driverless car. Since the conditions on the road change constantly, you need an intelligent system to keep up.

To achieve that, data engineers build multi-layered, sophisticated deep neural networks (DNN), allowing data to remain highly connected while passing between neuron-like nodes.

Two types of deep learning algorithms are the most common:

  • Convolutional neural networks: These algorithms are designed to work with images. They scan every element on an image, looking for a feature or an object.
  • Recurrent neural networks: These algorithms introduce ‘memory’ to machine learning. As a result, the system ‘learns’ based on old decisions and data points, providing more context when evaluating new information.

Differences between Machine Learning and Deep Learning


When talking about the difference between machine learning and deep learning, it’s best to start with this simple statement: all deep learning is machine learning, but not all machine learning is deep learning.

While there are many differences between the two subsets of AI, these are the biggest ones:

Time and Speed

Machine learning systems are relatively simple to set up, requiring less time to be trained. In contrast, deep learning takes significantly more resources to set up and train, but for very complex problems (e.g. video analysis, object recognition) they provide greatly superior results

Hardware Complexity

Machine learning algorithms are less complex than deep learning and can run on conventional computers. On the other hand, deep learning systems require powerful equipment and more electricity to run.

Amount of Data Required

Both machine learning and deep learning systems can make use of structured and unstructured data. However, the difference is in the amount of data that they require to run properly, as deep learning often needs millions of data points whereas machine learning can work with only a thousand.


Even though you might not notice, machine learning is present in your everyday life. For example, it is the brain behind the suggested Netflix show you’re seeing or Facebook recognizing your friends on photos. On the other hand, deep learning is the basis of more autonomous and intricate systems, including fraud detection, self-driving cars, natural language processing, and visual recognition.


Authors and Contributors

Martin Bachler | Chief Technical Officer, NETCONOMY

As NETCONOMY’s CTO, Martin has invaluable technology and software engineering expertise which he’s happy to share with our clients to ensure they benefit from cutting-edge technological developments.

Nikola Pavlovic | Content Marketing Manager, NETCONOMY

Nikola is an experienced content and communication professional who believes that powerful storytelling is key for building brands, educating audiences, and designing marketing campaigns that deliver.