Deep learning vs. machine learning – what are the differences?

Many people are suspicious of artificial intelligence. They don’t understand how computers can ‘learn’ and make intelligent decisions. Yet, the concept of AI can be understood by anyone.

Machine learning and deep learning are the two most important concepts in making AI possible. The two terms are often conflated, but they describe two fundamentally different methods with their own areas of application.


  1. Conceptual framework
  2. Deep learning vs. machine learning: What are the differences?
  3. Different areas of application

Conceptual framework

A pie chart representing machine learning and deep learning as subsections of artificial intelligence
Machine learning vs. deep learning: Both approaches belong to artificial intelligence. Deep learning can be considered a kind of machine learning.

Both machine learning and deep learning are subsections of artificial intelligence. Both approaches result in computers being able to make intelligent decisions. Deep learning, however, is a subtype of machine learning, as it’s based on unsupervised learning.

In both cases, this intelligence is limited to individual areas of application. We speak of so-called “weak artificial intelligence,” as opposed to “strong artificial intelligence,” which would have a human-like capacity to make intelligent decisions across many areas and situations.

Both technologies rely on large quantities of data being available for systems to learn from. That’s where the similarities end, though.

Deep learning vs. machine learning: What are the differences?

Historically speaking, machine learning is the older and simpler technology. It works with an algorithm that adapts when it receives human feedback. One requirement for making use of this technology is the availability of structured data. First, the system is fed structured and categorized data, and in this way, it understands how to classify new data of the same type. Depending on the classification, the system then carries out programmed activities. For example, it can distinguish whether a photo features a dog or a cat, and allots the files to their respective folders.

An initial application phase is followed by the optimization of the algorithm using human feedback – for this, the system is informed about any incorrect classifications and the correct categorizations.

With deep learning, structured data isn’t necessary. The system works with multi-layer neural networks that combine different algorithms that are modeled on the human brain. That’s why the system can also process unstructured data.

The approach is most suitable for complex tasks where not all aspects of objects can be categorized beforehand.

Important: In deep learning, the system finds suitable differentiation characteristics in the files by itself, with no need for any external categorization. In other words: training by the developer isn’t necessary. The system itself considers whether to change classifications or produce new categories based on new input.

While machine learning can already work with a manageable data pool, deep learning requires much more data. For the system to produce reliable results, more than 100 million data points should be available.

The technology for deep learning is also more costly to implement. It takes more IT resources and is significantly more expensive than machine learning, meaning that – for now, at least – it isn’t an option for mainstream businesses.

An overview of the differences between machine learning and deep learning

 Machine learningDeep learning
Data formatStructured dataUnstructured data
Data poolManageable data poolMore than a million data points
TrainingRequires human trainersSelf-learning system
AlgorithmA changeable algorithmNeural network made of algorithms
Field of applicationSimple routine activitiesComplex tasks

Different areas of application

Machine learning could be seen as a precursor to deep learning. In fact, all tasks that can be carried out by machine learning can also be processed by deep learning. It shouldn’t even be necessary to weigh up deep learning vs. machine learning.

Since deep learning requires significantly more resources, though, it isn’t an efficient procedure. The areas of application for both technologies are therefore clearly separated, and if machine learning can be used then machine learning will be used.

Using both technologies provides an enormous competitive advantage to companies, as both machine learning and deep learning are far from standard in the day-to-day business environment.

Areas of application: Machine learning

Online marketing: What marketing measures create results? Humans are generally not very good at surveying large quantities of data and delivering reliable estimations. This is where marketing analytics tools, based on machine learning, come in. These can evaluate existing data and make reliable forecasts as to the kind of content that would lead to conversions; what content customers want to read; and which marketing channels primarily result in a purchase.

Customer support:  Chatbots can be based on machine learning. They are oriented towards keywords included in the user’s query, and can guide customers to the information they are looking for through queries and yes/no questions in the dialog.

Sales: If it works for Netflix and Amazon, it can also be used in sales. Thanks to machine learning, systems can successfully predict which products and services existing customers might also be interested in. Here, the systems are able to provide very detailed recommendations which, in the case of large product ranges and highly customizable products, simplify sales.

Business intelligence: Machine learning can also be used to visualize important business data and to make forecasts easier to understand for the human decision-maker.

Areas of application: Deep learning

IT security: Unlike with machine learning, IT and cybersecurity systems that are based on deep learning not only recognize pre-defined dangers, but also new, hitherto unknown threats, as these are picked up as anomalies by the neural network’s pattern recognition. The effectiveness of security measures can be dramatically increased with the help of deep learning.

Customer support: Chatbots that are based on deep learning understand human language, and don’t rely on certain keywords being used. The dialog is much more efficient and the solution offered is more accurate.

Content creation: With deep learning, content creation can be automated. If enough content is available as a data pool, the system can create new content from it and perform translations autonomously.

Speech assistants: Digital assistants like Siri, Alexa, and Google are based on deep learning. In business contexts, too, the first speech assistants are now being used. For example, users can ask them in a natural way to place orders, send emails, create reports, or carry out research.

Beyond the areas of application listed here, both technologies can also be used in many more areas, such as in medicine, science, or mobility.

Deep Learning vs. Machine Learning – What’s The Difference?

To most people, the terms Deep Learning and Machine Learning seem like interchangeable buzzwords in the AI world. However, that’s not true. Hence, everyone who seeks to better understand the field of Artificial Intelligence should begin by understanding the terms and their differences. The good news: It’s not as difficult as some articles on the topic suggest.

What’s the difference between Deep Learning and Machine Learning?

Machine Learning means computers learning from data using algorithms to perform a task without being explicitly programmed. Deep Learning uses a complex structure of algorithms modeled on the human brain. This enables the processing of unstructured data such as documents, images, and text.

Understanding the difference between 'machine learning' and 'deep learning'
Machine Learning is a type of Artificial Intelligence. Deep Learning is an especially complex part of Machine Learning.

To break it down in a single sentence: Deep Learning is a specialized subset of Machine Learning which, in turn, is a subset of Artificial Intelligence. In other words, Deep Learning is Machine Learning.

But let’s dig a little bit deeper.

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‍What is Machine Learning?

Machine Learning is the general term for when computers learn from data. It describes the intersection of computer science and statistics where algorithms are used to perform a specific task without being explicitly programmed; instead, they recognize patterns in the data and make predictions once new data arrives.

In general, the learning process of these algorithms can either be supervised or unsupervised, depending on the data being used to feed the algorithms. If you want to dive in a little bit deeper into the differences between supervised and unsupervised learning have a read through this article.

A traditional Machine Learning algorithm can be something as simple as linear regression. For instance, imagine you want to predict your income given your years of higher education. In the first step, you have to define a function, e.g. income = y + x * years of education. Then, give your algorithm a set of training data. This could be a simple table with data on some people’s years of higher education and their associated income. Next, let your algorithm draw the line, e.g. through an ordinary least squares (OLS) regression. Now, you can give the algorithm some test data, e.g. your personal years of higher education, and let it predict your income.

While this example sounds simple it does count as Machine Learning – and yes, the driving force behind Machine Learning is ordinary statistics. The algorithm learned to make a prediction without being explicitly programmed, only based on patterns and inference.

So much about Machine Learning in general – to summarize:

  • Machine Learning is at the intersection of computer science and statistics through which computers receive the ability to learn without being explicitly programmed.
  • There are two broad categories of Machine Learning problems: supervised and unsupervised learning.
  • A Machine Learning algorithm can be something as simple as an OLS regression.

Let’s now examine how the term Deep Learning relates to all of this.

What is Deep Learning?

Deep Learning algorithms can be regarded both as a sophisticated and mathematically complex evolution of machine learning algorithms. The field has been getting lots of attention lately and for good reason: Recent developments have led to results that were not thought to be possible before.

Deep Learning describes algorithms that analyze data with a logical structure similar to how a human would draw conclusions. Note that this can happen both through supervised and unsupervised learning. To achieve this, Deep Learning applications use a layered structure of algorithms called an artificial neural network (ANN). The design of such an ANN is inspired by the biological neural network of the human brain, leading to a process of learning that’s far more capable than that of standard machine learning models.

Understanding deep learning means understanding neural networks
A simple artificial neural network

Consider the example ANN in the image above. The leftmost layer is called the input layer, the rightmost layer of the output layer. The middle layers are called hidden layers because their values aren’t observable in the training set. In simple terms, hidden layers are calculated values used by the network to do its “magic”. The more hidden layers a network has between the input and output layer, the deeper it is. In general, any ANN with two or more hidden layers is referred to as a deep neural network.

Today, Deep Learning is used in many fields. In automated driving, for instance, Deep Learning is used to detect objects, such as STOP signs or pedestrians. The military uses Deep Learning to identify objects from satellites, e.g. to discover safe or unsafe zones for its troops. Of course, the consumer electronics industry is full of Deep Learning, too. Home assistance devices such as Amazon Alexa, for example, rely on Deep Learning algorithms to respond to your voice and know your preferences.

How about a more concrete example? Imagine the company Tesla using a Deep Learning algorithm for its cars to recognize STOP signs. In the first step, the ANN would identify the relevant properties of the STOP sign, also called features. Features may be specific structures in the inputted image, such as points, edges, or objects. While a software engineer would have to select the relevant features in a more traditional Machine Learning algorithm, the ANN is capable of automatifeature engineering. The first hidden layer might learn how to detect edges, the next is how to differentiate colors, and the last learn how to detect more complex shapes catered specifically to the shape of the object we are trying to recognize. When fed with training data, the Deep Learning algorithms would eventually learn from their own errors whether the prediction was good, or whether it needs to adjust.

Read more about AI in business here.

What the job of a neuron in an ANN really looks like.
What the job of a neuron in an ANN really looks like.

Overall, through automatic feature engineering and its self-learning capabilities, the Deep Learning algorithms need only little human intervention. While this shows the huge potential of Deep Learning, there are two main reasons why it has only recently attained so much usability: data availability and computing power.

Firstly, Deep Learning requires incredibly vast amounts of data (we will get to exceptions to that rule). Tesla’s autonomous driving software, for instance, needs millions of images and video hours to function properly.

Secondly, Deep Learning needs substantial computing power. However, with the emergence of cloud computing infrastructure and high-performance GPUs (graphic processing units, used for faster calculations)  the time for training a Deep Learning network could be reduced from weeks (!) to hours.

But probably one of the most important advances in the field of Deep Learning is the emergence of transfer learning, i.e. the use of pre-trained models. The reason: Transfer learning can be regarded as a cure for the needs of large training datasets that were necessary for ANNs to produce meaningful results.

These enormous data needs used to be the reason why ANN algorithms weren’t considered to be the optimal solution to all problems in the past. However, for many applications, this need for data can now be satisfied by using pre-trained models. In case you want to dig deeper, we recently published an article on transfer learning.

To sum up:

  • Deep Learning is a specialized subset of Machine Learning.
  • Deep Learning relies on a layered structure of algorithms called an artificial neural network.
  • Deep Learning has huge data needs but requires little human intervention to function properly.
  • Transfer learning is a cure for the needs of large training datasets.

Learn more about ANN vs CNN vs RNN.

The main differences between Machine Learning and Deep Learning

This is a common question and if you have read this far, you probably know by now that it should not be asked in that way. Deep Learning algorithms are Machine Learning algorithms. Therefore, it might be better to think about what makes Deep Learning special within the field of Machine Learning. The answer: the ANN algorithm structure, the lower need for human intervention, and the larger data requirements.

First and foremost, while traditional Machine Learning algorithms have a rather simple structure, such as linear regression or a decision tree, Deep Learning is based on an artificial neural network. This multi-layered ANN is, like a human brain, complex and intertwined.

Secondly, Deep Learning algorithms require much less human intervention. Remember the Tesla example? If the STOP sign image recognition was a more traditional machine learning algorithm, a software engineer would manually choose features and a classifier to sort images, check whether the output is as required, and adjust the algorithm if this is not the case. As a deep learning algorithm, however, the features are extracted automatically, and the algorithm learns from its own errors (see image below).

Machine learning vs deep learning
The Deep Learning algorithm doesn’t need a software engineer to identify features but is capable of automatic feature engineering through its neural network. (Source:

Thirdly, Deep Learning requires much more data than a traditional Machine Learning algorithm to function properly. Machine Learning works with a thousand data points, deep learning oftentimes only with millions. Due to the complex multi-layer structure, a deep learning system needs a large dataset to eliminate fluctuations and make high-quality interpretations.

Find out more about Data Science vs ML vs AI

Got it. But what about coding?

Deep Learning is still in its infancy in some areas but its power is already enormous. It is mostly leveraged by large companies with vast financial and human resources since building Deep Learning algorithms used to be complex and expensive. But this is changing. We at Levity believe that everyone should be able to build his own custom deep learning solutions.

If you know how to build a Tensorflow model and run it across several TPU instances in the cloud, you probably wouldn’t have read this far. If you don’t, you have come to the right place. Because we are building this platform for people like you. People with ideas about how AI could be put to great use but who lack time or skills to make it work on a technical level.

I am not going to claim that I could do it within a reasonable amount of time, even though I claim to know a fair bit about programming, Deep Learning and even deploying software in the cloud. So if this or any of the other articles made you hungry, just get in touch. We are looking for good use cases on a continuous basis and we are happy to have a chat with you!