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What is the difference between machine learning and deep learning?
It is not unusual to encounter the terms machine learning and deep learning in the context of data science. The way they are used is often the same, and sometimes they can even mean the same thing.
This often leads the inexperienced reader to confuse the true interpretation of these two terms.
This article aims to clarify the two terminologies, so that the reader can have a precise understanding of what machine learning and deep learning are, respectively.
Introduction: some definitions
To better understand the differences between machine and deep learning, we must first understand what these two disciplines actually are and in what context they reside. This is especially important for those who want to orient themselves in the field and are just starting their journey into the world of data analysis.
Every discipline mentioned in this article is part of the field of artificial intelligence. This is in turn part, as many will know, of computer science. As a result, both machine learning and deep learning are part of AI first and then CS.
Computer science is a very broad scientific discipline, so we will only focus on artificial intelligence because that is where we operate as analysts.
Artificial intelligence is a very broad field. Machine learning and deep learning are just two of the most known disciplines at the moment.
- the creation of search and optimization algorithms
- creation of logical, perceptual, learning and planning structures
There are many others, and depending on the level of detail we want to consider these can be expanded to integrate other groups.
As a result, machine learning and deep learning, being part of AI, touch and influence the disciplines mentioned above and so do the latter.
To visualize this hierarchy, let’s consider this image
Surely a robotics expert will use machine learning or deep learning to teach the machine to achieve a goal. In this case, he will likely use reinforcement learning, another branch of machine or deep learning.
Let’s focus now on the differences, formalizing a set of readable bullet-points.
Definition of Machine Learning
Machine learning is, as we have mentioned, a branch of artificial intelligence which in turn is a branch of computer science.
Machine learning allows a machine to perform tasks without being specifically programmed to do it. Machine learning therefore becomes one of the most interesting and powerful solutions to automate a task.
In fact, this is one of the reasons that led data scientists to be prominent figures in the workplace — they are in fact enablers of automation.
Data scientists are enablers of automation
Thanks to machine learning, an analyst can potentially consider any task and integrate a level of automation into it so that they can
- help speed up execution times
- reduce human error
- allow to scale the business on multiple fronts
Each of these points is fundamental in a business context, as they have a direct impact on the economic aspect.
You can read in detail what machine learning is, with examples and applications at the following link, where I write about the topic in detail for those who want to approach the field.
Definition of Deep Learning
Deep learning is part, as we have seen, of machine learning. More than a discipline in its own right, the term indicates a set of specific tools to solve a particular group of problems.
While the term machine learning refers to the discipline, deep learning refers to the way the machine learns. Neural networks are the protagonists of this field. In fact, a neural network learns through its layers, which can be very deep.
Neural networks are considered the essence of deep learning, and there are really plenty of them… each designed to solve a particular type of problem.
For example, there are neural networks that “remember” very long sequences, so that the output is influenced not only by the latest data, but also those that preceded it long before. These are called LSTM (long-short term memory) neural networks. Others instead, called convolutional neural networks, apply filters to the images so as to learn only the most relevant characteristics of the subjects represented.
If you want to read about neural networks, at this link you will find an introductory article that explains how they work and learn.
Differences between Traditional Machine Learning and Deep Learning
The key difference between traditional machine learning and deep learning can be found in the problems that these algorithms attempt to solve. Many of these are designed to solve specific problems, such as time series or text regression and classification.
In any case, there are algorithms for all kinds of problems in both families.
Here is a list of the main differences between traditional machine learning and deep learning algorithms.
Deep learning is a set of techniques and algorithms certainly more suitable for specific problems, usually very complex. Since neural networks can model any function if they have unlimited resources and time, they are used to address complex problems involving unstructured data, such as text, video and audio.
For tabular, therefore structured, data, less complex machine learning techniques can also exceed the performance of a deep learning algorithm.
Furthermore, neural networks are more effective than traditional algorithms at learning from much larger datasets (big data).
While there are very complex traditional machine learning algorithms, such as XGBoost, deep learning algorithms are by definition more complex.
Designing deep learning models is one of the most important challenges in data science. Machine learning engineers strive to innovate in the field every day. Some of the most relevant companies in the field are Hugging Face, Google, Meta, Baidu, OpenAI and many others.
Typically, traditional machine learning algorithms require less computational power than deep learning ones. This is because neural networks can take advantage of GPUs (graphic processing units — basically video cards) to increase training speed. In addition to GPUs, they can also take advantage of TPUs (tensor processing units), which are chips optimized for deep learning.
Some traditional machine learning algorithms can also take advantage of GPUs — among them are XGBoost, LightGBM, and Catboost.
An aspect often not considered when talking about the differences between machine learning and deep learning is how much the models and algorithms are interpretable.
Neural networks are typically considered to be black boxes — that is, we know how they work, but we don’t know how they achieve the expected result, nor can we predict it. Only by experimenting with different architectures can we gradually get closer to the best configuration.
Traditional algorithms, on the other hand, such as decision trees, are easily interpretable and communicating how these work and how they achieve results is relatively easy.
Machine learning and deep learning are essentially the same thing — methods and techniques that allow a machine to make inferences precise enough to be used in a business or non-business context. These methods depend on the context we are facing.
It could be overkill to use deep learning techniques on small table datasets, because a “trivial” random forest could perform much better and converge faster to solutions.
My advice is as always to carefully evaluate the context and ask yourself clear questions that help to understand the problem we are facing.
If you want to read more about tips and approaches in general, I suggest you to browse my Medium profile which contains articles that touch on mental models and templates to optimize the work in data science.
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Thanks for your attention and see you soon! 👋
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