Dark background with 1s and 0s flying from a humanesque side profile Hello, and welcome back to another episode of debugging (get it?), the difference between two commonly mixed up concepts.

Is deep learning machine learning?

If you read our Machine Learning vs AI blog, you'll have discovered that machine learning is a subset of artificial intelligence (AI). It follows then that deep learning is a subset of machine learning but is not precisely the same. It should also be known that deep learning is AI. Still, AI, being the overarching category of machine learning, is not deep learning. If we've lost you, we'd recommend you check out the blog mentioned above to get familiar with ML and AI, then jump back here to continue the journey.

Learning through rules and data is the same for machine and deep learning, but the difference is in the details. Machine learning algorithms use data to understand, then make decisions. Deep learning needs more data and layered algorithms that power the artificial neural network to mimic the human brain. The algorithms require less human supervision because they learn from their mistakes.

Break it down

Artificial Intelligence (AI)

AI is the general ability of computers to mimic human thinking and perform tasks. Oxford Languages defines AI as "the theory and development of computer systems able to perform tasks that normally require human intelligence."

Machine learning

Machine learning refers to a technology's ability to use algorithms to memorize patterns and apply each new understanding to make better and better decisions.

Machine learning is the study of computer systems that learn and adapt automatically from experience without being explicitly programmed.

One example you may be familiar with is Spotify learning your music preferences to offer you new suggestions. Each time you indicate that you like a song by listening to the end or adding it to your library, the service updates its algorithms to give you the best recommendations. Netflix and Amazon use similar machine learning algorithms to offer personalized recommendations.

Deep learning aka, machine learning's younger brother

Deep learning is a machine learning technique that layers algorithms and computing units—or neurons—into what is known as an artificial neural network.

What is a neural network?

A neural network is a deep learning algorithm that consists of more than three layers, complete with inputs, weights, a bias or threshold, and an output.

  • Inputs, like binary code, can be simple or more complex that feeds into the neural network.
  • Weights are assigned to determine which of the inputs are more important.
  • Biases are used to make adjustments within the neurons themselves.

All of the above influence the data outputs. Suppose the output of any specific node is above a specified threshold value. In that case, that node gets activated, and the data is sent to the next layer within the network. If it's lower, the information stops there.

Now take this same process and repeat it thousands of times with multiple layers and networks acting on top of each other. Then toss in "hidden" layers as the neural network algorithm usually goes, and you have yourself a host of outputs from multiple layers, becoming the inputs to many others.

The "deep" in deep learning refers to the depth of layers in a neural network. A neural network consisting of more than three layers—including the inputs and the output—can be considered a deep learning algorithm. (edit this sentence as it's copy/paste)

While the forward motion of input → layers → output millions of times over is already a mind-boggling concept, deep learning algorithms can also work in reverse in a process called backpropagation. This function allows the programmer to see where any error is in the algorithm so they can adjust it accordingly.

What's the difference between deep learning and machine learning?

  • Machine learning usually needs human supervision and correction to guide the algorithm. In contrast, deep learning algorithms can improve themselves on their own.
  • Machine learning algorithms learn from smaller data sets, but deep learning machines need large data sets containing variable and unorganized data.
  • Machine learning requires less training but comes with less accuracy, whereas deep learning requires more initial input and training but is much more accurate.
  • Machine learning makes simple, linear correlations. Deep learning makes non-linear, complex correlations via the layers of neural networks.

It all boils down to the first point: classical or non-deep machine learning needs more human intervention.

Like machine learning, deep learning can also use labelled data sets. However, deep learning can skip the labels altogether, taking unstructured data and outputting the desired result.

For example, say a programmer wanted a computer to determine different types of cats (because the internet loves 'em). They could tell the machine to distinguish each image by fur type; "tabby," "British shorthair," "Main Coon," etc. With classic machine learning, the engineer would label each image accordingly. The machine would then learn the pattern and repeat the process with similar pictures. In deep learning, there is no human labelling required. The device would simply take the raw images (or whatever format they were in) and correctly categorize each feline's photo.

Taking it one step further, deep learning could also classify images into appropriate categories. However, it would require more data inputs to increase accuracy.

Examples of deep learning

Deep learning and data science knowledge can be used across just about every field Here are some examples you’ve either interacted with or might soon.

Translation apps. If you've ever used a text-to-speech app that automatically translates into your target language, you've interacted with deep learning.

Vision for driverless delivery trucks, drones, and self-driving cars. Autonomous vehicles use deep learning algorithms to recognize various road signs and hazards. Because of these algorithms, designers and engineers can be sure that humanless cars recognize and react appropriately to stop signs, green lights, and oncoming traffic. More training can sharpen the algorithm's skills, like recognizing a traffic sign even if it's covered in snow.

Image colourization. Adding colour back into photos used to take a talented artist, and while it's still a job for humans, machines can do the same using deep learning algorithms. Using context and the images in the pictures, the machine can accurately add some colour to great-grandma's fireside portrait.

Facial recognition. Deep learning is at work whether you're passing through a high-security zone or simply needing to tag your friend in that embarrassing photo on Facebook. Those layered neural networks are put to work, especially when your appearance has changed. The machine is trained to recognize you even if you've shaved your beard, dyed your hair, or even recently started wearing glasses.

Netflix, Spotify, and other entertainment platforms. Deep learning is essential when it comes to suggesting your next favourite movie on Netflix or an album you may like on Spotify. You may think that Amazon knows your shopping habits too well, and that's because it most likely does. Deep learning algorithms match what you've previously listened to, bought, or watched, to make suggestions on what they think you'll be interested in next.

Why is deep learning preferred to machine learning?

Before we let deep learning take all the hype, it should be known that deep learning still has its drawbacks.

  • Due to its complexity, deep learning algorithms can take much longer to build. Machine learning algorithms can be quickly built and deployed. If speed is of the essence, machine learning may be the better option.
  • Larger datasets mean more initial research and data hunting has to be done by the programmer, and clean data can sometimes be hard to find.
  • Although deep learning modules can correct and adjust themselves as they go, their results can be more challenging to read and less explainable.

However, deep learning presents the advantage of correctly sorting vast amounts of data with little human intervention. Deep learning can also solve problems more efficiently than machine learning can. Deep learning algorithms can solve problems end-to-end, whereas machine learning algorithms need to have the issues broken down before reassembling them in the final stage.

Deep learning can also help reduce costs as it can better predict product deficiencies. It can also eliminate the need for feature engineering. The algorithm can scan the data to find correlating features to better prep the machine for future learning without being told to do so.


In summary, although deep learning will most likely carry us into the future, machine learning and deep learning have their place. Basic machine learning models are best used for predictive programs like forecasting stock market prices and weather patterns, identifying spam, and designing treatment plans. As mentioned above, deep learning comes into play for voice recognition, identifying consumer behaviour patterns for sites like Amazon and Netflix, and, most notably, self-driving cars.

Our Data Science Bootcamp is the right place to start if you want to jump into machine learning. In addition to nailing down machine learning basics, you'll also gain knowledge of all the necessary coding languages to succeed in the world of data science and machine learning.