Machine learning vs. deep learning — differences you need to know

Let’s begin with a fun fact — machine learning is not a synonym for deep learning. Yes, some of you may have already known that, but many people use them interchangeably and it’s a definite mistake. Understanding the topic requires differentiating between the two of them as well as including their almighty mother figure — the artificial intelligence itself. Knowing the dependencies among the big three simplifies the matter and lets us abstract a comprehensible, clear-cut version of the story. So, ready for some deep learning?

First, understand the big picture

If artificial intelligence (AI) is a mother, then machine learning (ML) is its daughter, and deep learning (DL) would make its grandkid.

Now, put aside the whole family picture and focus on what’s what. AI is a science that studies ways of building programmes and machines ready for creative problem solving. ML is its subfield enabling automated learning and improving, not being explicitly programmed at all. Lastly, DL is a subfield of machine learning using neural networks similar to the human neural system for an in-depth analysis.

I guess now the bigger perspective is clear and the foundations are built. Time to move on to the more advanced definitions.

What’s machine learning?

Its assignment comes down to teaching computers how to learn, not needing to be programmed for specific tasks. How is that even possible? By creating algorithms that use data to both study and make predictions.

But that’s not all. To be well educated, a machine needs three crucial components:

  1. Datasets

They are a collection of samples including many different data, such as numbers, texts or images. But they’re not easy to create — it takes a lot of time and effort.

2. Algorithm

The same task can be solved by means of different algorithms. The speed or the accuracy of the process depends on a chosen algorithm. Sometimes they can be combined together for better results.

3. Features

They are essential pieces of data indispensable for the proper task solution. Their main job is to show the machine what to pay special attention to. Features are selected depending on a particular task.

Using ML makes the software more independent — it identifies the patterns and makes useful predictions. Some say that with high quality data and the right choice of features, systems powered by machine learning are able to effectively replace humans at certain tasks.

What’s deep learning?

DL algorithms are usually more sophisticated and complex than the ML ones, providing us with a whole range of new, previously unimaginable possibilities. Being distinctly human-like, they analyze data more logically and draw legitimate conclusions. Achieving the best results requires using a layered structure of algorithms — an artificial neural network (ANN). Its design derives from a true human brain neural system conforming a machine even more to a human being. DL needs an incredibly vast amount of data and a substantial computing power, too.

Neural network works transferring the information from one layer to another while connecting weighted channels with attached values. Neurons have their biases, i.e. unique numbers indicating if a particular neuron gets activated. Each of the activated ones passes information to the following layers. The neural networks need to be trained using a huge amount of data. The more parameters we consider, the more accurate solutions we acquire.

Ok, we may have gotten into too much detail and technicalities, but, as you can see, it can get complicated being simple at the same time. And the topic is definitely hype right now. DL is used in:

  • speech recognition systems (Google Assistant or Amazon’s Alexa),
  • facial recognition programs,
  • automated driving,
  • military,
  • consumer electronics,
  • and more.

Machine learning vs. deep learning — which one rocks?

You should already understand that deep learning algorithms are in fact machine learning algorithms. That’s why we should actually focus on what makes the DL ones more special. The answer may be simple: the ANN, the vast data requirements, and the lower need for human intervention. But let’s dig into them more closely.

The ANN algorithm structure supremacy

Traditional ML’s algorithms structure is rather simple — they’re in the form of linear regression or a decision tree. As we’ve already mentioned, the multi-layered ANN is complex and intertwined like a human brain, working more comparably to one, too.

No (little) help from humans needed

Machine learning needs humans to identify and hand-code features based on the data type. Deep learning is far more emancipated and skilled — it’s able to learn the same features without any human intervention.

Huuuuge amount of data requires special treatment

Yes, the amount of data in DL is so huge that we need to stress it out with a quadruple “u”. And so is the complexity of the mathematical calculations. All of that comes down to a necessity of using more powerful hardware, one of them being GPUs — graphical processing units. ML programs don’t need as much computing power. They settle for simpler solutions.

Complexity equals more time for training

It’s quite obvious that professional sportsmen need more time for training and thus are able to become the champions of the world. So does the deep learning. Due to a large amount of data and complicated mathematical formulas it takes more time to learn. While ML is ready within seconds or hours, DL can take not only hours but sometimes even weeks!

They like different things

Summing up all the differences mentioned above, we can assume that machine and deep learning are used as two quite different solutions developing diverse applications. ML apps can forecast prices in the stock market or even weather conditions in a certain area, identify spam, and design treatment plans for medical patients. DL supports film and music streaming services, facial recognition apps, and advanced solutions like self-driving cars which can determine objects to avoid, tell the traffic lights apart, know when to slow down, speed up, and stop.

Dig deeper into deep learning — here come the examples

We’ll start from the most vivid one and elaborate a bit more on the topic of automated driving. How would we programme a car to recognize a “stop” sign? First, the sign would have to be deeply analyzed by the ANN which would then characterize all of its features: edges, points, or objects. At this point, in traditional machine learning, a software engineer would select the features himself. ANN is a little miss independent being capable of automatic feature engineering. One of the layers can learn how to detect edges, the other one to distinguish between different colors, there can be one for more complex shapes, and so on, and so forth. It’s crucial to feed it with enough, good quality data and let it learn by itself, sometimes on its own errors.

Let the second example be a face recognition app. It definitely uses DL, but how? Firstly, it learns to recognize all the lines and edges of various faces, then their characteristic parts, and finally their overall representation. The program trains itself using a vast amount of data. The more it processes, the more accurate answers it provides. The training takes place while using the neural networks without any human interaction.

So, what have we learned today?

Don’t worry — the quantity of information today wasn’t as vast as the amount of data used by deep learning. Some of the facts may even stuck in the neural structure of your brain. But let’s organize them one last time:

  1. The most crucial difference between deep learning and machine learning is the presentation of data. ML algorithms require structured data, and DL uses the artificial neural networks’ layers.
  2. ML algorithms learn how to complete certain tasks by understanding labeled data, but when the output isn’t satisfactory, they need to be retrained once again by a human.
  3. As opposed to ML, deep learning doesn’t require human intervention. The advanced, nested layers in the neural networks are able to learn by themselves drawing conclusions from their own mistakes.
  4. The quality of data determines the quality of the result. That’s why they always have to be vast, advanced, and precise.

Back to the future

Both machine and deep learning are a true hit right now and won’t cease to be used in the future software engineering. Their possibilities are nearly endless. We have robots improving our everyday life locked in simple electronic devices, doctors being able to predict or detect cancer on an early stage with the help of a machine, companies saving money and investing more wisely with exhaustive calculations and accurate predictions, and many more.

As a software company we are constantly open to what the future will bring and totally excited for all the artificial intelligence based projects to come!

Want to use our knowledge and competences? Don’t hesitate to get in touch.

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