The terms of Machine Learning and Deep Learnig are often interchangeable used when talking about AI. In this article we will explore these terms in a non-technical way so that you can have a better sense of what deep learning and machine learning are.  

We need to be conscious that in order for AI to achieve its full potential we need to think thoroughly about which algorithm to apply. However, we know that this is not so easy since people who are not familiar with the technology (for example, management) are sometimes fixated on some “fancy words” instead of realistic standards.

This might lead to the number one mistake: “pushing a model” or choosing the wrong model.  If you push a model that doesn’t fit well with your project or data just so you can use a ‘fancy title’ for your project, it will lead to performance problems in the future.   


When it comes to data analysis each case is different and you should aim to use the technique that adapts the best to your project, not just pushing for some models to please management.

In this article, we will explore the basic concepts of Machine Learning vs Deep Learning so you can get a better picture of when to choose which technology. 

So let’s start with some generalities about Machine Learning. 

Machine Learning allow us to automatically learn a series of complex rules from labelled data or examples in a more accurate and faster way than trying to program them one by one.   

So we can say that Machine Learning is used in order to be able to explore how computers can program themselves and improve over time when getting more experience.  Hence, with machine learning and statistical methods: Machine Learning algorithms can make conclusions based on previous data. 

This technology is especially useful while detecting certain patterns that are almost impossible to detect by humans.  Like the human natural learning process, machine learning depends a lot of the training data and the amount of data; the more examples the system has the more precise its results will be.

To put things into perspective, here are some daily applications of machine learning:

        • Recommendation systems
        • Fraud detection
        • Spam detection
        • Forecasts and predictions
        • Natural language processing
        • Image processing
Now let’s explore the basic concepts of Deep Learning. 

Deep Learning

Deep learning is a type of machine learning. It uses neural networks as a learning method.

Most common applications of deep learning:

CNN: Convolutional neural networks are often used for image applications.

RNN: Since language is most naturally represented as sequence data we can use Recurrent Neural Networks for natural language processing.

One of the key advantages of using Deep learning is that, in order to train the neural network, you just need the input and the output, the neural network will pretty much figure out the middle by itself. To do so you will need to train it using lots of data.

The advantage of deep learning over machine learning is that it includes an automatic feature learning phase for the phase of supervised training. This phase its a hierarchy go multiple layers, that cascades and feed each other in every iteration.

When to use Deep learning? 

Well, it all depends on the amount of data and your equipment.

Deep learning systems are becoming more and more popular due to the impressive gains and state of the art performance on complex tasks.  It also has the advantage of reducing the need for human action in order to find good features. However, in order to work as intended deep learning needs very large training sets and computing power.

 Deep learning is considered to be complex in terms of implementation. If you want to implement a deep learning system you will have to make sure to have two things: a large volume of data for training and a well-equipped server to withstand such computation levels.

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