Let's get something straight right from the start, deep learning is a subset of machine learning, right? In conclusion, they are not the same, but we will talk about that later.

First we want to ask you a question: are you familiar with these terms? No? And look at us, trying to explain that one is a subset of the other... Okay, let's go step by step.

A self-driving car, instant translation done by a machine, personalized shopping suggestions, these are just some of the tasks that machine learning has made possible.

In fact, you may have interacted with these concepts and not known about them, or your company might use them and you don't even know. 

Want to know more about them? Keep reading to discover their benefits: 

What is machine learning?

This discipline, which has a short life span compared to others, belongs to the field of artificial intelligence. Through different algorithms, it gives computers the ability to identify certain patterns of data to make predictions. 

It’s like a small robotic child that is given information and, based on it, learns to do certain tasks independently. That is, you don't have to be a supervisor all the time to make sure it does the job well.

Oh, and by the way, the term was first used in 1959, but as you may have noticed, it didn't become relevant up until a few years ago.

Machine learning algorithms

As you might have guessed, there are different types of algorithms that machine learning uses for machine learning, although if we're being honest, the first 2 are the most commonly used:

1. Supervised learning

This is perhaps the simplest model to explain, since based on a simple system of labels it is able to learn, as these are associated with the data to make decisions or predictions.

Remember the spam section of your email? Well, those tags have already been used to let the program know what to determine as spam and what not. Of course, it's not perfect because you might have received an informative email from us and it got sent to that folder by mistake. 

2. Unsupervised learning

It’s the opposite side of the previous algorithm system. Here the software is not given any prior knowledge, but is thrown in front of a sea of data. Why is it done this way? So that it finds patterns and organizes them in some way.

In digital marketing, for example, we find in social networks hundreds of data that can be mined for organization. They will offer certain information to elaborate advertising campaigns with better segmentation and, therefore, impact.

3. Reinforcement learning

As we told you before, it’s not the most common, since it forces the algorithm to learn based on its own experience. Let's say that operant conditioning is used, since decisions are considered and successes are rewarded.

Unlike the previous 2, it’s not so common in the world of digital marketing, but it is in other areas, such as facial recognition detection, medical diagnostics, among others.

Benefits of machine learning in companies

This new trend was already being used in the business world, especially in the digital world, but if you had not noticed its relevance until now, it may have been for two reasons: either you live under a rock or you have not been aware of its benefits. 

In either case, here are the contributions it can make to your company:

• Trend prediction: it helps to perform market analysis to predict a time of high demand for a certain product, which helps to raise or lower prices.

• Helps to innovate: since it collects, analyzes and provides results from the data you provide, it can help you find innovative solutions.

• Better target segmentation: algorithms focused on search and pattern detection help you analyze the target audience to have more data and segment better.

• Ad segmentation: some algorithms can help you determine which ads have a higher success rate in a given target audience, to ensure better results.

• Cost reduction: thanks to task automation you can reduce total costs in your company and increase your profit margin.

• Better interaction with customers: chatbots are active 24 hours a day, 7 days a week, 365 days a year, so you will have data at all times to learn more about your customers.

Practical applications of machine learning

Although the concept of digital transformation has been around for a long time, the reality is that machine learning does help almost any company to streamline certain processes and start digitizing. There are different problems that you encounter when you want to take this step, but many of them can be solved with this discipline.

• Recommendations: helps your customers find the products they are looking for. It can also be seen in other applications such as Spotify, which recommends songs based on the user's history.

• Social networks: in Twitter you can see machine learning to reduce spam, while in others such as Facebook, it is used to detect and eliminate fake news or unauthorized retransmissions.

• Natural Language Processing: Siri or Alexa can translate from one language to another just by listening to you, they can recognize the user's voice and this helps to perform a wide variety of tasks.

• Search: search engines are empowered with machine learning to better respond to users' search intentions. 

• Cybersecurity: new antivirus already have machine learning algorithms in their system, which allows them to offer better detection systems elimination, boost scanning, among other benefits.

What is deep learning? 

The fact that they share the word "learning" does not make them the same thing. Deep learning is different because it has three or more neural layers and its intention is to emulate the human brain, although of course, it’s not able to match its analysis and learning capacity.

However, it has the ability to learn based on the neural network that has been developed with greater precision, since these hidden layers allow it to have greater optimization.

Today, deep learning is present in more services and applications than you can imagine. Are you familiar with Artificial Intelligence (AI)? Then you may have already approached deep learning before.

This discipline seeks to improve automation, performing certain analytical and physical tasks without too much intervention on our part. Would you like to know some of the services that rely on this technology? 

• Virtual digital assistants.

• TV controls enabled by voice recognition.

• Credit card fraud detection.

• Autonomous car driving. 

And these are just some of the many that we can mention and that have become popular in recent years.

What is the difference between deep and machine learning?

We told you at the beginning that deep learning is a subset of machine learning, but in reality, it is distinguished by the data it processes, but also by the methods it uses to learn. Let's take it one step at a time, shall we?

1. Data processing

All the processing work that machine learning uses is almost eliminated at its root in deep learning. 

The fact is that its ability to process unstructured data, i.e. text and images, helps to automate certain features, which further reduces human intervention, i.e. greater autonomy. 

Let's look at a more practical example of this. Remember your smartphone asking you if you want to catalog your photos by faces? There it could be using deep learning, since based on the detection of certain facial features, it can relate certain faces and group them together.

The photos of you that, for example, your phone could identify because you have a mole under your eye, would be different from the photos of your spouse, because he/she has freckles. Where there would have to be a human to classify, there is now a machine.

2. Types of learning

We can say that deep learning is a type of specialization in machine learning, where the algorithm has taken on greater relevance and independence from human interaction.

In deep learning, learning is more similar to that of a human in its operation, since it uses neural networks, compared to the trees used in machine learning for decision making.

Applications of deep learning

Just as machine learning has applications in the working world, so does deep learning. 

In fact, we've already told you that you've interacted with each of them so far without realizing it, but for now we'll focus on the times you may have used deep learning and didn't know it:

1. Law Enforcement

We're not saying that you've acted outside the law, but that deep learning can help detect fraudulent or criminal activity that it has unwittingly protected you from. 

Voice recognition, computer vision, among other applications of deep learning help and contribute to better efficiency in the investigation of patterns, as well as evidence of recordings, videos, sound and images. 

2. Financial Services

Did you apply for a loan and get approved? Maybe you went through one of the predictive analytics methods that some financial institutions use to assess the risks of a business before agreeing to a loan.

In short, congratulations, not only for the loan, but after an evaluation of this magnitude, your business seems to not only meet your expectations, but those of an AI specialized in detecting potential scams or economic risks for the financial services entity in which it works.

3. Customer service 

Chatbots have taken a new step towards using AI to include natural language and some of them even incorporate visual recognition. But many of them attempt to determine ambiguous questions and answer them in a straightforward manner.

Some examples of these are virtual assistants such as Siri, Alexa, Google Assistant, to name a few of the most popular.  

Leverage the contributions of deep and machine learning

While it may be overwhelming to take a step of this magnitude, the benefits outweigh the risks. We have already told you about some of them, but at the end of the day, the decision is up to you.

However, if you are determined to implement this in your company, we want you to answer these two questions: which of the two do you need, machine or deep learning? In which area of your company would you use it? We will be happy to read you in the comments.