Deep Learning And Machine Learning: Being the basis behind tools like Google Translate and Cortana (Microsoft’s personalized assistant), deep learning is a promising technology used for various purposes and sectors, such as healthcare industries and e-commerce websites.
As will be clarified throughout this content, it generates excellent improvements and facilities, whether in people’s lives or the internal processes of a company.
What Is Deep Learning?
Simply put, deep learning, is a technology that uses neural networks and works like the human brain: it processes data, creates patterns and makes decisions. It is also important to mention that it is a subset of machine learning, which will be explained further below.
Unlike machine learning, however, deep understanding goes further. He creates complex machine models that simulate how humans learn new information, i.e. knowledge by example.
Examples of technologies that use deep learning are driverless cars, which can recognize a red traffic light and, based on that, stop and also differentiate a pedestrian from a light pole, for example.
How Does It Work?
To understand how deep learning works, it is essential to know how “learning” works, that is, learning. It is about the number of layers in which the data will be processed, and in deep learning, the number of layers and data is enormous.
The technology can learn patterns from two types of data: unstructured data and unlabeled data. Unstructured data needs to be organized, such as conversations on social networks and text documents. Unlabeled data is data that has an unknown input. Deep learning, however, discovers the patterns and, from there, starts to execute tasks repeatedly.
How Important Is Deep Learning?
The importance of deep learning is revealed, especially in its everyday applications. Thanks to him, for example, e-commerces can make strategic and assertive offers to users.
Deep learning also generates excellent advantages for companies when it allows them, for example, to have efficient point management with touchless point control, which works through facial recognition and voice command.
The ease users have to search on Google just using their voice since the browser can understand what users say due to deep learning. And the examples don’t stop: there is also the automatic generation of subtitles in videos, as it happens on YouTube and in the analysis of exams.
What Is Machine Learning?
Being a branch of artificial intelligence, machine learning is a data analysis method capable of automating the construction of analytical models.
In an uncomplicated way, this technology is based on the idea that systems can perform some tasks, such as learning from data, identifying patterns and making decisions, with minimal human intervention.
It uses data and algorithms to mimic how humans learn, gradually becoming more accurate. Some examples where machine learning is applied are speech recognition and image identification.
What Is The Difference Between Deep Learning And Machine Learning?
One of the main differences between machine learning and deep learning is how each algorithm learns. If you check the translation of each expression, you can get an idea: machine learning is “machine learning” and is more straightforward, while deep learning is “deep learning” and is more complex.
With machine learning, for example, algorithms use labelled and structured data to make predictions. Of course, they can eventually use unstructured data to make them. However, such data will have to go through a pre-processing that will be in charge of structuring them.
However, deep learning eliminates a portion of this data pre-processing. Thanks to it, algorithms process unstructured data, such as images and texts, and automate feature extraction. This eliminates some of the dependency on human experts.
In summary, deep learning eliminates some manual human intervention and can use much larger data sets, which makes it very scalable. Machine learning, on the other hand, relies more on human intervention to be able to learn.