Deep Learning and Machine Learning are two specific Artificial Intelligence (AI) branches that are gaining more relevance in the digital age. You may have already heard of these concepts, but do you know how they work?
Machine Learning is a branch of AI that develops machines to learn from a series of data. Deep Learning is a much newer concept and works within Machine Learning or as a specific branch within Machine Learning.
Here we explain the difference between Deep Learning and Machine Learning and how this technology can be an added value for your business.
Machine Learning and Deep Learning
The AI is based on data processing resulting from previous experiences. It works similarly to our brain; it receives information and processes it to acquire knowledge.
Within the concept of AI, two specific concepts are improving the ability to understand systems. On the one hand, Machine Learning, the algorithm capable of learning with examples (data) and, on the other hand, Deep Learning, where systems develop the ability to understand this data. Let’s look at each concept separately.
It is a branch of AI that involves creating algorithms capable of modifying themselves without human intervention. It is a method of data analysis based on the idea that systems can learn from this data, identify patterns and make decisions without the help of a human being.
It is a form of Machine Learning that trains a system to learn by itself, through pattern recognition, performing tasks like human beings. Deep Learning uses a specific class of algorithms, called neural networks.
Algorithms are created and work similarly to Machine Learning. However, there are numerous layers to these algorithms, each providing a different interpretation of the data. Neural networks work in an attempt to mimic the function of neural networks in the human brain. This concept includes speech recognition, object detection, and image identification.
Also Read: Importance Of SEO – 10 Advantages At A Glance
How Does Deep Learning Work?
A Deep Learning model is programmed to analyze data continuously, with a logical structure similar to that of a human being. To achieve this ability, Deep Learning uses layers of algorithmic structures, called artificial neural networks. This network is inspired by the biological neural network of the human brain. This leads to a more advanced learning process than basic Machine Learning models.
Deep Learning is Machine Learning, and it works the same way, but the capabilities are different. Deep Learning is an evolution of Machine Learning.
Although the basic models of Machine Learning are progressively improving their functions, they still need human supervision. That is, if an AI algorithm delivers an incorrect prediction, a human will have to step in and make the necessary adjustments. However, an algorithm can determine if a prediction is wrong with a deep learning model through its neural network.
In summary, these are the main differences between both technologies:
- Machine Learning uses algorithms to analyze data, learn from that data, and make decisions based on what is learned.
- Deep Learning structures algorithms in layers to create an artificial neural network, which can learn and make decisions independently.
- Deep Learning is a branch of Machine Learning. And even though both are framed within Artificial Intelligence, Deep Learning goes much further, seeking to emulate the way of learning of humans.
Examples of Application of These Technologies:
There are numerous examples of Machine Learning and Deep Learning around us. Netflix, for example, uses these technologies to detect which movie or series you want to watch next. Thanks to these systems, Facebook knows who the person who appears in a photo or autonomous cars know where to drive.
- Fraud detection: This technology can be used to identify actions that may be fraudulent.
- Web search engines: Google uses this technology to improve its capabilities. Through a history of data, it delivers more personalized and appropriate results to each user.
- Autonomous cars: It is one of the most popular AI applications since it allows these vehicles to drive and recognize obstacles without human intervention.
- Recommendation engines: The recommendation or suggestion systems used by Amazon, Netflix, or other apps are examples of Machine Learning and Deep Learning. By analyzing the data of millions of users, this technology can predict what the user might be interested in based on previous purchases or viewing history.
- Customer service bots: Using natural language processing and customer service data, bots can answer common questions and improve the quality of responses.
- Medicine: This technology coupled with human capabilities can simplify disease diagnosis and determine the most effective treatment.
- Robots: Robots with Machine Learning and Deep Learning skills can improve their capabilities, getting closer and closer to the capabilities of the human being.
- Voice recognition: Systems like Siri, Google Home, or Google Assistant work with Deep Learning.
- Pattern recognition: Deep Learning is also capable of understanding facial expressions or written and spoken words.
How Important are Machine Learning and Deep Learning for Companies?
Nowadays, most companies are beginning to consider using Artificial Intelligence as a way to solve daily problems. Regardless of the sector of activity or the company’s size, the implementation of this technology has a very positive impact on its efficiency.
Companies that work with large amounts of data recognize the importance of Deep Learning and Machine Learning. These technologies can be used in different areas, from financial services or healthcare to marketing and sales.
Some of the advantages of AI in these branches are:
- Speed in data processing and identification of relevant information.
- Accurate analysis of consumer behavior.
- Fraud detection and prevention.
In addition, both Machine Learning and Deep Learning are great allies to improve decision-making in companies. They offer the ability to work more efficiently and intelligently, reducing the rate of human error and gaining a competitive advantage.