Today we will talk about Deep Learning, a visionary way to make machines think more like humans, and how it has been implemented as part of fundamental strategies for various companies in multiple sectors.

Digitalization of the future. Companies and institutions all over the world continue to form and implement digital strategies to keep up and gain competitive advantages in a ruthless market.

What is Deep Learning?

Deep Learning (DL) is a subfield of Machine Learning, which uses an artificial neural network composed of different hierarchical levels of depth and complexity.

This way, the system is capable of constantly and autonomously learning from the situations presented to it without the need for human intervention. Thanks to this technology, computers learn not only meanings, but complex symbols and contexts as well. 

The definition of this technology first appears in 1974 in Paul Werbo’s doctoral thesis. However, thanks to technological advances in the 2010s, Deep Learning has become a protagonist in the realm of data solutions.

Common technology applications

• Intelligent Translation: Google uses Deep Learning for the translation of various web pages, demonstrating the powerful ability of this tool to understand meaning and context.
• Speech recognition: The application of DL in this area allow for faster and more precise results, creating the possibility of recognizing not only searches but also texts.
• Image recognition: The technology can classify categories qualitatively.
• Interpretation: DL can understand comments and conversations.

With technology like this, you can:

• Recognizes speech.
• Recognize images.
• Automatically translate languages.
• Perform facial recognition.

Deep Learning in the business world

• Customer service: Various companies and institutions service their users through automatic response programming to facilitate processes and save time for employees. 

• Manufacturing: In this industry, it is increasingly common to implement Deep Learning to perform tasks, especially when they require a lot of time and tend to be repetitive. When used in the manufacturing industry, it can prevent and reduce human errors. Thus, the manufacturing processes are safer for the personnel.

• Sports sector: This industry currently recruits specialists in DL and Big Data in order to analyze the performance of their players and improve game strategies.

• Retail companies: Companies like Burberry use Big Data and Deep Learning to gain competitive advantage and create deeper connections with their customers. Thus, they generate a relationship of loyalty and customer experience beyond regular expectations. For example, they created a program in which users can log in to share history and style preference data, as well as their shopping aversions. In this way, the sales force accesses this data and analyzes the information in real time to offer unique shopping experiences, creating a unique 360​​° shopping journey for the consumer.

What is the difference between Deep and Machine Learning?

Both technologies generate models that allow the identification and prediction of patterns in data and mimic the way of human learning, but their main difference is the type of algorithm used in each one. 

Machine Learning is comparatively simple, while Deep Learning has a more complex and advanced development due to its attempt to imitate the human brain. Meanwhile, Machine Learning can be compared to a decision tree.

Here’s an example to differentiate Machine Learning from the Deep kind: 

Look up the word “cat” in a search engine. The results will include information and images related to the word searched, as well as various cat species and pet training tips.

Machine Learning has a system similar to a probability tree, with preset options, which are compared and contrasted to better pick a response to the search input. In our example, the engine would sort a cat into categories such as “animal”, “mammal” and “feline”.

Deep Learning, by imitating the human brain, understands beyond the simple answer and can predict suggestions based on the input. It would then be able to generate results for different cat breeds as well as options like “Cats adoption” and “Cat videos”.

Even though the answers might be similar, each one has its own goals and scope.

Now that you know a little more about this technology and what it can do for the business world, do you plan on implementing it soon?