Deep Learning: everything you need to know

AI of Things    6 September, 2022
Photo: Hitesh Choudhary / Unsplash

Thanks to the enormous technological development of recent years, there are now solutions with a great impact on the business environment. A clear example of this is Deep Learning, a concept that is becoming more and more relevant in digital strategies thanks to the possibilities it offers.

What is Deep Learning?

It is basically a field of Artificial Intelligence (AI) in which algorithms mimic the way the human brain processes data.

This happens thanks to artificial neural networks that seek to emulate the way the human brain operates, to identify patterns and generate predictions from large volumes of data. All without the need for supervision, so it is a type of Machine Learning, but much more advanced than traditional machine learning.

Given the potential of this technology to extract valuable information – for example, to identify business opportunities or improve processes – several industries are investing in developments related to Deep Learning.

It is estimated that this market will grow from USD 6.85 billion in 2020 to USD 179.96 billion in 2030, indicating its importance for business strategies.

What role do artificial neural networks play?

As we saw earlier, Deep Learning seeks to mimic the functioning of the human brain, using logical structures that resemble the nervous system, with a system of “artificial neurons” capable of perceiving different characteristics of the objects being analysed.

To process the data, these neural networks are organised in layers that integrate multiple interconnected processing units, which work simultaneously, emulating the way the brain processes information.

These layers are organised as follows:

  1. Input layer: represents the input fields for the data to be entered into the system.
  2. Hidden layers: can be several. They symbolise the bridge between the input and output of the neural network. Data will pass through all the processing units that make up these layers.
  3. Output layer: represents the final destination of the data and the place where the output of the model will be generated. 

Artificial neural networks continuously improve the performance of their predictions by comparing the responses with the expected results.

These artificial neural networks represent algorithms capable of recognising patterns and distinguishing, for example, specific images or sounds in a matter of seconds.

And although initially their predictive capacity will be very limited, after many repetitions the algorithm will be able to accurately replicate the known result based on the data used, acquiring greater autonomy and accuracy.

Deep Learning Uses

Now that we have clarified what Deep Learning is and how it works, let’s take a look at some of its main uses today.:

  • Speech recognition and automatic translation on platforms such as YouTube and Skype, or in digital assistants such as Siri and Alexa.
  • Facial recognition in Google Photos.
  • Anti-fraud methods that analyse the details of transactions (time of execution, recipients, amounts, among other relevant information) to detect suspicious actions that may affect bank accounts.
  • In the agricultural sector, it can be seen in intelligent irrigation systems that take into account factors such as the level of water in the soil or humidity in the air.

Deep Learning Benefits

Given the applications it has, Deep Learning is positioned as one of the technologies with the greatest impact on the business environment, generating benefits such as:

  • Process automation: the autonomous work capacity of deep learning allows different processes to be automated, achieving greater efficiency and quality.
  • Ability to work with unstructured data: Deep learning is able to identify patterns and make predictions in a powerful way even when data is not organised.
  • Long-term profitability: Deep learning can help organisations detect business opportunities or improvements in various areas.
  • Scalable system: this technology works seamlessly with large volumes of data, so it can easily adapt to higher levels of information to keep pace with the growth of an organisation.

Deep Learning is setting the pace in the digital transformation of organisations, impacting areas such as workflows, customer service and process optimisation.

Making the leap towards this technology is crucial to boost competitiveness and, with it, strengthen the positioning of companies in an increasingly digitalised market.

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