Deep learning

Knowledge to boost innovation

Deep learning is one of the most advanced branches of artificial intelligence (AI), a field that seeks to develop systems capable of simulating human intelligence. But, what does deep learning mean? It is a automatic learning technique that allows machines to learn autonomously from large volumes of data. Although it is closely linked with machine learning, deep learning stands out for its capacity to address more complex problems with greater precision and efficiency.

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What is deep learning?

Deep learning is a branch of machine learning. Machine learning and deep learning refer to computer systems that identify patterns in data and learn from them to make decisions or predictions. Within this field, deep learning stands out for using artificial neural networks of multiple layers, inspired by the structure of the human brain.

These networks are especially useful to analyze unstructured data, in other words, information that isn't organized into tables or standard formats, such as images, videos, audios, or texts. For example, deep learning allows an application to recognize faces in a photo without the programmer indicating to the machine how to do it exactly.

How does deep learning work?

Deep learning works through deep artificial neural networks, designed to process data in a similar way to the human brain. These networks are organized into various interconnected layers:

Entrance layer: it collects the initial information, such as an image or audio fragment.
Hidden layers: they make calculations and progressive transformations on the data. Each layer detects more complex patterns, such as edges in an image or keywords in a text.
Exit layer: it generates the final result, which may be a classification (such as identifying a face in a photo) or a prediction (such as determining the next step in a process).

Deep learning models

There are different models in deep learning, each one designed for specific tasks:

Convolutional neural networks (CNN)
Specialized in visual analysis, used in facial recognition, medical diagnosis, or satellite image analysis.
Recurrent neural networks (RNN)
Designed to process sequential data, such as text or audio recordings. They are used in technologies such as automatic language translation and sentiment analysis.
Generative adversarial networks (GAN)
Used to generate new content, such as realistic images or music.
Transformers
Advanced models that are applied to natural language processing (NLP), used in automatic translation or text generation.

Deep learning applications today

Examples of deep learning that stand out for their practical application in diverse sectors:

Energy

It optimizes the exploration of resources, analyzing geological data with greater precision to identify optimal areas. It is also used in predictive maintenance to anticipate necessary adjustments in equipment and ensure its performance.

Health

Deep learning allows the detailed analysis of medical images, helping to diagnose illnesses in early stages with great precision.

Automotive industry

It is the technology behind self-driving cars, helping them to recognize traffic signals, pedestrians, and other obstacles.

Retail

It improves the user's experience by personalizing product recommendations in real time.

Education

Deep learning boosts adaptive learning, a methodology that automatically adjusts content and activities to the level and pace of each student.

Entertainment

Streaming platforms use deep learning to decide which series to show depending on the preferences of each user.

The future of deep learning

Deep learning continues to advance and promises new applications that will complement existing ones in sectors such as energy. Today, technologies such as energy demand prediction systems are beginning to be used to adjust production in real time. In the future, these systems are expected to become even more precise and capable of better adapting to electrical grids, efficiently integrating renewable energy sources such as solar or wind. They will also be able to optimize weather analysis, anticipating specific weather conditions to maximize production from these sources.

Key innovations in deep learning

The future of deep learning is driven by developments such as multimodal learning models, capable of combining different types of data (text, images, sounds) in a single platform. These technologies promise more natural interactions between humans and machines, improving the machines' ability to understand context and adapt to specific user needs.

Challenges and ethical considerations for deep learning

Despite its potential, this technology faces significant challenges. Its high consumption of computational resources requires intensive use of energy, which raises the need to develop more efficient infrastructures. On the other hand, regulation and transparency standards are increasingly relevant. It is essential that automated decisions are understandable to users and that systems minimise possible biases that can generate results considered unfair or inconsistent.

Repsol and deep learning

At Repsol, deep learning plays a central role in our digital transformation strategy. As part of our commitment to innovation, we have developed ARiA (Advanced Repsol Intelligence & Analytics), our Big Data and artificial intelligence cloud platform, to optimize our operations and decisions.

Specifically, ARiA allows us to:

Analyze large volumes of unstructured data, such as images and geological data, allowing for the identification of patterns that optimize the exploration of energy resources.
Optimize industrial processes, adjusting key parameters in real time to improve efficiency and reduce operating costs.
Prevent and maintain infrastructures, using predictive models that anticipate intervention needs, ensuring their operational continuity.
Manage energy consumption, adjusting production according to the demand detected by advanced algorithms.

Develop your talent in an innovative and technological environment

Would you like to work on large-scale projects? At Repsol, we are looking for professionals who want to innovate in areas related to artificial intelligence.

Join the Repsol team