What is deep learning?

Deep learning uses artificial neural networks with multiple layers to learn from large amounts of data and recognize complex patterns. This technique differs from traditional machine learning methods in its ability to autonomously process and adapt deep abstractions, allowing it to effectively handle complex tasks.

Application examples:

• Image recognition: Identification of objects, faces or patterns in images and videos

• Speech processing: Automatic speech recognition, machine translation and text generation.

• Autonomous systems: self-driving cars and drones make independent decisions and navigate themselves.

• Medical diagnosis: Analysis of medical images, such as CT scans, to detect diseases.
  

Challenges:

The method places high demands on computing power and requires extensive amounts of data to train the models. In addition, the complexity of the models is a challenge for the traceability of the decision-making processes.