Deep Learning

Nowadays, great computing capacity is available thanks to the evolution of computers and the ease of access to powerful resource hardware through cloud services, available on a “pay-per-use” basis. Consequently, the door is open to the use of algorithms with great predictive power among which, without a doubt, those of Deep Learning with Neural Networks as representatives stand out.

These types of algorithms allow us to approach problems on a large scale in affordable computational times for their development, testing, and production. Within these challenges it is worth mentioning its unquestionable usefulness in Natural Text Processing (NLP) and the classification and analysis of images, audio, and even video in real time. In this way, it adds dimensions that were hitherto unattainable for Machine Learning.

Among the most recognized applications of Deep Learning are:


  • Sentimental analysis in reviews
  • Classification of documents according to their content
  • Development of Chatbots capable of interacting with the user

Image and video:

  • Facial recognition
  • Identification of obstacles and dangers on the road
  • Quality control at the end of the production line


  • Transcription of conversations
  • Automatic generation of subtitles
  • Signal cleaning in telecommunications
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