DEEP LEARNING
In this course, we will explore how Deep Learning has radically transformed artificial intelligence, being key in the resolution of complex problems in various disciplines. To this end, we will analyse use cases highlighted by the impact of this technology, from medical diagnostics to recommendation systems.
You will discover the fundamental steps and best practices for developing robust models, selecting architectures appropriate for specific scenarios and evaluating the quality of the solutions implemented. Through this immersive approach, you will gain a deep understanding of how Deep Learning drives innovation in the current era.
Discover the key modules and concepts
INTRODUCTION TO DEEP LEARNING
- Use cases.
- Basic concepts.
- Development tools.
SUPERVISED LEARNING TECHNIQUES
- Multilayer perceptron.
- Convolutional neural networks.
- Recurrent neural networks.
UNSUPERVISED LEARNING TECHNIQUES
- Word2Vec neural networks.
- Autoencoders neural networks.
admission process
Description
The aim of this course is to provide students with the know-how to identify and develop possible use cases for data, through the use of Deep Learning techniques.
These types of advanced data processing techniques have proven to be fundamental for the processing of images, audio, text, etc., revolutionising sectors such as the healthcare industry with the automatic processing and monitoring of medical data, among others.
With all this innovation, it is essential to know how to anticipate trends in the sector, identifying areas where data could help to improve processes and designing solutions.
Objectives
- Identify cases of use where Deep Learning is of interest.
- Have steps and good practices for developing this type of model.
- Select the architecture(s) appropriate for each use case.
- Be able to evaluate the quality of the solution.
Methodology
ENAE uses an active and participative methodology, ‘learning by doing’, which alternates the presentation of concepts, techniques and methods of analysis with the development of practical cases reflecting real business situations.
By promoting teamwork, the aim is to achieve the integration of all members and to resolve the cases presented more effectively, through the exchange of different points of view, opinions and experiences. Students will learn from the trainers but also from the professional experiences of their colleagues.
