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Home   >  Master's & postgraduate courses  >  Education  >  Postgraduate course in Artificial Intelligence with Deep Learning
  • Presentation
  • Training Content
  • Learning methodology
  • Final projects
  • Teaching team
  • Associates entities
  • Career opportunities
  • Testimonials
This programme is full up. Open a second group for the year 2026-2027
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Presentation

Edition
7th
Credits
15 ECTS (123 teaching hours)
Delivery method
Live online
This programme can also be taken face-to-face
Language of instruction
English
Fee
€4,100
Special conditions on payment of enrolment fee and 0,7% campaign

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Dates
Start date: 05/10/2026
End date: 17/03/2027
Class schedule
Monday: 6:00 pm to 9:00 pm
Wednesday: 6:00 pm to 9:00 pm
Presentation video
Why this programme?

The artificial intelligence has become one of the main drivers of economic transformation at a global level. The market surpassed $300 billion in 2025 and continues to grow at over 25% annually, driven especially by generative AI, which is already transforming multiple industries.

This growth is accompanied by strong investment and widespread adoption by companies and public administrations, positioning AI as a strategic priority both in Europe and worldwide.

At the same time, the labor market shows a rapidly increasing demand for specialized talent. Currently, 87% of companies consider AI skills a key factor in hiring, while demand for machine learning skills could grow by up to 383% in the coming years. In addition, Europe faces a structural shortage of technology professionals by 2030, making this specialization a clear competitive advantage.

In this context, the advancement of foundation models and multimodal systems has accelerated the integration of AI into products, services, and business processes. The ability to design, train, and deploy deep learning models has become a critical skill for professionals who aim to lead this transformation.

The postgraduate program in Artificial Intelligence with Deep Learning responds to this market need by training professionals who are not only able to use AI tools, but also to develop advanced solutions based on data and deep neural networks.

The program combines a solid theoretical foundation with a strong practical approach, enabling participants to work with current technologies such as PyTorch and to gain an in-depth understanding of the capabilities and limitations of deep learning models.

Additionally, the teaching team, with international experience in both industry and research, connects the program with the latest advances presented at leading conferences such as NeurIPS, CVPR, and ICLR, ensuring training aligned with the state of the art.

Ultimately, this program provides the skills needed to position yourself in one of the most promising, impactful, and in-demand professional fields in today’s market.

Promoted by:
  • Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona. ETSETB (UPC)
Aims
  • Design deep learning models, especially for processing text, images, video and audio.
  • Optimize and monitor the training of deep neural networks.
  • Process large data volumes with specialized hardware: Central Processing Unit (CPU) and Graphics Processing Unit (GPU).
  • Implement solution in deep learning frameworks.
  • Develop projects powered by artificial intelligence.
Who is it for?
  • Graduates in telecommunications, computer science, math and physics who would like to develop their skills on machine learning with deep neural networks.
  • IT professionals working who would like to focus their activity towards artificial intelligence.
  • Software developers willing to benefit from the new opportunities created by artificial intelligence.
Knowledge of Python programming is required, as well as a basic understanding of algebra and calculus at the engineering undergraduate level. Students must have access to high-speed internet to attend live video lectures and a computer with the Google Chrome browser. The computer does not require any special hardware or software.

Training Content

List of subjects
4 ECTS 30h
Deep Learning
  • Introduction to machine learning.
  • Backpropagation training.
  • The perceptron.
  • Softmax and Multilayer perceptron.
  • Losses.
  • Convolutional Neural Networks (CNN).
  • Interpretability.
  • Optimization.
  • Methodology.
  • Graph convolutional networks and Recommender Systems.
3 ECTS 24h
Natural Language Processing
  • Recurrent neural networks (RNN).
  • Attention.
  • Transformers.
  • Introduction and text processing.
  • Word embeddings.
  • Language models and advanced adaptations.
3 ECTS 21h
Computer Vision
  • Transfer learning.
  • Self-supervised learning and autoregressive models.
  • Metrics and recovery.
  • Video architectures.
  • Object detection.
  • Segmentation.
  • Variational autoencoders (VAE).
  • Generative adversarial networks (GAN) and diffusion.
2 ECTS 21h
Advanced Applications
Students will be able to decide the itinerary of the subject, choosing one option from block A and one option from block B.

Block A (6 teaching hours)
  • Option 1: Advanced NLP
    • Advanced applications.
    • Advanced personalisation and training techniques.
  • Option 2: Advanced CV
    • 3D reconstruction.
    • Anomaly detection with VAE.
    • Applications of generative models.
    • Video.
Block B (9 teaching hours)
  • Option 1: Speech Processing
    • Introduction to audio and speech.
    • Speech enhancement.
    • Speech recognition.
    • Text-to-speech.
  • Option 2: Reinforcement Learning
    • Introduction to Reinforcement Learning.
    • Tabular Q-Learning.
    • Deep Q-Learning.
    • Policy gradient.
3 ECTS 27h
Project
  • Programming in Python for deep learning and setup.
  • Hyperparameters.
  • Cloud computing.
  • APIs.
  • Monitoring of neural network training: training curves, computational resources.
  • Docker.
The projects will be executed in groups of 4 students.
Degree
University-specific Expert Diploma in Artificial Intelligence with Deep Learning, Issued by the Universitat Politècnica de Catalunya. Issued in accordance with the provisions of Article 7.1 of Organic Law 2/2023, of 22 March, on the University System; Article 36 of Royal Decree 822/2021, of 28 September, which establishes the organisation of university education and the procedure for quality assurance; and Articles 2 and 8 of the Amendment to the Regulations for Continuing Education Programmes, approved by Agreement CG/2025/02/35, of 25 March, of the Governing Council of the UPC. To obtain this qualification, it is necessary to hold a prior university degree equivalent to level 2 of the Spanish Qualifications Framework for Higher Education (MECES). Otherwise, the student will receive a certificate of completion of the programme issued by the Fundació Politècnica de Catalunya. (See details appearing on the certificate).

Learning methodology

The teaching methodology of the programme facilitates the student's learning and the achievement of the necessary competences.

The learning methodology of the programme combines live (70%) and recorded (30%) content. This scheme prioritizes the online interaction between instructors and students, but also exploits the flexibility of schedules allowed by pre-recorded video.

There exist two types of sessions: practical and lecture sessions. Practical sessions are based on a live development and coding of a practical case that students build in synchronization with the instructor, who will address their questions. Lecture sessions are built on top of a recorded talk that students watch previously at their convenience. During the lecture session, the instructors will review the contents of the talk and slides, solve questions from students, and propose exercises to consolidate the learning goals.

All students must have high speed Internet access for accessing the live video-lectures and a computer with a modern web browser.



Learning tools
Participatory lectures
A presentation of the conceptual foundations of the content to be taught, promoting interaction with the students to guide them in their learning of the different contents and the development of the established competences.
Practical classroom sessions
Knowledge is applied to a real or hypothetical environment, where specific aspects are identified and worked on to facilitate understanding, with the support from teaching staff.
Solving exercises
Solutions are worked on by practising routines, applying formulas and algorithms, and procedures are followed for transforming the available information and interpreting the results.
Flipped classroom
The contents are prepared prior to the face-to-face lessons. Practical sessions take place in the classroom, which enable understanding and application of concepts to real cases and the expansion of knowledge with more technical and specialised details.
Tutorship
Students are given technical support in the preparation of the final project, according to their specialisation and the subject matter of the project.
Assessment criteria
Attendance
At least 80% attendance of teaching hours is required.
Level of participation
The student's active contribution to the various activities offered by the teaching team is assessed.
Solving exercises, questionnaires or exams
Individual tests aimed at assessing the degree of learning and the acquisition of competences.
Completion and presentation of the final project
Individual or group projects in which the contents taught in the programme are applied. The project can be based on real cases and include the identification of a problem, the design of the solution, its implementation or a business plan. The project will be presented and defended in public.
Work placements & employment service
Students can access job offers in their field of specialisation on the My_Tech_Space virtual campus. Applications made from this site will be treated confidentially. Hundreds of offers of the UPC School of Professional & Executive Development employment service appear annually. The offers range from formal contracts to work placement agreements.
Virtual campus
The students on this postgraduate course will have access to the My_ Tech_Space virtual campus - an effective platform for work and communication between the programme's students, lecturers, directors and coordinators. My_Tech_Space provides the documentation for each training session before it starts, and enables students to work as a team, consult lecturers, check notes, etc.

Postgraduate's final projects

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Teaching team

Academic management
  • Pueyo Morillo, Jorge
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    PhD student in Computer Vision at the Polytechnic University of Catalonia (UPC). Master in Advanced Telecommunication Technologies with Deep Learning Specialization by the UPC. Degree in Telecommunication Technologies and Services Engineering from the Higher Technical School of Telecommunications Engineering of Barcelona (ETSETB). Currently doing research in the field of Computer Vision, especially applied to 3D content. Previously part of the Mobile Wireless Internet group of the i2cat research center.
  • Ruiz Hidalgo, Javier
    Ruiz Hidalgo, Javier
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    The holder of a doctorate in Telecommunications Engineering from the Universitat Politècnica de Catalunya (UPC) and a MSc degree by the University of East Anglia (UEA), UK. Associate Professor in the Department of Signal Theory and Communications at UPC, and a member of the Intelligent Data Science and Artificial Intelligence Research Center (IDEAI-UPC). He has led research and technology transfer projects in the field of computer vision - area in which he publishes internationally. His research focuses on deep learning and applications in 3D graph processing and generative networks.
Teaching staff
  • Aguilar Carrillo, Rafael Ignacio
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    Computer Engineer from the Lisandro Alvarado Central Western University (UCLA). He currently works as a software engineer in the GlovoMaps team at Glovo. He is a software engineering mentor for organizations and individuals. He has more than ten years of experience in different transnational companies in fields such as logistics, retail, real estate and software consultancies.
  • Albors Zumel, Laia
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    Graduated in Data Science and Engineering from the Universitat Politècnica de Catalunya (UPC), and holds a master's degree in Computer Vision from the Universitat Autònoma de Barcelona (UAB). She is currently doing her doctorate in the Department of Signal Theory and Communications at the UPC, and is writing her doctoral thesis on the efficient use of deep learning techniques for the detection and identification of fauna species and flora. She previously worked at the Barcelona Supercomputing Center (BSC), in the Emerging Technologies for Artificial Intelligence group in a joint project with CaixaBank.
  • Anglada Rotger, David
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    PhD candidate in Medical Image Processing from the Polytechnic University of Catalonia (UPC). Master in Advanced Mathematics and Mathematical Engineering from the UPC. Graduated in Mathematics and Data Science and Engineering from the Interdisciplinary Training Center (CFIS) by the UPC. Currently, a research assistant in the Digipatics project, for the development of artificial intelligence algorithms for the processing of histopathological images, in collaboration with the Catalan Institute of Health (ICS).
  • Cámbara Ruiz, Guillermo
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    Graduated in Physics from the University of Barcelona. He is a doctoral student in automatic speech recognition at Pompeu Fabra University (UPF) and Telefónica Research, and has a master's degree in Interactive Intelligent Systems from UPF. His research in deep learning for audio processing, speech and natural language has been applied in cognitive systems including Aura, Telefónica's home assistant, and Ingenious, a voice-to-voice translator for European emergency teams. He has also worked with researchers at prestigious institutions, such as the Brno University of Technology (BUT) and Dolby Labs.
  • Cardoso Duarte, Amanda
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    PhD in Signal Theory and Communications from the Universitat Politècnica de Catalunya (UPC). Master's degree in Computer Engineering from Universidade Federal do Rio Grande (FURG - Brazil). Currently an AI4S Fellow and Artificial Intelligence Team Leader at the Barcelona Supercomputing Center (BSC), leading projects that integrate AI/ML in Earth science tasks. With a background in multimodal learning, sign language translation, and climate-related AI applications, she has contributed to major European research initiatives such as Destination Earth and Horizon Europe projects.
  • Carós Roca, Mariona
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    Holder of a master's degree in Telecommunications Engineering from the Polytechnic University of Catalonia (UPC), specialising in multimedia (DL in vision, speech and text). She worked at Telefónica as a Data Scientist developing DL models to detect anomalies in networks. She is currently taking her doctorate in LiDAR data modeling for environmental applications at the University of Barcelona (UB), in collaboration with the Cartographic and Geological Institute of Catalonia (ICGC). She is also a member of Young IT Girls, a non-profit organisation encouraging girls to pursue technology studies.
  • Carrino, Casimiro Pio
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    Graduate in Physics from the University of Naples Federico II and Master in Physics of Complex Systems from the University of Turin, with more than 10 years of experience in Natural Language Processing (PLN). He has worked at the Barcelona Supercomputing Center (BSC) developing models for Catalan and Spanish.
    He is currently a senior scientist in Preply, applying generative AI to improve language teaching, and a doctoral candidate at the Polytechnic University of Catalonia (UPC), investigating multilingual and cross-language text comprehension for question and answer systems.
  • Caselles Rico, Pol
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    A graduate in Telecommunications Engineering and the holder of a master’s degree in Advanced Telecommunication Technologies from the UPC He is currently a doctoral student at the UPC, and works with the Institut de Robòtica Industrial (IRI) research centre. He works on 3D reconstruction with deep learning at Crisalix Labs. His bachelor's degree final project, which he wrote at the Insight Centre for Data Analytics (Dublin), focused on saliency prediction, and he wrote his master's degree final project on model weight disentanglement at the University of St. Gallen in Switzerland.
  • Escolano Peinado, Carlos
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    Doctor in Computer Science from the Universitat Politècnica de Catalunya (UPC) and a master's degree in Artificial Intelligence from the UPC. He is currently a researcher in the language technologies group at the Barcelona Supercomputing Center (BSC), as well as an associate professor in the Department of Computer Science at the UPC. His area of expertise is natural language processing, especially multilingual machine translation with neural networks.
  • Fojo Àlvarez, Daniel
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    He graduated in Mathematics and Physical Engineering from the Barcelona Interdisciplinary Higher Education Centre (CFIS) and holds a Master’s Degree in Advanced Mathematics and Mathematical Engineering. Machine learning engineer at Lace Lithography.
  • Giardina, Claudia
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    Master's degree in Computer Science from the Polytechnic Faculty of the National University of Asunción, Paraguay (UNA). The holder of a degree in Medical Electronics Engineering from the Polytechnic Faculty of the UNA. A specialist in Didactics in Higher Education at UNA. She is currently a doctoral student in the Department of Signal Theory and Communications at the Universitat Politècnica de Catalunya (UPC), working on a project involving artificial intelligence applied to medical imaging.
  • Giró Nieto, Xavier
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    An applied scientist at Amazon Science Barcelona, in the field of deep learning applied to computer vision. He was the founder and director of the postgraduate course in Artificial Intelligence with Deep Learning for the first nine courses between 2019-2022, which he combined with his research and teaching at the Universitat Politècnica de Catalunya (UPC) and the Institute of Robotics and Industrial Informatics (IRI). He is a member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and one of the instigators of the Deep Learning Barcelona Symposium (DLBCN).
  • Gómez Duran, Paula
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    The holder of a master's degree in Advanced Telecommunication Technologies (MATT) from the Universitat Politècnica de Catalunya (UPC). She is currently taking a doctorate in Contextual Recommendation Systems at the University of Barcelona (UB). She has three years of experience in full-stack programming (Visual Engineering) and research in various fields of artificial intelligence, at universities including the University of Barcelona and the UPC, and at institutions including the Insight SFI Research Centre for Data Analytics, Telefonica Research and TV3. She has recently published a study on Graph Convolutional Embeddings for Recommender Systems.
  • Granero Moya, Marcel
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    PhD Candidate in Artificial Intelligence at UPF Barcelona. Ex-Amazon AI Cambridge. Master in Data Science at EPFL Switzerland. Bachelor's in Telecommunications Engineering at UPC BarcelonaTech.
  • Hernández Pérez, Carlos
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    Doctoral Ph.D. student at Universitat Politècnica de Catalunya (UPC). He has a deep interest in A.I. technology and how it can benefit the future of our humanity. He focuses on its use for medical applications, but also enjoys using it for artistic purposes.
  • Jiménez Martín, Lauren
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    A doctoral student in the Department of Signal Theory and Communications at the Universitat Politècnica de Catalunya (UPC), funded by FI AGAUR 2022. The holder of a bachelor's degree in Computer Science from the University of Havana. She has applied machine learning techniques to restore medical images. She is currently preparing her doctoral thesis on the application of deep learning to solve medical problems in histopathological images, and the study of Attention and Transformers in particular.
  • Malik Ara, Ibrar
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    Graduated in Data Science and Engineering from the Polytechnic University of Catalonia (UPC) and a Master in Computer Vision from the Autonomous University of Barcelona (UAB). He currently works in the deep learning team at Crisalix as a tech lead, applying his experience in the field of 3D reconstruction and deep learning in production.
  • Mosella Montoro, Albert
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    Research Scientist specializing in the intersection of Computer Vision and Graphics. He is currently collaborating with the Human Sensing Lab at Carnegie Mellon University. He earned his PhD in Deep Learning from the Universitat Politècnica de Catalunya. Albert previously worked at Epic Games, where he developed neural networks to facilitate and accelerate the design and animation of 3D characters. Before Epic Games, he served as a Computer Vision Engineer at Ficosa, where, he implemented and integrated computer vision algorithms for advanced driving assistance systems.
  • Nieto Salas, Juan José
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    Bachelor's degree in Telecommunications Engineering from the Universitat Politècnica de Catalunya (UPC) and a master's degree in Data Science from the UPC. He did a research assistant internship using deep learning and reinforcement learning techniques at the Insight Centre for Data Analytics and at Telefónica. He currently works as a Data Scientist at Glovo.
  • Peiró Lilja, Alex
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    Telecommunications Engineer from the Polytechnic University of Catalonia (UPC) and Master's in Intelligent Interactive Systems from Pompeu Fabra University (UPF). I research and develop speech synthesis and recognition for video games. Currently, I'm a Research Engineer at the Barcelona Supercomputing Center (BSC) on the AINA project, and a PhD student at the University of Barcelona (UB).
  • Pina Benages, Oscar
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    A doctoral student at the Universitat Politècnica de Catalunya (UPC). He holds a master's degree in Advanced Telecommunication Technologies, with a mention in Deep Learning for Multimedia Processing. His research focuses on self-supervised graph representation learning and its applications in medical image processing, and specifically in the field of digital histopathology.
  • Pons Puig, Jordi
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    A graduate in Telecommunications Engineering from the UPC, and holds a doctorate in Music Technology, Large Sound Collections and Deep Learning from the Music Technology Group at Pompeu Fabra University (UPF). He also has a master's degree in Sound and Music Technologies. He is currently a researcher at Dolby Laboratories. He did work placements at the Institut de Recherche et Coordination Acoustique/Musique de Paris (IRCAM), at the German Hearing Center in Hannover, at Pandora Radio and at Telefónica Research.
  • Pueyo Morillo, Jorge
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    View profile in futur.upc / View profile in Linkedin / View profile in Orcid / View profile in Google Scholar
    PhD student in Computer Vision at the Polytechnic University of Catalonia (UPC). Master in Advanced Telecommunication Technologies with Deep Learning Specialization by the UPC. Degree in Telecommunication Technologies and Services Engineering from the Higher Technical School of Telecommunications Engineering of Barcelona (ETSETB). Currently doing research in the field of Computer Vision, especially applied to 3D content. Previously part of the Mobile Wireless Internet group of the i2cat research center.
  • Rafieian, Bardia
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    Doctoral student and researcher in the Computer Science department at the Universitat Politècnica de Catalunya (UPC). Holds a master’s degree in Software Engineering and Data Mining from Qazvin Azad University (QIAU). He currently works at Bechained.ai in MLOps, doing research and development on software integration, energy optimisation, recommender systems, NLP and time series forecasting. He has seven years of experience in data mining and natural language processing, and five years in machine learning and software integration.
  • Ruiz Hidalgo, Javier
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    The holder of a doctorate in Telecommunications Engineering from the Universitat Politècnica de Catalunya (UPC) and a MSc degree by the University of East Anglia (UEA), UK. Associate Professor in the Department of Signal Theory and Communications at UPC, and a member of the Intelligent Data Science and Artificial Intelligence Research Center (IDEAI-UPC). He has led research and technology transfer projects in the field of computer vision - area in which he publishes internationally. His research focuses on deep learning and applications in 3D graph processing and generative networks.
  • Sanchez Cervera, Ariadna
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    Bachelor's degree in Audiovisual Systems Engineering from the Universitat Politècnica de Catalunya (UPC) and The holder of a master's degree in Speech and Language Processing from the University of Edinburgh. Until 2023, she was a researcher on Amazon's text-to-speech team. She is currently completing a PhD in Speech and Voice Technologies for Pathological Voices at the University of Edinburgh.
  • Solé Gómez, Jaume Alexandre
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    Pursuing a PhD at the Image Processing Group, UPC, under the supervision of Javier Ruiz-Hidalgo. Interested in topics related to graph neural networks and self-supervised learrning. Previously, Research Fellow at Istituto Italiano di Tecnologia, Research Assistant at Vicomtech, and Research Assistant at TU Delft. MSc in Telecommunications Engineering (2020) and a BSc in Telecommunications Technologies and Services Engineering (2018) at Universitat Politècnica de Catalunya. During my studies, I did stays at Télécom ParisTech, TU Delft, and University of Luxembourg.
  • Tarrés Benet, Laia
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    A graduate in Telecommunications Engineering from the Universitat Politècnica de Catalunya (UPC), and the holder of a master's degree in Advanced Telecommunication Technologies from the UPC. She has participated in many deep learning projects with the Image Processing Group at the UPC. She is currently doing her doctorate at the UPC, and is preparing her doctoral thesis on the application of transformations in sign language. She has previously been involved in projects consisting of detecting skin lesions and colouring historical images in black and white using deep learning. He has also done internships at Amazon Research Germany.
  • Vilaplana Besler, Verónica
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    Holds a doctorate in Image Analysis from the Universitat Politècnica de Catalunya (UPC), a MSc degree in Mathematics and a MSc degree in Computer Sciences from the Universidad de Buenos Aires (Argentina). Associate professor at the Department of Signal Theory and Communications at UPC, teaching Deep Learning, Machine Learning and Computer Vision. Member of the Intelligent Data Science and Artificial Intelligence Research Center (IDEAI-UPC). Her research focuses on machine learning, deep learning and applications in medical imaging and remote sensing.
  • Ysern García, Maria
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    Master in Advanced Telecommunication Technologies (MATT) by the Universitat Politècnica de Catalunya (UPC), mention in Deep Learning for Multimedia Processing. Currently, a PhD student in the Department of Signal Theory and Communications at the UPC. Her research focuses on the use of generative models for medical imaging.

Associates entities

Collaborating partners

Career opportunities

  • Artificial intelligence engineer.
  • Engineer in deep neural networks.
  • Computer vision engineer.
  • Engineer in natural language processing.
  • Engineer in the processing of audio and voice.
  • Data analyst/data scientist.



Testimonials

Testimonials

I was looking for training to go more deeply into the area of deep learning and to be able to enter the labour market. My starting point was a completely theoretical profile, as my background is in mathematics. From the postgraduate degree in Artificial Intelligence with Deep Learning, I would highlight on the one hand its practical approach, and on the other, the wide range of content it covers. The course also works on both classic and modern developments of some ideas. This training has opened up a field with new opportunities for me, since this area has considerable impact in the current situation. The final project was very interesting. It was about the segmentation of medical images. The truth is that when I started the postgraduate course I couldn't imagine being able to do something that was that complex. In short, I would recommend this training because of its applied approach focused on the world of work, in which you learn the mechanics behind deep learning, and acquire the tools you need to put it into practice.

Núria Sánchez Alumni of the postgraduate course in Artificial Intelligence with Deep Learning

Testimonials
Artificial Intelligence is one of the latest technological topics, in and out of the professional world. As well as being personally interested in it, as a member of the digitisation team of an industrial company, I have to keep up with the times. If I can also get detailed technical knowledge, this is great added value both for the company I work for, and for my personal professional project. This is precisely what the postgraduate in Deep Learning brought me: a first immersion in this field of Artificial Intelligence, and the possibility of going further into its different areas, depending on my interest. The fact that the students included professionals from different sectors gave me new points of view, especially when identifying potential projects in which to apply AI. With the knowledge I gained, I have the information to promote the use of the technology within the company to optimise processes and even devise new business paths.

Martí Pomés Technical Lead of Process Robotics Projects in Omya

Testimonials
From my position at CatSalut doing data analysis in public health, I wanted to learn more about how to apply statistics to obtain valuable information from large amounts of data, especially to help medical diagnosis. The postgraduate degree allowed me to solidly understand the bases of deep learning and the different branches in which it can be applied. It has a very practical aspect that allows you to read ready-made programs, modify them and create your own. The highly specialized teachers, together with the possibility of carrying out a deep learning project from scratch, contribute to achieving visible and real results. What I learned, I have been able to apply in my professional career. In fact, I have been so encouraged that I will start a doctorate in this field, where I will apply artificial intelligence to generate medical images.

Júlia Folguera Data Analyst at CatSalut

Testimonials
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