Deep Dive in Deep Learning with TensorFlow



7:00 - Doors open. Welcome. Networking. Beer.

7:15 - Convolutional Neural Networks for NLP.

7:45 - Q&A break.

8:00 - Introduction in Generative Adversarial Networks.

8:30 - Q&A break

8:45 - Networking.

Convolutional Neural Networks for NLP.

Convolutional Neural Networks have proven very effective in classification tasks. Initially created for computer vision for image recognition and classification, they were adopted also in natural language processing (NLP). We will make a short introduction in Convolutional Neural Networks and will explain how they apply to NLP problems.


Carlos Segura has a Telecommunications Engineering background and received his PhD from the UPC in 2011 in multimedia signal processing. He has been doing research in Deep Learning for the last 3 years applied to computer vision, speech processing and more lately in natural language processing and dialog systems. He currently works at Telefónica I+D as an associate researcher.

Silvia Necsulescu, passionate about algorithms and foreign languages, got a PhD in Natural Language Processing from UPF Barcelona addressing the automatic extraction of semantically related words. She works as an NLP Scientist on problems about automatic text classification.

Introduction to Generative Adversarial Networks.

Generative Adversarial Networks are a recent type of generative model framed within Deep Learning. They allow us to model very high dimensional distributions, making them very effective to generate novel samples for complex domains. They have been applied successfully in computer vision, where these systems generate images of high quality out of detailed descriptions. Other fields also are adopting this adversarial methodology for its proven effectiveness, like speech processing. In this talk GANs will be introduced, as well as their current applications in different domains. Moreover, there will be a coding example to see how we can construct a GAN to solve a toy example in TensorFlow.


Santiago Pascual, graduated in Telecommunications Engineering in 2016 at Telecom BCN@UPC. He has been working in Deep Learning research for more than two years, and more specifically in speech and language processing with these methodologies. Nonetheless, he also likes working in multimodal technologies, in an end2end fashion whenever it is possible. He is currently a PhD candidate at TALP@UPC, working in architectures for end2end speech processing with deep learning.

Additional information