Federico Di Mattia
Machine Learning & Computer Vision Engineer.
He received his MSc in Computer Science and Engineering at the University of Modena and Reggio Emilia. He spent a period working with the Computer Vision team at the Queen Mary University in London where he worked on his research thesis on a cognitive people tracker.
He worked on different Computer Vision related projects regarding security systems and worked on image processing algorithms. With a passion for psychology and negotiation, he tries always to get the best from people and the work environment.
Recently, working in Zuru, he could go in-depth in the research and studies of deep-learning algorithms applied to numerous areas using Tensorflow and Keras. During the last year, he had the chance to work on multiple tasks such as classification, segmentation, and anomaly detection.
Currently, he is working on Generative Adversarial Networks and many different Computer Vision tasks.
Twitter: @iLeW Github: https://github.com/iLeW
Deep Diving into GANs: From Theory to Production with TensorFlow 2.0
GANs are one of the hottest topics in the ML arena; however, they present a challenge for the researchers and the engineers alike. This workshop will guide you through both the theory and the code needed to build a GAN and put into production.