Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK
Miguel Gonzalez-Fierro

Miguel Gonzalez-Fierro
Senior Data Scientist, Microsoft

Website | @miguelgfierro

Miguel González-Fierro is a senior data scientist at Microsoft UK, where he helps customers leverage their processes using big data and machine learning. Previously, he was CEO and founder of Samsamia Technologies, a company that created a visual search engine for fashion items, allowing users to find products using images instead of words, and founder of the Robotics Society of Universidad Carlos III, which developed different projects related to UAVs, mobile robots, small humanoids competitions, and 3D printers. Miguel also worked as a robotics scientist at Universidad Carlos III of Madrid and King’s College London, where his research focused on learning from demonstration, reinforcement learning, computer vision, and dynamic control of humanoid robots. He holds a BSc and MSc in electrical engineering and an MSc and PhD in robotics.

Sessions

11:3012:00 Tuesday, 23 May 2017
Level: Intermediate
Mathew Salvaris (Microsoft), Miguel Gonzalez-Fierro (Microsoft)
The speed of a machine-learning algorithm can be crucial in problems that require retraining in real time. Mathew Salvaris and Miguel González-Fierro introduce Microsoft's recently open sourced LightGBM library for decision trees, which outperforms other libraries in both speed and performance, and demo several applications using LightGBM. Read more.
14:0514:45 Wednesday, 24 May 2017
Secondary topics:  Deep learning
Level: Intermediate
Average rating: ***..
(3.33, 3 ratings)
Deep learning is one of the most exciting techniques in machine learning. Miguel González-Fierro explores the problem of image classification using ResNet, the deep neural network that surpassed human-level accuracy for the first time, and demonstrates how to create an end-to-end process to operationalize deep learning in computer vision for business problems using Microsoft RServer and GPU VMs. Read more.