Practical Deep Learning: on-site training

Case study: O2 Telefonica


O2 Czech Republic (operating under the O2 brand) is the largest integrated telecommunication operator in the Czech Republic. Activities of its R&D department include optimizing internal products and customer management, as well as external projects for signaling networks and geolocation.

O2’s data science team has been joined by new members recently, and O2 chose RARE to help bring everyone up-to-speed with the latest deep learning technologies through an on-site 2-day training workshop. The workshop covered conceptual understanding, background and technical aspects, including installation, implementation and model optimization.

We’re already using RARE’s open source software internally in O2, and I also attended their public workshop at MLPrague. I really liked both. So RARE was a natural choice when looking for an expert Deep Learning training.

Jan Romportl, Chief Data Scientist, O2 Czech Republic


RARE delivered a 2-day onsite training on Deep Learning in Practice at the O2 headquarters in Prague. The interactive workshop was attended by 13 members of the O2 data science team and lead by Dr. Piotr Migdal, RARE’s deep learning instructor. The course went from a conceptual overview, to practical concepts and optimizations in Keras, PyTorch and Tensorflow.

See the Deep Learning in Practice syllabus.

“The course structure and instructor expertise were outstanding. We especially appreciated the “before you arrive” instructions and well prepared Docker-based environment, so we didn’t waste any time on-site and were able to jump right into the material.

All team members benefited from this training. It put our knowledge in context, and even our senior staff consolidated their understanding of deep learning. I can now assign a DL project to anyone in my team and be confident they can jump right in.

I look forward to repeating the training for retention and our new team members again next year.”

Jan Romportl, Chief Data Scientist, O2 Czech Republic