Category - Nordic Data Science and Machine Learning Summit 2018

Deep Learning has been delivering state of the art results across a growing number of problems and domains. Correspondingly, Deep Learning models are being deployed across a growing number of applications and use cases...

In this talk, we will present our latest research results on applied machine/deep learning algorithms to various applications such as camera & Lidar object detection system, and road friction/surface detection for...

Developing and training distributed deep learning models at scale is challenging. We will show how to overcome these challenges and successfully build and train a distributed deep neural network with TensorFlow. First...

Deep Learning is now the standard in object detection, but it is not easy to analyze large amounts of images, especially in an interactive fashion. Traditionally, there has been a gap between Deep Learning frameworks...

We will walk through an overview of how Spotify uses machine learning to match fans and artists in a personal and relevant way. Some relevant example consumer products are the Spotify start tab, search, Daily Mix...

Ning will discuss which use cases have the real need of personalization and the reasons of that. Then I will go into details of one or two examples on model architecture, training strategy and performance measurement...

While data science has made great inroads in analysing traditional datasets, the need for deep learning algorithms to analyze audiovisual data will continue to grow. This seminar will cover how to conduct speech...

The topic will cover three parts:

Grundfos aims to be an AI driven company and our vision Where are we in the vision with one or two use case Where are we heading and how do we get there