Inverse Reinforcement Learning and Finding Proper Reward Signals for Snake-like Robots

2020-11-26T11:04:25+00:00

Learning is a process that can be observed across all living creatures – and also machines with the advent of sophisticated hardware and algorithms. This article introduces preference-based inverse reinforcement learning and explains how it can support a snake-like robot to learn to move forward efficiently.

Humans are constantly being taught and acquire knowledge: first by parents, later in school by teachers and at work by colleagues. In fact, learning is a process that

Inverse Reinforcement Learning and Finding Proper Reward Signals for Snake-like Robots2020-11-26T11:04:25+00:00

Deep Learning for Mobile Devices with TensorFlow Lite: Concepts and Architectures

2020-11-13T10:19:37+00:00

This first post tackles some of the theoretical background of on-device machine learning, including quantization and state-of-the-art model architectures for TensorFlow Lite.

The amount of mobile applications making use of some sort of machine learning is quickly increasing, just as the number of potential use cases in this area. Whenever

Deep Learning for Mobile Devices with TensorFlow Lite: Concepts and Architectures2020-11-13T10:19:37+00:00

Deep Learning on Bad Time Series Data: Corrupt, Sparse, Irregular and Ugly

2020-10-22T09:53:03+00:00

How do you train neural networks on time series that are non-uniformly sampled,  irregularly sampled, have non-equidistant timesteps, or have missing or corrupt values? In the following post, I try to summarize and point to effective methods for dealing with such data.

How do you train neural networks on time series that are non-uniformly sampled, irregularly sampled, have non-equidistant timesteps, or have missing or corrupt values

Deep Learning on Bad Time Series Data: Corrupt, Sparse, Irregular and Ugly2020-10-22T09:53:03+00:00