This is the second part of our series about Machine Learning interpretability. We want to describe LIME (Local Interpretable Model-Agnostic Explanations), a popular t
This blog explains the basic concept of Reinforcement Learning, giving you an understanding of the closed loop system, in which an agent uses actions to change the state of the environment and thus receives rewards, with the goal of maximizing the return.
I would like to start this series about reinforcement learning by giving an overview of what reinforcement learning is, what it is used for and what terminology is ne
Entropy is a significant, widely used and above all successful measure for quantifying eg. inhomogeneity, uncertainty or unpredictability. It is an integral part of the latest machine learning models deployed on real-world data sets. In this article, I want to highlight the simplicity, beauty and meaning of entropy.
If you are dealing with Statistics, Data Science, Machine Learning, Artificial Intelligence or even general Computer Science, Mathematics, Engineering or Physics, you
Despite their outstanding performance on various tasks, machine perception systems are not infallible. We highlight this problem by means of particular adversarial glasses that manage to force face recognition systems to make mistakes und we show how to achieve robustness against such attacks.
Despite the fact that machine perception systems achieve superhuman performance on different perceptual tasks, researchers have recently demonstrated that they are no
Everybody talks about AI and deep learning and everybody uses it, including you! But what exactly is deep learning and what are artificial neural networks? In this article I shine a light on some basic yet crucial concepts in an attempt to lift the veil.
Artificial intelligence or deep learning: Everybody talks about it and everybody uses it, including you! Of course you immediately have the evil terminator in mind wh
I use state-of-the-art NLP techniques to improve an existing pricing model in an online car market. Online car markets usually use technical car attributes for price prediction with sellers adding description texts to provide more details. In my thesis, I use these texts to improve the existing pricing model.
tl;dr: This blog post summarizes my masters‘ thesis. I use state-of-the-art NLP techniques to improve an existing pricing model in an online car market. Online
This article presents SeqPolicyNet, our Deep Learning approach to accessing information stored in an Elasticsearch instance given natural language questions.
tl;dr (spoiler alert): We’ve trained an advanced neural network to query Elasticsearch based on natural language questions. Our model, called SeqPolicyNet, incorporat
This article unveils the connections between artificial intelligence, machine learning and deep learning based on a simple example. It suits as an introduction for newbies as well as a reference point for advanced readers looking for more complex content.
There has always been a gap between the capabilities of men and machine. While computers were able to perform complex multiplications or store large amounts of data,
Machine learning as a service (MLaaS) bietet Unternehmen eine einfache Möglichkeit, Daten zu verarbeiten, Modelle zu trainieren und Prognosen zu erstellen. In diesem Artikel werden die Angebote von vier der größten Cloud-Anbieter vorgestellt: GCP, AWS, MS Azure und IBM Cloud/Watson.
Cloud Computing gewinnt durch sein flexibles Bereitstellungsmodell immer größere Bedeutung. Von Software (SaaS),Plattformen (PaaS) bis hin zur IT-Infrastruktur (IaaS)
In the last years, artificial neural networks (ANN) have successfully been applied across a number of tasks. However, designing well performing ANNs requires expert knowledge and experience. Neuroevolution aims at solving this difficult and often time-consuming process by using evolutionary techniques.
In the last years, artificial neural networks (ANN) have successfully been applied across a number of tasks, such as image classification, speech recognition and natu
In this article we will look at the history of Neuroevolution and present state-of-the-art work that was performed by Google, Uber and other companies.
Neuroevolution describes the evolution of Artificial Neural Networks for problems in the domain of supervised or reinforcement learning. This article is the result of
In iOS 11 Apple introduced Core ML, its own framework for Machine Learning on iPhone and iPad. In this article we show how to convert a trained TensorFlow Model and integrate it with an iOS app.
Machine learning and more precisely convolutional neural networks are an interesting approach for image classification on mobile devices. In the recent past it wasn
In this post we'll show how to integrate machine learning, more accurately a neural network, to recognize houseplants in an Android app—using TensorFlow Mobile directly on the device!
Nowadays, modern mobile devices are extremely powerful and enable new approaches. Even if it sounds like a platitude, it is clear that some of these approaches are ve
Recent improvements on the architecture and the training of Generative Adversarial Networks have rendered them applicable on a greater variety of problems, e.g sequential or discrete data. In this blog article we take a closer look on the general theoretical GAN architecture and its variations.
Neural networks are one of the technologies that have the potential to change our lives forever. Besides lots of applications and machines in the industry they have d
We built a text spotting (OCR) pipeline that out-performed Google Cloud Vision using semi-supervised Generative Adversarial Networks.
Despite all advances in machine learning due to the advent of deep learning, the latter has one major shortcoming: It requires a lot of data during the learning proce