Neuroevolution describes the evolution of Artificial Neural Networks for problems in the domain of supervised or reinforcement learning. This article is the result of
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
In this blog series we explain how you can train and deploy a convolutional neural network for image classification to a mobile app using TensorFlow Mobile.
Smart Assistants, fancy image filters in Snapchat and apps like Prisma all have one thing in common—they are powered by Machine Learning. The use of Machine Learning
Neural networks are the basis of some pretty impressive recent advances in machine learning. From greatly improved translation to automatic transfer of painting style