Neuroevolution: A Primer On Evolving Artificial Neural Networks

2019-04-02T13:48:07+00:00

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

Neuroevolution: A Primer On Evolving Artificial Neural Networks 2019-04-02T13:48:07+00:00

Neuroevolution: Scaling the Evolution of Artificial Neural Networks

2019-04-02T17:43:46+00:00

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

Neuroevolution: Scaling the Evolution of Artificial Neural Networks 2019-04-02T17:43:46+00:00

Using TensorFlow Models with Core ML on iOS

2019-04-02T18:01:10+00:00

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&#

Using TensorFlow Models with Core ML on iOS 2019-04-02T18:01:10+00:00

Use Your TensorFlow Mobile Model in an Android App

2019-04-02T18:01:57+00:00

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

Use Your TensorFlow Mobile Model in an Android App 2019-04-02T18:01:57+00:00

Generative Adversarial Networks explained

2019-04-02T17:42:14+00:00

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

Generative Adversarial Networks explained 2019-04-02T17:42:14+00:00

Text Spotting using semi-supervised Generative Adversarial Networks

2019-04-02T17:41:50+00:00

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

Text Spotting using semi-supervised Generative Adversarial Networks 2019-04-02T17:41:50+00:00

TensorFlow Mobile: Training and Deploying a Neural Network

2019-09-16T15:24:57+00:00

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

TensorFlow Mobile: Training and Deploying a Neural Network 2019-09-16T15:24:57+00:00

Neural Networks in the Browser

2019-04-02T17:40:53+00:00

Neural networks are the basis of some pretty impressive recent advances in machine learning. From greatly improved translation to automatic transfer of painting style

Neural Networks in the Browser 2019-04-02T17:40:53+00:00
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