GANs in TensorFlow from the Command Line: Creating Your First GitHub Project

[unable to retrieve full-text content]In this article I will present the steps to create your first GitHub Project. I will use as an example Generative Adversarial Networks.
Original Post: GANs in TensorFlow from the Command Line: Creating Your First GitHub Project

Generative Adversarial Networks, an overview

[unable to retrieve full-text content]In this article, we’ll explain GANs by applying them to the task of generating images. One of the few successful techniques in unsupervised machine learning, and are quickly revolutionizing our ability to perform generative tasks.
Original Post: Generative Adversarial Networks, an overview

InfoGAN - Generative Adversarial Networks Part III

[unable to retrieve full-text content]In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.
Original Post: InfoGAN - Generative Adversarial Networks Part III

InfoGAN - Generative Adversarial Networks Part III

[unable to retrieve full-text content]In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.
Original Post: InfoGAN - Generative Adversarial Networks Part III

CAN (Creative Adversarial Network) - Explained

[unable to retrieve full-text content]GANs (Generative Adversarial Networks), a type of Deep Learning networks, have been very successful in creating non-procedural content. This work explores the possibility of machine generated creative content.
Original Post: CAN (Creative Adversarial Network) - Explained

The Major Advancements in Deep Learning in 2016

By Pablo Soto, Research Engineer at Tryolabs. Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all. One of the main challenges researchers have historically struggled with has been unsupervised learning. We think 2016 has been a great year for this area, mainly because of the vast amount of work on Generative Models. Moreover, the ability to naturally communicate with machines has been also one of the dream goals and several approaches have been presented by giants like Google and…
Original Post: The Major Advancements in Deep Learning in 2016

Generative Adversarial Networks – Hot Topic in Machine Learning

By Al Gharakhanian. NIPS2016 (Neural Information Processing System) is an annual event that attracts the best and the brightest of the field of Machine Learning both from academia as well as industry. I attended this event last week for the very first time and was blown away by the volume and diversity of the presentations. One unusual observation was that a large chunk of exhibitors were hedge funds in search of ML talent. Some of the papers were highly abstract and theoretical while others quite pragmatic from the likes of Google, Facebook. The topics were wide-ranging but there were two topics stood out attracting a sizable attention. The first was “Generative Adversarial Networks” (GANs for short), while the second was “Reinforcement Learning” (RL for short). My plan is to cover GANs in this post and hope to do the same for RL in a future post.…
Original Post: Generative Adversarial Networks – Hot Topic in Machine Learning