Applied AI #2 – Building intelligent data products
Applied AI meetup #4 – The mechanism of thought
Applied AI #4 – Using machine learning for games as a spring-board for solving real-world problems’
Applied AI #2 – Building intelligent data products
Applied AI meetup #4 – The mechanism of thought
Applied AI #4 – Using machine learning for games as a spring-board for solving real-world problems’
Applied AI #3 – Deep learning applications in healthcare
Applied AI #3 – Affect modelling as preventative healthcare
Applied AI #3 – How AI will change the face of healthcare
This repository contains an implementation of Generating Videos with Scene Dynamics by Carl Vondrick, Hamed Pirsiavash, Antonio Torralba, to appear at NIPS 2016. The model learns to generate tiny videos using adversarial networks
Magenta is a Google Brain project to ask and answer the questions, “Can we use machine learning to create compelling art and music? If so, how? If not, why not?” Our work is done in TensorFlow, and we regularly release our models and tools in open source. These are accompanied by demos, tutorial blog postings and technical papers. To follow our progress, watch our GitHub and join our discussion group.
Open sourcing the Embedding Projector: a tool for visualizing high dimensional data. This project is release by Google same time as NIPS 2016 but not part of NIPS
See Live Demo: http://projector.tensorflow.org/
Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Indeed, in the context of TensorFlow, it’s natural to view tensors (or slices of tensors) as points in space, so almost any TensorFlow system will naturally give rise to various embeddings.
Although this project from Microsoft is already old however a demo was scheduled in NIPS which brought it back to highlight.
Project Malmo is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. We aim to inspire a new generation of research into challenging new problems presented by this unique environment.
It’s a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis. We compare two different candidate proposal strategies to guide the object search: with and without overlap.