Using Deep Learning to improve FIFA 18 graphics – Towards Data Science


Game Studios spend millions of dollars and thousands of development hours designing game graphics in trying to make them look as close to reality as possible. While the graphics have looked amazingly realistic in the last few years, it is still easy to distinguish them from the real world. However, with the massive advancements made in the field of image processing using Deep Neural Networks, is it time we can leverage that to improve the graphics while simultaneously also reducing the efforts required to create them? To find out whether the recent developments in deep learning can help me answer my question, I tried to focus on improving the player faces in FIFA using the (in?)famous deepfakes algorithm. It is a Deep Neural Network that can be trained to learn and generate extremely realistic human faces.

Interview with Ross Taylor, Founder of Throne AI


In a previous post, I wrote about Throne AI, a sports prediction platform or "Kaggle for sports." If you're a sports fan and interested in using your machine learning abilities to predict the outcome of sports matches, then I highly recommend you sign up for Throne AI. After becoming obsessed with the platform, I wanted to know more about how it was created, what its future looks like, and the mind behind it. The following transcript has edited and condensed for the purpose of clarity. First off, tell us about your background, your education and work experience.

Sony's SXSW experiences draw out your sweat and feels


Although the company has been attending SXSW since the show's music festival days, this is only Sony's second time bringing a warehouse full of quirky mini adventures to SXSW, and it's expanded a bit beyond last year's VR-heavy affair. Walking into the Wow Studio this year, you're confronted with robots, photobooths and dark little rooms hiding mysterious demos with names like "Ghostly Whisper" and "Acoustic Vessel'Odyssey'." You can stop to play with adorable new Aibos (that now understand English) if you wish, but the fun lies beyond the foyer. Some of them have obvious sports themes. Things like "VR Soccer" and "A(i)r Hockey" showed off creative implementations of existing tech.

Global AI Market in Agriculture 2013-2018 & 2023 - Increasing Number of IOT Device Installation in Agriculture & Decreasing Price of Sensors


Drivers High penetration of IOT in agriculture industry Increasing number of IOT device installation in agriculture Decreasing price of sensors Increase in adoption of cattle facial recognition Impact analysis of drivers on market forecast 4.3.3

Control Structures in R: Using If-Else Statements and Loops


This tutorial is based on part of our newly released Intermediate R course. The course is a continuation of the R Fundamentals course and includes a certificate of completion. In this tutorial, we teach you how to use control structures by building a simple algorithm that tells you who won or lost a soccer match. We assume some familiarity with basic data structures, arithmetic operations, and comparison operators. Let's say we're watching a match that decides which team makes the playoffs.

A Beginners Guide to Beating the Bookmakers with TensorFlow


My pitch for a remake of The Hangover didn't go down very well, but hopefully the research I did for the script will still be useful to someone. This article explains how to use the TensorFlow Estimator API to create a simple Deep Neural Network (DNN) that makes predictions about football (soccer) matches. It will assume that you have installed TensorFlow and are familiar with the Python language. The critical thing for a beginner to understand about a DNN model, is that it is a function. Its purpose is to map a series of input features to an output.

Champions League live streams to become harder to access as UEFA obtains blocking order

The Independent

UEFA has obtained a high court injunction that will make it harder for people in the UK to stream Champions League matches for free.

PlayeRank: Multi-dimensional and role-aware rating of soccer player performance Artificial Intelligence

The problem of rating the performance of soccer players is attracting the interest of many companies, websites, and the scientific community, thanks to the availability of massive data capturing all the events generated during a game (e.g., tackles, passes, shots, etc.). Existing approaches fail to fully exploit the richness of the available data and lack of a proper validation. In this paper, we design and implement {\sf PlayeRank}, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We validate the framework through an experimental analysis advised by soccer experts, based on a massive dataset of millions of events pertaining four seasons of the five prominent European leagues. Experiments show that {\sf PlayeRank} is robust in agreeing with the experts' evaluation of players, significantly improving the state of the art. We also explore an application of PlayeRank --- i.e. searching players --- by introducing a special form of spatial query on the soccer field. This shows its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.

Neurons.AI (UK - Leeds) (Leeds, United Kingdom)


This is a group for anyone interested in Artificial Intelligence, how it can be used in the enterprise and what benefits it can bring. This group was started to meet others with an interest in AI technology and to share different experiences. The group is aligned with Neurons.AI - The Online Professional Network for AI, but you don't have to be a member of Neurons to join this meetup ... everyone is welcome to these meetings. Don't forget to follow us on Twitter @Neurons_AI and sign up to Neurons.AI Would you like to run a Neurons.AI Meetup Chapter in another City, if so please let us know at For more information on this and other Neurons.AI meetups please visit http://Chapters.Neurons.AI