Computer vision is essentially an arm of artificial intelligence that lets machines analyze and understand images by breaking them down and processing them on a pixel-by-pixel basis, rather than by meta data (such as keywords and descriptions that rely not only on human actions, but on human accuracy too). Predictive typing keyboard company SwiftKey was recently snapped up by Microsoft -- not because it has a popular little app for Androids and iPhones, but because it's building a sophisticated back end built on artificial intelligence and machine learning. This includes artificial neural networks (ANNs) that are more directly based on the structure and workings of the human brain. From enterprise software and drug discovery through to predictive typing and now stock photography searches, machine learning is less of an abstract research field now and more of a reality.

Shutterstock is flexing its AI muscles with the news that the stock photo giant is introducing new computer-vision search smarts to its platform. The company, which is headquartered in New York's Empire State Building, went public back in 2012 and now offers more than 70 million images for bloggers and media outlets -- which can make searching for specific assets challenging. Of course, the trusty old keyword search tool is effective to an extent, but what if you want to find images that are similar to one you have in your possession? Or what if you want alternative images based on color schemes, mood, or shapes? This is where Shutterstock's new reverse image search comes into play.

This technique uses deep learning to create beautiful paintings from terribly drawn sketches. The results look so great that many people called this work out to be an April Fools' day joke! Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_c... Music: John Stockton Slow Drag by Chris Zabriskie is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/...) Source: http://chriszabriskie.com/uvp/

Decision trees are simple and powerful types of multiple variable analysis. Decision trees are produced by algorithms that identify various ways of splitting a data set into branch-like segments. These segments form an inverted decision tree that originates with a root node at the top of the tree. The object of analysis is reflected in this root node as a simple, one-dimensional display in the decision tree interface. The name of the field of data that is the object of analysis is usually displayed, along with the spread or distribution of the values that are contained in that field.

Abstract: In this webinar targeted at developers who are new to Machine Learning, I'll present the type of predictive features that can be integrated in any app (mobile, desktop, web…) and powered by data and machine learning. We'll also look at what's at stake when using ML in production and what the steps are to integrate this technology into real-world apps. In particular, we'll review open source libraries and cloud platforms that make it easier to create and experiment with ML models, but also to deploy them to production. I'll demo how to create a simple predictive model in Python with scikit-learn, how to deploy it as a REST API, and how to use that API. About your host: Louis Dorard is General Chair of the PAPIs.io Louis is also the instructor of the Operational Machine Learning workshop.

Rembrandt may have died in 1669 but his artistic legacy lives on thanks in part to a new piece of work created with computer data, instead of the artist's hands. Revealed in the Netherlands, 'The Next Rembrandt' is the result of an 18-month project undertaken by a group of art historians, along with software developers, scientists, engineers and data analysts – not the usual team of artists for portraits work. Don't miss our biggest TNW Conference yet! The team worked tirelessly to anyalyze all known works of Rembrandt, which amounts to over 300 paintings, using high resolution 3D scans to capture every little detail and create an algorithm that would eventually be able to accurately recreate his style. That data was then fed to a 3D printer, which recreated the painting using 13 layers of paint-based UV ink.

As CTO and co-founder, Kaheer Suleman led the creation of Maluuba's Deep-Learning based Natural Language Understanding platform and is the technology visionary behind the algorithms that power voice search across millions of devices. Kaheer's background is in information retrieval and artificial intelligence and he previously worked in the artificial intelligence and information retrieval labs at the University of Waterloo under Information Retrieval experts – Professor Pascal Poupart and Olga Vectomova. Kaheer's expertise in building language understanding and conversational systems has served as guide for Maluuba's research and development team through some of the toughest challenges in machine comprehension and spoken dialogue. Conversational Language Understanding: Recent advances in deep learning have all but solved speech recognition and image processing. The next frontier is natural language.

There are not many books on deep learning at the moment because it is such a young area of study. There are a few books available though and some very interesting books in the pipeline that you can purchase by early access. In this post you will discover the books available right now on deep learning. Deep Learning Books (I wish) Photo by Indi Samarajiva, some rights reserved. There is a deep learning textbook that has been under development for a few years called simply Deep Learning.

This video session features the keynote speaker Professor Geoff Hinton FRS, "Deep Learning". This lecture was filmed on May 22, 2015. This video session features the keynote speaker Professor Geoff Hinton FRS, "Deep Learning". This lecture was filmed on May 22, 2015.

Avanti Recruitment is working with a successful and rapidly expanding organisation in the Adtech sector to recruit a Data Scientist with Machine Learning skills. You will work in an R&D environment with a bright and talented team researching and implementing advanced Machine Learning Algorithms. They use a range of techniques including logistic regression, decision trees, classification, deep learning, topic modelling, collaborative filtering, matrix factorisation and topic modelling. You will have the opportunity to publish your research and keep abreast of the last Machine Learning developments. Finally they need someone who can put these ideas in to production so you must also be commercially minded and have strong programming skills in Python to not only develop models but also develop production code.