Rajat Monga, TensorFlow Technical Lead & Manager, Google TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was developed by researchers and engineers working on the Google Brain Team for the purposes of conducting machine learning and deep neural networks research. He is particularly interested in enabling smarter devices. Prior to Google, as the Chief Architect and Director of Engineering at Attributor, Rajat hired the founding engineering team, and led the labs and operations to design and build Attributor's core matching engine.

Apple bought Emotient, an artificial intelligence startup that reads people's emotions by analyzing facial expressions, the Wall Street Journal reported. The report did not specify the financial terms of the deal. The tech giant's plans for Emotient were not immediately clear, the Journal reported, confirming the news with an Apple spokeswoman. Emotient's software reads the expressions of individuals and crowds to gain insights that can be used by advertisers to assess viewer reaction or a medical practitioner to better understand signs of pain in patients. San Diego-based Emotient had previously raised 8 million from investors including Intel Capital, the Journal said.

Always wanted to compete in a Kaggle competition, but not sure you have the right skill set? We created a free interactive Machine Learning tutorial to help you out!Together with the team behind Kaggle, we have developed a free interactive tutorial on how to apply Machine Learning Techniques that can be used in your Kaggle competitions. Step by step, through fun coding challenges, the tutorial will learn you how to predict survival rate for Kaggle's Titanic competition using R and Machine Learning. Start the Machine Learning with R tutorial now!This free R tutorial is provided by DataCamp, an online interactive education platform that offers courses in data science and R programming. Each course is built around a certain data science topic, and combines video instruction with in-browser coding challenges so that you can learn by doing.

The Python ecosystem is growing and may become the dominant platform for machine learning. The primarily rationale for adopting Python for machine learning is because it is a general purpose programming language that you can use both for research and development and in production. In this post you will discover the Python ecosystem for machine learning. Python Ecosystem for Machine Learning Photo by Stewart Black, some rights reserved. Python is a general purpose interpreted programming language.

The last few years have seen an explosion of interest in Machine Learning (ML) technology and potential applications. Machine Learning is the unsung hero that powers many applications, systems, sensors, devices, and products. Today, Machine Learning is so pervasive that we can often assume its presence in most of the applications and systems without having to specifically call it out. In simple terms, machine learning is a computer's ability to learn from data, and it is one of the most useful tools we have to develop intelligent systems and applications. Machine learning is used widely today for all kinds of tasks, from churn prediction in large companies, to web search, to medical diagnostics, to robotics.

As we go about our daily lives, we tend to assume that our perceptions -- sights, sounds, textures, tastes -- are an accurate portrayal of the real world. Sure, when we stop and think about it -- or when we find ourselves fooled by a perceptual illusion -- we realize with a jolt that what we perceive is never the world directly, but rather our brain's best guess at what that world is like, a kind of internal simulation of an external reality. Still, we bank on the fact that our simulation is a reasonably decent one. If it wasn't, wouldn't evolution have weeded us out by now? The true reality might be forever beyond our reach, but surely our senses give us at least an inkling of what it's really like. Not so, says Donald D. Hoffman, a professor of cognitive science at the University of California, Irvine. Hoffman has spent the past three decades studying perception, artificial intelligence, evolutionary game theory and the brain, and his conclusion is a dramatic one: The world presented to us by our perceptions is nothing like reality.

Hui Wang has seen the nature of online fraud change a lot in the 11 years she's been at PayPal. In fact, a continuous evolution of methods is kind of the nature of cybercrime. As the good guys catch onto one approach, the bad guys try to avoid detection by using another. Today, said Wang, PayPal's senior director of global risk sciences, "The fraudsters we're interacting with are… very unique and very innovative. In deep learning, though, Wang and her team might have found a way to help level the playing field between PayPal and criminals who want exploit the online payment platform.

This book is a high quality statistical text with R as the software of choice. If you want to be comfortable with fundamental concepts in parallel with learning R, then this is the book for you. Having said this, you will love this book even if you have studied advanced statistics. The book also covers some advanced machine learning concepts such as support machine learning (SVM) and regularization.

Why is statistical inference and machine learning approaches important for analysing Big Data? To answer this question, I want to draw your attention to the world's largest coral reef system, and one of Australia's biggest natural wonders, the Great Barrier Reef. The Great Barrier Reef is composed of over 2900 reefs and 900 islands, spanning over 2300km, and is one of the most diverse ecosystems on the Earth. However, because of its large size, monitoring and predicting different trends in the reef is really difficult. For example, here at QUT we're using machine learning approaches to design robots to seek out and control the damaging crown-of-thorns starfish. In this course we show you how to apply certain predictive analysis, dimension reduction, clustering, and machine learning techniques to analyse big data and make informed decisions.

A "robot revolution" will transform the global economy over the next 20 years, cutting the costs of doing business but exacerbating social inequality, as machines take over everything from caring for the elderly to flipping burgers, according to a new study. As well as robots performing manual jobs, such as hoovering the living room or assembling machine parts, the development of artificial intelligence means computers are increasingly able to "think", performing analytical tasks once seen as requiring human judgment. In a 300-page report, revealed exclusively to the Guardian, analysts from investment bank Bank of America Merrill Lynch draw on the latest research to outline the impact of what they regard as a fourth industrial revolution, after steam, mass production and electronics. "We are facing a paradigm shift which will change the way we live and work," the authors say. "The pace of disruptive technological innovation has gone from linear to parabolic in recent years. Penetration of robots and artificial intelligence has hit every industry sector, and has become an integral part of our daily lives."