Whether you're interested in learning how to apply facial recognition to video streams, building a complete deep learning pipeline for image classification, or simply want to tinker with your Raspberry Pi and add image recognition to a hobby project, you'll need to learn OpenCV somewhere along the way. The truth is that learning OpenCV used to be quite challenging. The documentation was hard to navigate. The tutorials were hard to follow and incomplete. And even some of the books were a bit tedious to work through. The good news is learning OpenCV isn't as hard as it used to be. And in fact, I'll go as far as to say studying OpenCV has become significantly easier. And to prove it to you (and help you learn OpenCV), I've put together this complete guide to learning the fundamentals of the OpenCV library using the Python programming language. Let's go ahead and get started learning the basics of OpenCV and image processing. By the end of today's blog post, you'll understand the fundamentals of OpenCV.

Donald Trump has responded to the EU's decision to charge Google with a record-breaking fine. "I told you so!" the president tweeted. "The European Union just slapped a Five Billion Dollar fine on one of our great companies, Google. They truly have taken advantage of the US, but not for long!" The post is just the latest mark of deteriorating relations between the EU and the US.

Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around. Instead of transferring large and sensitive data over the network or losing accuracy on ML training with sample csv files, you can have your R/Python code execute within your database. You can work in Jupyter Notebooks, RStudio, PyCharm, VSCode, Visual Studio, wherever you want, and then send function execution to SQL Server bringing intelligence to where your data lives. This tutorial will show you an example of how you can send your python code from Juptyter notebooks to execute within SQL Server.

Earlier this year at the QCon.ai I also presented an interactive workshop, using R and the Microsoft Cognitive Services APIs, to automatically generate captions for images, and to create a tool to recognize images of hotdogs. Video from both the presentation, and the workshop (which starts at the 10:00 mark), is now available to view the QCon.ai You can find the slides for the presentation here. The R code for the "Not Hotdog" workshop is available as an Azure Notebook which you can clone and use to follow along with the workshop.

Microsoft is set to launch "all-new Xbox hardware", the company has announced. It has revealed little more other than the fact that the announcements will made at Gamescom, the major industry event next month. It is unlikely that the announcement refers to a new console: the Xbox One X was only released in November, and the next full new version of the Xbox is not expected until 2020. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

A long-standing problem in model free reinforcement learning (RL) is that it requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to increase the sample efficiency of RL when we have access to demonstrations. Our approach, which we call Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. We perform experiments in a competitive four player game (Pommerman) and a path-finding maze game. We find that this weak form of guidance provides significant gains in sample complexity with a stark advantage in sparse reward environments. In some cases, standard RL did not yield any improvement while Backplay reached success rates greater than 50% and generalized to unseen initial conditions in the same amount of training time. Additionally, we see that agents trained via Backplay can learn policies superior to those of the original demonstration.

About IBM: IBM received a record-breaking 9,043 U.S patents in 2017, including 1,400 artificial intelligence and 1,900 cloud technology patents. Business focus: cloud computing, AI, software and services. Valuation is "dirt cheap," and the dividend yield is at a 24-year high.--SeekingAlpha.com IBM is the preferred IT provider for: 92 of the world's largest 100 banks 10 out of 10 of the world's largest insurers 8 out of 10 of the world's largest airlines 9 of the world's largest retailers Principal domain: IBM.com. Date/time: 18 July 2018, 5pm EDT Link to IBM's 2Q18 Earnings webcast Materials for download (to be updated after market close on July 18).

From Formula 1 to Yelp, industries across the board are seeking ways to apply machine learning to their work. Even academics and Goldman Sachs analysts tried using it to predict World Cup winners. But how is machine learning playing out in education--and how does it impact not just students, educators and parents, but also the businesses building technology tools to support teaching and learning? At the SF Edtech Meetup, hosted by EdSurge on July 10, four panelists gathered to discuss the challenges around deploying machine learning in the classroom and the boardroom. The speakers were Carlos Escapa (Senior Principal, AI/ML Business Development, Amazon Web Services), Vivienne Ming (Founder and CEO, Socos Labs), Matthew Ramirez (Director of Product Management, AI Writing Tools, Chegg) and Andrew Sutherland (CTO and co-founder, Quizlet).

The iPhone is getting more than 70 new emoji as Apple tries to make the set even more representative. There are also new kinds of expression: a cold face, a party face, a pleading face and a face with hearts. And there will be a whole host of new animals and foods, including pictures of lobsters, parrots and peacocks and mangoes and cupcakes. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.