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Do you want to build complex deep learning models in Keras? Do you want to use neural networks for classifying images, predicting prices, and classifying samples in several categories? Keras is the most powerful library for building neural networks models in Python. In this course we review the central techniques in Keras, with many real life examples. We focus on the practical computational implementations, and we avoid using any math.

Deep learning and IoT are two game-changing technologies that have the potential to revolutionize the stakes for oil and gas companies facing profitmaking pressure in the face of the dramatic drop in price of oil. In this blog, based on Flutura's extensive experience in the oil and gas industry, we have highlighted three practical use cases, from the trenches, where these technologies are practically applied to solve real-life problems and impact meaningful business outcomes. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications. You forgot to provide an Email Address.

You can now learn how to build a flying car in just four months thanks to a new $400 (£295) online course. Online education provider Udacity, which is owned by Google X and Kitty Hawk founder Sebastian Thrun, has announced two new'nanodegrees'. One course will teach users the basics of driverless car engineering, while another will show students how to make systems for autonomous flying vehicles. You can now learn how to build a flying car in just four months thanks to a new $400 (£295) online course. Education provider Udacity has announced two new'nanodegrees' teaching users to make driverless or flying vehicles, such as the AeroMobil car pictured here Students will learn the basics of autonomous flight, including vehicle state planning and estimation, as well as motion planning.

New research has shown that there's a wide variation in the risk that unmanned aircraft, or drones, pose to people on the ground. The study involved collecting drone impact data from test dummies whose head and neck contained sensors to measure acceleration and force. The study is important because many of the most promising applications for these aircraft - including package delivery, public safety and traffic management - entail flights over people and raise the chance, however unlikely, of an impact between a drone and a human. New research has shown that there's a wide variation in the risk that unmanned aircraft, or drones, pose to people on the ground. Researchers at Virginia Tech assessed head and neck injury risks from three commercially available aircraft in a variety of impact scenarios.

Appvance has announced a breakthrough using artificial intelligence algorithms to automatically create and maintain scripts for testing software applications. By modeling human testers, including manual and test automation tasks such as scripting, Appvance has developed algorithms and expert systems to take on those tasks, similar to how driverless vehicle software models what a human driver does. AI-driven test automation is available to preview today, commercially in October, and requires no changes or added code in applications under test. Most software testing today is manual (up to 90% based on recent industry surveys). The rest is semi-automated by writing scripts in various languages such as Selenium or Java to repeat actions for future tests.

With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification. Fortunately, however, the most successful approaches to object detection are currently extensions of image classification models. A few months ago, Google released a new object detection API for Tensorflow.

Robots are now so smart they can work out whether you're male or female and even what your personality is like from one handshake. Researchers are developing an'emotional' humanoid robot that is sensitive to human touch and can read social situations so they always come across as polite and empathetic. In addition to looking like a human, robots must also become more sociable so they can integrate into human environments, researchers say. First results show a robot is capable of inferring someone's gender and personality in 75 per cent of cases simply by shaking hands (stock image) First results show that a robot is capable of inferring someone's gender and personality in 75 per cent of cases simply by shaking hands. In addition to looking like a human, robots must also become more sociable so they can integrate into human environments, researchers say.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

New research from Capgemini's Digital Transformation Institute shows that four out of five companies implementing AI have created new jobs as a result of AI technology Paris, September 7, 2017 – Capgemini, a global leader in consulting, technology and outsourcing services, has today announced the findings of "Turning AI into concrete value: the successful implementers' toolkit", a study of nearly 1,000 organizations with revenues of more than $500m that are implementing artificial intelligence (AI), either as a pilot or at scale[1]. The research both counters fears that AI will cause massive job losses in the short term, as 83% of firms surveyed say AI has generated new roles in their organizations, and highlights the growth opportunity presented by AI: three-quarters of firms have seen a 10% uplift in sales, directly tied to AI implementation. The report, which surveyed executives from nine countries and across seven sectors, found that four out of five companies (83%) have created new jobs as a result of AI technology. Specifically, organizations are producing jobs at a senior level, with two in three jobs being created at the grade of a manager or above. Furthermore, among organizations that have implemented AI at scale, more than 3 in 5 (63%) said that AI has not destroyed any jobs in their organization.