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.

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.

Now the real work begins for The Engine, MIT's ambitious venture fund and incubator. The organization announced its first batch of seven investments on Tuesday (see below), and revealed that it has raised $200 million, with plans to back 40 to 50 so-called "tough-tech" companies over the next few years. The Engine initially raised $150 million for its first fund, but later tacked on the additional $50 million. MIT is one of the investors in the fund; it chipped in $25 million. MIT launched The Engine almost a year ago to provide resources to startups whose technologies might get stranded in the research lab because they would take more time and money to develop than most venture capitalists are willing to invest--think biotech, medical devices, robotics, advanced manufacturing, materials science, and energy. The Engine combines a venture fund and access to work space, expert advisors, educational workshops and events, and business services.

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

Online learning portal Udacity launched its first 36-week "nanodegree" course for self-driving car engineering last year. There's a new, introductory course available now as well, focused on bringing students with minimal programming into the larger program. Even better, Udacity has partnered with Lyft (which has self-driving plans of its own) to provide scholarships to the intro course in order to increase diversity to the program. Lyft says that people "from all backgrounds and perspectives" should have the opportunity to contribute to the future of transportation in the form of self-driving cars. "Diversity is crucial for creating solutions that serve everyone, and ridesharing is for everyone," the company writes on its website.

Welcome to this course: Deep Learning - Learn Convolutional Neural Networks. Deep Learning has made some huge and significant contributions and it's one of the mostly adopted techniques in order to drive insights from your data nowadays. Convolutional neural networks have gained a special status over the last few years as an especially promising form of deep learning. Rooted in image processing, convolutional layers have found their way into virtually all subfields of deep learning, and are very successful for the most part. Convolutional Neural Networks are very similar to ordinary Neural Networks: they are made up of neurons that have learnable weights and biases.

Description: Learn how to Visualize Data with MATLAB in 2D, 3D, 4D, 5D and create animated plots with tens of projects and examples!

The class will be broken down into three daylong lessons. Attendees may purchase one-day passes for the lesson(s) of their choice, or a full-access pass to attend the workshop in its entirety. Taught by Joe Caserta, author of The Data Warehouse ETL Toolkit, the class will be held in midtown Manhattan.

A new degree program at Carnegie Mellon University and an online data science training course at MIT are focused on arming those in the workforce with new skills. This complimentary document comprehensively details the elements of a strategic IT plan that are common across the board – from identifying technology gaps and risks to allocating IT resources and capabilities. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.