All the core content from the text rewritten in bulletized form for cut-to-the-chase mastery of the subject. Includes all objective testable terms, concepts, persons, places and events in browser based e-book format. Not just the facts, but interactive problem solving labs to ensure you master the concepts as well. Lab tools allow for thread-like collaboration among classmates and friends. Includes pre-made flashcards, and practice tests in true or false, multiple choice, mastery, or completion formats.

We've already started to see effective use of emerging technologies, such as Industrial IoT (IIoT)-enabled remote condition monitoring and Big Data analytics for predictive maintenance and similar offline applications; but process engineers are interested in knowing if and how emerging technologies can be used to improve the actual production process and product quality. In a recent pilot program, Mitsui Chemicals, Inc. and NTT Communications Corporation (NTT Com), the industrial control technology (ICT) solutions and international communications business within NTT Group, have successfully created a Deep Learning technique that accurately predicts the quality of gas products during production; 20 minutes before the final product is created. As we learned in a recent press release from NTT Com, the technique is based on modeling the relationship between the different data sets sourced from raw materials feeding into the reactor; reactor conditions; and the trace gas impurities that represent gas product quality, expressed here as "X-gas." The goal of this joint project between NTT Com and Mitsui Chemicals is to improve the accuracy of detecting abnormalities in product quality to improve operational efficiencies and product quality. The two companies initiated the pilot project at one of Mitsui Chemicals' gas production plants in 2015.

I had a background in financial sales and portfolio strategy, but Fintech was on the rise, the RoboAdvisor was gaining momentum, and Bogleheads were taking over. They were consolidating to the point of monopoly and had determined that fee income and cross-sell ratio were more important than service and fiduciary responsibility to client wealth. "Too big to fail" became the justification for mass abuse in the financial system. We only have to look to Wells Fargo's recent crisis to see the results. The problem is systemic, encouraged, and will get worse before it gets better.

A rover headed for the red planet will perform an unprecedented search for rocky remnants of dead Martians--so where should we send it? Fossil stromatolites, like this one from Bolivia, offer clues to the kinds of preserved life we may find on Mars. Nearly four billion years ago, when Earth was coming alive, Mars was gradually choking to death. The thick atmosphere that had warmed the red planet was leaking into space, and plummeting temperatures caused Martian lakes and rivers to freeze, turning the wet surface into a dry wasteland. But it's possible life took root in those early years.

While many advances have been made, it still has been difficult for non-programmers to create objects made of many materials (or mixtures of materials) without a more user-friendly interface. But this week, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) will present "Foundry," a system for custom-designing a variety of 3-D printed objects with multiple materials. "In traditional manufacturing, objects made of different materials are manufactured via separate processes and then assembled with an adhesive or another binding process," says PhD student Kiril Vidim?e, who is first author on the paper. "Even existing multi-material 3-D printers have a similar workflow: parts are designed in traditional CAD [computer-aided-design] systems one at a time and then the print software allows the user to assign a single material to each part." In contrast, Foundry allows users to vary the material properties at a very fine resolution that hasn't been possible before.

Researchers from Reconfigurable Robotics Lab at École polytechnique fédérale de Lausanne have created a soft and flexible robot. Made of elastomers--synthetic polymers with elastic properties--like silicon and rubber, the soft robots are powered by muscle-like actuators. Since the soft robots are cheap to produce and can be manufactured on a large scale, they can be applied on many levels. In Nature's journal Scientific Reports, the scientists reveal potential applications of the soft robots, which range from biomimetic systems and home care to handling fragile objects and patient rehabilitation. "Our robot designs focus largely on safety," said Jamie Paik, the director of the RRL, in a statement.

The Founder and Executive Chairman of the World Economic Forum published a book in January called "The 4th Industrial Revolution." In his book, Klaus Schwab proposes that we are at the beginning of a fundamental shift in human/technological interaction. Advances in robotics will redefine the workforce. Advances in information technology will deliver meaningful artificial intelligence. And advances in biotechnology will redefine what it means to be human.

Machine learning is kind of magic right? But is it the kind of magic that can make us rich? And I don't mean lucrative consulting gig rich, I mean digging valuable metals out of the ground rich. Also I'd been meaning to try out some transfer learning and looking around for a good topic to try it on. Transfer learning is where you take a pre-trained convolution (or other) network and use it for your task.

The relentless increase in computing power and the accumulation of big data over the years has sparked intense interest in machine learning and its associated techniques. The new SAS Visual Data Mining and Machine Learning software will feed this need for smarter analytics. Advanced analytics offer insight to businesses, but machine learning and deep learning algorithms take it deeper, revealing insights that were previously out of reach. For example, machine learning use can include facial recognition in security systems, speech recognition in customer service applications, accurate product recommendations in e-commerce, self-driving cars and medical diagnostics. "SAS Visual Data Mining and Machine Learning shatters barriers related to data volume and variety, limited analytical depth and computational bottlenecks. That means greater productivity – and faster, deeper insight," said Hugo D'Ulisse, Head of Analytical Platform, SAS UK & Ireland.

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications.