WEST LAFAYETTE, Ind. – A system under development at Purdue University uses artificial intelligence to detect cracks captured in videos of nuclear reactors and represents a future inspection technology to help reduce accidents and maintenance costs. "Regular inspection of nuclear power plant components is important to guarantee safe operations," said Mohammad R. Jahanshahi, an assistant professor in Purdue's Lyles School of Civil Engineering. "However, current practice is time-consuming, tedious, and subjective and involves human technicians reviewing inspection videos to identify cracks on reactors." Complicating the inspection process is that nuclear reactors are submerged in water to maintain cooling. Consequently, direct manual inspection of a reactor's components is not feasible due to high temperatures and radiation hazards.

This'Top 10' list has been created on the basis of best content, and not exactly the number of views. To help you choose an appropriate framework, we first start with a video that compares few of the popular Python DL libraries. I have included the highlights and my views on the pros and cons of each of these 10 items, so you can choose one that best suits your needs. I have saved the best for last- the most comprehensive yet free YouTube course on DL . Before I actually list the best DL in Python videos, it is important that one understands the differences between the 5 most popular deep learning frameworks -SciKit Learn, TensorFlow, Theano, Keras, and Caffe.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data.

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Google scientist clarifies misconceptions and myths around Deep Learning Adversarial Examples, including: they do not occur in practice, Deep Learning is more vulnerable to them, they can be easily solved, and human brains make similar mistakes.

How do you find errors in a system that exists in a black box whose contents are a mystery even to experts? That is one of the challenges of perfecting self-driving cars and other deep learning systems that are based on artificial neural networks--known as deep neural networks--modeled after the human brain. Inside these systems, a web of neurons enables a machine to process data with a nonlinear approach and, essentially, to teach itself to analyze information through what is known as training data. When an input is presented to a "trained" system--like an image of a typical two-lane highway shown to a self-driving car platform--the system recognizes it by running an analysis through its complex logic system. This process largely occurs inside a black box and is not fully understood by anyone, including a system's creators. Any errors also occur inside the black box and are thus difficult to identify and fix.

Practical machine learning development has advanced at a remarkable pace. This is reflected by not only a rise in actual products based on, or offering, machine learning capabilities but also a rise in new development frameworks and methodologies, most of which are backed by open-source projects. In fact, developers and researchers beginning a new project can be easily overwhelmed by the choice of frameworks offered out there. These new tools vary considerably -- and striking a balance between keeping up with new trends and ensuring project stability and reliability can be hard. The list below describes five of the most popular open-source machine learning frameworks, what they offer, and what use cases they can best be applied to.

Since version 1.2, Google dropped GPU support on macOS from TensorFlow. As of today, the last Mac that integrated an nVidia GPU was released in 2014. Only their latest operating system, macOS High Sierra, supports external GPUs via Thunderbolt 3.1 Who doesn't have the money to get one of the latest MacBook Pro, plus an external GPU enclosure, plus a GPU, has to purchase an old MacPro and fit a GPU in there. Any way you see it, it's quite a niche market. There's another community that Google forgot.

It can be hard to prepare data when you're just getting started with deep learning. Long Short-Term Memory, or LSTM, recurrent neural networks expect three-dimensional input in the Keras Python deep learning library. If you have a long sequence of thousands of observations in your time series data, you must split your time series into samples and then reshape it for your LSTM model. In this tutorial, you will discover exactly how to prepare your univariate time series data for an LSTM model in Python with Keras. How to Prepare Univariate Time Series Data for Long Short-Term Memory Networks Photo by Miguel Mendez, some rights reserved.

Needless to say, IoT is one of the most talked about technologies in 2017. According to Statista, the global IoT market is forecast to be valued at more than 1.7 trillion U.S. dollars. "What According To You Is The Most Exciting IoT Trend To Watch For In 2018?" I think the most exciting IoT trend to watch out for in 2018 is the use of Blockchain technology to accelerate transactions, ensure trust, and reduce costs. The Internet of Things, (IoT), is such and exciting yet complex ecosystem.