It is a coincidence, but one laden with meaning, that Nvidia is setting new highs selling graphics processors at the same time that SGI, one of the early innovators in the fields of graphics and supercomputing, is being acquired by Hewlett Packard Enterprise. Nvidia worked up from GPUs for gaming PCs to supercomputers, and has spread its technology to deep learning, visualization, and virtual desktops, all with much higher margins than GPUs for PCs or any other client device could deliver. SGI, in its various incarnations, stayed at the upper echelons of computing where there is, to a certain extent, less maneuvering room and more intense competition. In fact, systems using Nvidia's GPU motors have given the shared memory and clusters – many of the latter using Nvidia's Tesla accelerators to improve their computational efficiency – made by SGI a run for the money. SGI is no doubt wishing it had made GPUs for all kinds of devices and had transformed its OpenCL environment into something akin to CUDA.

Just recently, DeepMind's AlphaGo won a series of Go matches against a top-level human opponent. This victory has caused a mix of excitement and consternation. Are we seeing another case of a bigger and faster machine pushing the edge of performance, or are we perhaps approaching a fundamental crisis of "cognitive competition?" To answer this questions, we look at the succession of game-playing computers, and then explore the rise of "model-free methods" and what it foretells for our future. We have become used to the idea that purpose-built machines can surpass humans in almost any physical task.

"By developing a sophisticated molecular builder, using state-of-the-art quantum chemistry and machine learning, in addition to drawing on the expertise of experimentalists, we discovered a large set of high-performing blue OLED materials," said Aspuru-Guzik, Professor of Chemistry and Chemical Biology, who led the research. "Following that validation, I am extremely excited to see this platform adopted for commercial development, utilizing its capabilities for the rapid screening of TADF materials." The algorithms dramatically reduce the computational cost of testing candidate molecules for new technologies. In addition to Kyulux's licensing of the software, three key researchers who developed the system in Aspuru-Guzik's research group and were co-authors on the Nature Materials publication have chosen to join Kyulux's computational chemistry group in Boston. Professor Aspuru-Guzik will also join the company as a part-time scientific advisor.

In building automation, sensors such as motion detectors, photocells, temperature, and CO2 and smoke detectors are used primarily for energy savings and safety. Next-generation buildings, however, are intended to be significantly more intelligent, with the capability to analyze space utilization, monitor occupants' comfort, and generate business intelligence. To support such robust features, building-automation infrastructure requires considerably richer information that details what's happening across the building space. Since current sensing solutions are limited in their ability to address this need, a new generation of smart sensors (see figure below) is required to enhance the accuracy, reliability, flexibility, and granularity of the data they provide. Data Analytics at the Sensor Node In the new era of the Internet of Things (IoT), there arises the opportunity to introduce a new approach to building automation that decentralizes the architecture and pushes the analytics processing to the edge (the sensor unit) instead of the cloud or a central server.

Deep-learning artificial intelligence has mostly relied upon the general-purpose GPU hardware used in many other computing tasks. But Intel's recent acquisition of the startup Nervana Systems will give the tech giant ownership of a specialized chip designed specifically for deep learning AI applications. That could give Intel a huge lead in the race to develop next-generation artificial intelligence capable of swiftly finding patterns in huge datasets and learning through imitation. Nervana has leaned heavily on GPU hardware to build its own portfolio of deep-learning AI services for both companies and independent developers. But the startup has also been developing its own specialized deep learning hardware, called Nervana Engine, that includes only the components necessary for running deep-learning algorithms and eliminates the extra components used for general-purpose GPU tasks.

Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce.

Machine learning companies are being snapped up in droves by tech giants cognizant that these startups represent a new wave of technology innovation. This month alone, Intel announced plans to acquire deep learning startup Nervana Systems. And Apple confirmed it would acquire Turi inc. Earlier this year, Twitter acquired Magic Pony Technology, Salesforce acquired PredictionIO, ESI Group acquired Mineset, and Apple acquired Emotient, among other deals. PricewaterhouseCoopers LLP said 29 machine learning companies have been acquired so far this year by companies large and small, and total deals in 2016 will likely exceed the 37 such buyouts made last year.

Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle. In this post you will discover the step-by-step life-cycle for creating, training and evaluating deep learning neural networks in Keras and how to make predictions with a trained model.

Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle. In this post you will discover the step-by-step life-cycle for creating, training and evaluating deep learning neural networks in Keras and how to make predictions with a trained model. Deep Learning Neural Network Life-Cycle in Keras Photo by Martin Stitchener, some rights reserved. Below is an overview of the 5 steps in the neural network model life-cycle in Keras that we are going to look at. Deep Learning gets state-of-the-art results and Python hosts the most powerful tools.

I'll respond more specifically for deep learning. You can get a broad idea of deep what deep learning is about through tutorial lectures that are available from the Web.