Improving business productivity and performance majorly depends on collecting the data and effectively analyzing it. With the growing popularity of IoT, there will be an obvious surge in data that will require better infrastructure and smarter Data Science approaches. Internet of Things (IoT) is not a separate piece of the latest technology but a convergence of various complementing technologies that contribute towards production optimization, predictive maintenance, asset monitoring and management, new products and services, etc of the businesses. It is expected that, by 2020, more than 20 billion devices will be connected. This depicts the enormous volumes of data that will be generated from these devices.

We treat athletes as if they are real-life superheroes that overcome physical challenges to achieve greatness in their respective sports. Today's athletes are physically faster, stronger and more agile than the generation before, but something is wrong. Some recent news includes the NBA expanding its mental health programme for its players and the NFL changing its rules and procedures to better protect its stars from concussions. The focus of any individual or team sport is to maximise player performance. In our sports culture, we are obsessed with team and player statistics using traditional measures in each sport.

Learn Artificial Neural Networks (ANN) in R. Build predictive deep learning models using Keras and Tensorflow R Studio R interface to Keras Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras has the following key features: Allows the same code to run on CPU or on GPU, seamlessly. User-friendly API which makes it easy to quickly prototype deep learning models. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine.

The existing literature provides evidence that limit order book data can be used to predict short-term price movements in stock markets. This paper proposes a new neural network architecture for predicting return jump arrivals one minute ahead in equity markets with high-frequency limit order book data. This new architecture, based on Convolutional Long Short-Term Memory with Attention, is introduced to apply time series representation learning with memory and to focus the prediction attention on the most important features to improve performance. The use of the attention mechanism makes it possible to analyze the importance of the inclusion limit order book data and other input variables. Our architecture with this mechanism is used and compared to existing deep learning architectures with the data set that consists of order book data on five liquid U.S. stocks over 18 months.

Data Quality Assessment Is Not All Roses. What Challenges Should You Be Aware Of? 5 Famous Deep Learning Courses/Schools of 2019 12 Deep Learning Researchers and Leaders Data Quality Assessment Is Not All Roses. What Challenges Should You Be Aware Of?

As Instacart has grown, we've learned a few things the hard way. We're open sourcing Lore, a framework to make machine learning approachable for Engineers and maintainable for Machine Learning Researchers.

What's New: This week at the annual Neural Information Processing Systems (NeurIPS) conference in Vancouver, British Columbia, Intel is contributing almost three dozen conference, workshop and spotlight papers covering deep equilibrium models, imitation learning, machine programming and more. "Intel continues to push the frontiers in fundamental and applied research as we work to infuse AI everywhere, from low-power devices to data center accelerators. This year at NeurIPS, Intel will present almost three dozen conference and workshop papers. We are fortunate to collaborate with excellent academic communities from around the world on this research, reflecting Intel's commitment to collaboratively advance machine learning." Research topics span the breadth of artificial intelligence (AI) topics, from fundamental understanding of neural networks to applying machine learning to software programming to particle physics.

Close your left eye as you look at this screen. Now close your right eye and open your left -- you'll notice that your field of vision shifts depending on which eye you're using. That's because while we see in two dimensions, the images captured by your retinas are combined to provide depth and produce a sense of three-dimensionality. Machine learning models need this same capability so that they can accurately understand image data. NVIDIA researchers have now made this possible by creating a rendering framework called DIB-R -- a differentiable interpolation-based renderer -- that produces 3D objects from 2D images. The researchers will present their model this week at the annual Conference on Neural Information Processing Systems (NeurIPS), in Vancouver.

Tata Consultancy Services (TCS) has published a new report titled Understanding the Dynamics of Artificial Intelligence in Intellectual Property. Designed to provide technologists, academicians, entrepreneurs, professionals and policymakers with insights on the frontiers in AI-related research and IP management and protection, the report was released today in partnership with the Confederation of Indian Industry (CII) at the 5th International Conference on IPR, organized by CII in collaboration with the Department for Promotion of Industry and Internal Trade, Government of India, and the Intellectual Property Office, India, said a press release from the company. The report captures trends in AI-related research globally, noting for example that while machine learning is the most common AI technique, mentioned in 89% of the patents filed, deep learning (DL) is the fastest-growing technique mentioned in patent filings. Another finding is that the highest number of patent families is in computer vision (49%), NLP (14%) and speech processing (13%). While it is no surprise that the US and China lead the world in AI-related patents, it comes as an eye-opener that China leads the world in deep learning, with the Chinese Academy of Sciences owning the largest DL-related patent portfolio and Baidu leading the pack among corporates globally.

MIT SMR authors David Bray and R "Ray" Wang discuss how people-centered design principles can serve as a framework in AI implementation. As the AI field currently stands, deep learning is playing an increasingly critical role. As organizations begin adopting deep learning, leaders must ensure that these artificial neural networks are accurate and precise -- lest they negatively affect business decisions and potentially hurt customers, products, and services. Please join MIT SMR authors David Bray and R "Ray" Wang as they discuss how people-centered design principles can serve as a framework in AI implementation. They'll also cover the methods companies can take to put these principles into action in their AI projects.