Machine learning and the larger world of artificial intelligence (AI) are no longer the stuff of science fiction. They're here – and many businesses are already taking advantage. As a new breed of software that is able to learn without being explicitly programmed, machine learning (and deep learning) can access, analyse, and find patterns in Big Data in a way that is beyond human capabilities. And now we've made it easier to unlock its potential with embedded machine learning capabilities and services easily accessible through the cloud. Find out more at https://www.sap.com/machine-learning

According to Forrester research from 2015, 66 percent of global data and analytics decision-makers reported that their firms either expanded or are planning to implement Big Data technologies within the next 12 months. Enterprises today are becoming more serious about Big Data and analytics, and they're looking to attract data science talent so they can achieve all of their objectives for their data programs. "There are certain data science rock stars [who are] completely up to speed on deep learning and typically have a doctorate degree," said Thomas Dinsmore, a Big Data science expert who works at Cloudera. "The big tech companies basically bid up the salaries of those folks, so hiring is challenge and difficult, but not impossible."

Artificial Intelligence & Deep Learning is transforming the entire world of technology, but AI isn't new. It has been around for decades, but AI technologies are only making headway now due to the proliferation of data and the investments being made in storage, compute and analytics technologies. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data. In this insideBIGDATA Guide to Artificial Intelligence & Deep Learning, we provide an in depth look at AI and deep learning in terms of how it's being used and what technological advances have made it possible. Artificial Intelligence is an amazing tool set that is helping people create exciting applications and creating new ways to service customers, cure diseases, prevent security threats, and much more.

Stochastic gradient Markov Chain Monte Carlo (SG-MCMC) has been developed as a flexible family of scalable Bayesian sampling algorithms. However, there has been little theoretical analysis of the impact of minibatch size to the algorithm's convergence rate. In this paper, we prove that under a limited computational budget/time, a larger minibatch size leads to a faster decrease of the mean squared error bound (thus the fastest one corresponds to using full gradients), which motivates the necessity of variance reduction in SG-MCMC. Consequently, by borrowing ideas from stochastic optimization, we propose a practical variance-reduction technique for SG-MCMC, that is efficient in both computation and storage. We develop theory to prove that our algorithm induces a faster convergence rate than standard SG-MCMC. A number of large-scale experiments, ranging from Bayesian learning of logistic regression to deep neural networks, validate the theory and demonstrate the superiority of the proposed variance-reduction SG-MCMC framework.

Google's machine intelligence library, TensorFlow (TF), has become synonymous with deep learning. Despite the name, deep learning involves just a few simple things, the complexity comes from repeating these simple things millions of times (concretely, it's the composition of millions of elementary functions). To "solve" a problem in TF, you find the minimum of some function. The hard part is the backprop which requires the derivative of this massive function. This is where TF excels, as it removes the drudgery of algorithmic differentiation and automagically moves the computation to the GPU.

– Big data helps to make strategy for future and understand user behaviors. In 1959, Arther Samuel gave very simple definition of Machine Learning as "a Field of study that gives computer the ability to learn without being explicitly programmed". Now almost after 58 years from then we still have not progressed much beyond this definition if we compare the progress we made in other areas from same time. The idea of FinTech adopting some best practices from the Big Data and AI (Artificial Intelligence, Machine Learning and Deep Learning) is not so new, have you heard of accepting selfie as authentication for your shopping bill payment, Siri on your iPhone etc. A Decentralized Autonomous Organization (DAO) is a process that manifests these characteristics.

Laurent is a Senior Research Associate at the University of Cambridge, where his work focuses on the development and application of machine learning methods to understand high throughput biological data. Alternatively, Barbara Fusinska will be doing an all-day session on Practical Deep Learning with TensorFlow. Using an interactive learning platform, attendees will have a practical opportunity to use TensorFlow when building deep networks, training them and evaluating the results. After two days of conference sessions, either workshop is an excellent opportunity to dive deep on the fundamentals of machine learning and deep networks.

Laurent is a Senior Research Associate at the University of Cambridge, where his work focuses on the development and application of machine learning methods to understand high throughput biological data. Alternatively, Barbara Fusinska will be doing an all-day session on Practical Deep Learning with TensorFlow. Using an interactive learning platform, attendees will have a practical opportunity to use TensorFlow when building deep networks, training them and evaluating the results. After two days of conference sessions, either workshop is an excellent opportunity to dive deep on the fundamentals of machine learning and deep networks.

Spend less time and money on IT, and more time on data science. DGX Station can save you hundreds of thousands of dollars in engineering hours and lost productivity, waiting for stable versions of open source frameworks. Enjoy productive experimentation at your desk now, and then scale your work in the data center on NVIDIA DGX-1, or in the cloud--all powered by the NVIDIA GPU Cloud Deep Learning Stack and container technology.