Distributed machine learning algorithms enable processing of datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional indeed happen in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional distributed learning tasks. In this paper, two variants of an algorithm termed Byzantine-resilient distributed coordinate descent (ByRDiE) are developed and analyzed that solve distributed learning problems in the presence of Byzantine failures. Theoretical analysis as well as numerical experiments presented in the paper highlight the usefulness of ByRDiE for high-dimensional distributed learning in the presence of Byzantine failures.

Artificial Intelligence, Machine Learning and Deep Learning are all the rage in the press these days, and if you want to be a good Data Scientist you're going to need more than just a passing understanding of what they are and what you can do with them. There are loads of different methodologies, but for me I would always suggest Artificial Neural Networks as the first AI to learn - but then I've always had a soft spot for ANNs since I did my PhD on them. They've been around since the 1970s, and until recently have only really been used as research tools in medicine and engineering. Google, Facebook and a few others, though, have realised that there are commercial uses for ANNs, and so everyone is interested in them again. When it comes to algorithms used in AI, Machine Learning and Deep Learning, there are 3 types of learning process (aka'training').

Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon.

In this paper, we introduce R-NET, an end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation. Then we propose a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage. We finally employ the pointer networks to locate the positions of answers from the passages. We conduct extensive experiments on the SQuAD and MS-MARCO datasets, and our model achieves the best results on both datasets among all published results.

You may have heard about machine learning from interesting applications like spam filtering, optical character recognition, and computer vision. Getting started with machine learning is long process that involves going through several resources. There are books for newbies, academic papers, guided exercises, and standalone projects. It's easy to lose track of what you need to learn among all these options. So in today's post, I'll list seven steps (and 50 resources) that can help you get started in this exciting field of Computer Science, and ramp up toward becoming a machine learning hero.

Andrew Ng taught one of the most-viewed online courses of all time--more than 1.5 million people have registered to take one of the many sequences of his free online course about machine learning. That experience spurred him to co-found Coursera. Today Ng announced that this summer he's launching sequels to that blockbuster, with a series of courses on the AI concept known as deep learning. For the past two years Ng had been applying concepts of deep learning in the commercial sector, as a chief scientist for the Chinese tech giant Baidu. But he left that company in March, and since then has been working on three undisclosed projects in AI.

Machine learning is increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this course, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the course, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

Lipo Wang's research interests are in computational intelligence, i.e., neural networks, evolutionary computation, and fuzzy systems, with applications to multimedia, bioinformatics, and data mining. He has published over 50 journal publications, 14 books (authored/edited), 70 conference presentations, and 7 book chapters. He holds a U.S. patent on a neural network for image sequence processing. He is an Associate Editor / Editorial Board member for 7 international journals, including IEEE Transactions on Neural Networks, IEEE Transactions on Evolutionary Computation. He is Chair of the Emergent Technologies Technical Committee, IEEE Neural Networks Society.

On 24th May the Fieldfisher Silicon Valley team attended the Churchill Club's Top Tech Trends event in Santa Clara, CA where 5 leading VC's made their predictions for the next 5 years. The question posed was "What new tech trends will emerge with the potential for explosive growth in 5 years?", and is one posed annually to 5 leading Silicon Valley venture capitalists at the Churchill Club's Top Tech Trends debate. On pitching their trends for the future the 500 strong audience were then given the chance to vote and express their view. So, what is the future going to look like and did we just witness the introduction of a tech trend which everyone will know about within 5 years? That the trend is'non-obvious' today; and The panellists voted and critically appraised each other's predictions and then the vote was handed over to the audience.