The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications.

This seems to be the very first book on this ML framework (H2O). And is is just great. The book is crystal clear and extremely comprehensive, very easy to read, with examples you can reproduce easily (datasets are on line in a public Git repo). It covers a very practical ground on the 4 main algorithms implemented in H2O cluster: RandomForest, GBM, GLM, and last but not least: deep learning... "Practical" means explanations are strongly grounded on a set of 4 datasets, the author plays with, explaining both their preparation, analysis with H2O (code is both in R and PYTHON), and a great deal of time is spent on very useful considerations on how to'tune' the various algorithms to obtain better models, comparing their effectiveness. A must have for everyone interested in implementing ML features concretely.

In an effort to build new ways of collaborating digitally, we're opening up ALL of our new startup's files to you.

MIAMI, Feb. 3, 2017 /PRNewswire/ Men spend around 65% more than women when it comes to show their love on Valentine's day and jewels are their favorite gift to give, according to the analysis of consumer behavior patterns performed by Poder.IO, the artificial intelligence company for marketing and customer behavior prediction. According to Poder.IO, jewels are the preferred gift by men during Valentine's around the globe, even over flowers or romantic dinners. They spend over 210 dollars per gift, and women, a little bit more money savvy, spend around 127 dollars to surprise their significant other. Next to the jewels, clothing is the other favorite item of men with an average spend of 112.71 dollars. Those who don't want to splurge too much, but still want to make a gift go for gift cards, spending around 106 dollars.

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand.

With Valentine's Day just around the corner, let's consider a company that we can all learn a few customer service lessons from, 1-800-Flowers. The company is innovative and totally focused on the customer. It all started in 1976. Jim McCann was an entrepreneur who owned several flower shops in the New York City area. Ten years later, he acquired the 1-800-Flowers phone number from another company that was going out of business.

Humans develop a common sense of style compatibility between items based on their attributes. We seek to automatically answer questions like "Does this shirt go well with that pair of jeans?" In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper. The basic assumption of our approach is that most of the important attributes for a product in an online store are included in its title description. Therefore it is feasible to learn style compatibility from these descriptions. We design a Siamese Convolutional Neural Network architecture and feed it with title pairs of items, which are either compatible or incompatible. Those pairs will be mapped from the original space of symbolic words into some embedded style space. Our approach takes only words as the input with few preprocessing and there is no laborious and expensive feature engineering.

An effort to bring Target stores into future has reportedly been nixed before it could ever see the light of day. As Recode reports, Target's aptly named "Store of the Future" project, which would have put the retailer in direct competition with Amazon's forthcoming cashier-free Go stores, was abruptly canceled after a disappointing holiday season. According to Recode's sources, the new, smaller stores were slated to debut sometime this year and an internal team had already begun building out a test version. Instead of the normal rows and aisles most Target customers are used to, the Store of the Future would look more like a showroom and employ a team of robots to pick up items and bring them to customers at checkout. The stores also had an e-commerce component and Target intended to use the spaces to encourage other, non-retail experiences and community gatherings.

Amazon's Alexa was showered with attention at last month's CES tech show in Las Vegas, as dozens of companies invited the voice assistant to live in their cars, washing machines and set-top boxes. Yet, behind all that luster is a somewhat uncomfortable question about Amazon's 2-year-old artificial intelligence platform: Does Alexa make Amazon any money? The answer, say several analysts: No, but it will. And when it does, it'll be huge. As part of Amazon's fourth-quarter earnings report on Thursday, the company mentioned that Alexa-powered devices were Amazon's top-selling products this holiday season.

At NRF, retailers saw solid ideas and use cases for the implementation of AR/VR, helping them make the case for ROI. Machine learning will help retailers use these insights to create personalized experiences, forecast demand and set prices in more sophisticated ways. Predictive Artificial Intelligence 5. Watson Customer Engagement 1/31/2017Engaging Mobile Experiences5 Engaging Mobile Experiences Mobile continues to be a high priority for retailers. Even now, when most retailers have brand new apps and mobile-optimized websites, sales on mobile are not on par with desktop traffic. Retailers are challenging themselves to deliver experiences on mobile that not only engage, but also convert.