You do not have to model your time series forecast problem as-is. There are many ways to reframe your forecast problem that can both simplify the prediction problem and potentially expose more or different information to be modeled. A reframing can ultimately result in better and/or more robust forecasts. In this tutorial, you will discover how to reframe your time series forecast problem with Python. How to Reframe Your Time Series Forecasting Problem Photo by Sean MacEntee, some rights reserved.

From location beacons to artificial intelligence here is a comprehensive guide to what I predict will be the biggest marketing trends for the New Year. Pokémon Go brought augmented reality to the forefront for many marketers and we expect advertisers outside of the gaming realm to dive deep into this space in 2017. AR and VR technologies remain largely uncharted territory with literally endless creative possibilities. With this technology, the viewer becomes an integral part of the brand experience as opposed to a mere observer. Instead of a call to action, they are the action.

That's the premise of Nugit, a Singapore-based startup focused on marketing that raised $5.2 million in fresh funding from Sequoia Capital's India fund this week. Nugit landed undisclosed seed funding from 500 Startups and The Hub Singapore last year. Founded by Australia-born marketing executive David Sanderson, Nugit sits between its customers and their data platforms -- it supports 15 right now, including Facebook Ad Manager, Google AdWords and DoubleClick -- to help make sense of realms of information. The idea is to make a digital marketer's job easier by cutting down on noise and producing "decision-ready reports," such as PowerPoint presentations, graphics and other visualizations. The company started out automating many of the processes that marketers are faced with when dealing with data, such as cleaning information and aligning it, until Sanderson -- formerly of GroupM and other ad agencies -- realized that computers could go beyond that and deliver insight that is difficult or seriously time-consuming for humans.

Yojee Ltd, an Australia-based logistics and supply chain management solution supplier, will partner with Post Media, the parent company of the Phnom Penh Post and Post Khmer, to establish its own technology-based logistics platform on top of Post Media's existing daily newspaper delivery network, the company announced yesterday. A memorandum of understanding (MoU) between the two companies inked yesterday will see Yojee deploy a same-day, next-day delivery logistics service in Cambodia that leverages the efficiency and expertise of Post Media's delivery operations, generating new revenue and synergies. The collaborative partnership will employ Yojee's artificial intelligence (AI) logistics and supply-chain management software to manage delivery of freight and parcels in the Kingdoms' fast-growing e-commerce market. Ed Clarke, managing director of Yojee, said Post Media's nationwide business network and over 1 million active readers will provide a solid foundation for his company's entry into the Cambodian market without any additional asset acquisition costs. "Having this audience to market Yojee's delivery capabilities from day one is a coup for Yojee, as it literally saves us years and significant capital expenditure to acquire such a customer base in any other manner," he said.

You do not have to model your time series forecast problem as-is. There are many ways to reframe your forecast problem that can both simplify the prediction problem and potentially expose more or different information to be modeled. A reframing can ultimately result in better and/or more robust forecasts. In this tutorial, you will discover how to reframe your time series forecast problem with Python. How to Reframe Your Time Series Forecasting Problem Photo by Sean MacEntee, some rights reserved.

Predictive analytics, the Internet of Things and machine learning are all being touted as top technologies that will improve engagement with customers, even if marketers can't agree which to prioritise, a new international survey reports. The latest Marketo global survey into the future of technology and its impact on marketing found the trio of emerging technologies were top of the list for international marketers when it comes to how to improve customer engagement. However, respondents in different marketers didn't agree on which to prioritise first. Over half of US marketers, for example (57 per cent), believed predictive analytics will be the primary technology to leverage in order to best engage with customers, compared to only 11 per cent of international marketers. The report also found 37 per cent of all surveyed saw reporting and analytics as a top priority, with US marketers leading the pack (42 per cent versus 34 per cent).

For a few days, it looked like the humans had it figured out. Four poker pros facing off against the Libratus AI in a 20-day no-limit Texas Hold'em competition pulled back from an early $193,000 deficit with big wins on days four and six, bringing the deficit down to to $51,000, with one human, Dong King, up $33,000. "It took us a while to study and get an understanding of what was going on," one of the pros, Jason Les, wrote in an email. But then the bot started winning again and big. By the end of day 10, it was up a likely insurmountable $677,000, with all of the humans down six figures.

Australia's international airports are in the process of automating 90 percent of air traveler processing by 2020 by implementing facial recognition technology that involves biometric recognition of faces, irises and/or fingerprints, hence eliminating the need to carry essential traveling documents such as passports. The Department of Immigration and Border Protection, while building on the Seamless Traveller initiative announced in 2015, said Sunday that it will make a transition toward a "contactless" system for arrivals this year, according to the Sydney Morning Herald. Under the new system, manned counters will be replaced by automatic electronic booths and the existing SmartGate that scan passports electronically will also be overhauled. Before introducing it to a major airport (scheduled for November), the program is to be piloted in July at Canberra Airport where international flight operations are limited to New Zealand and Singapore. The Department of Immigration and Border Protection is scheduled to implement the technology in all major airports by March 2019.

Several types of such systems exists • Closed Loop Systems – based on a predefined irrigation scheme the control system takes over and makes detailed decisions on when and how much water to apply • Open Loop Systems – based on the amount of water to be applied and the timing of the irrigation • Volume Based System – The pre-set amount of water can be applied in the field • Time Based System – works with time clock controllers (Adapted from BOMAN et al. 2006)All rights reserved. USE CASE - Face recognition systems for Domestic Cattle • Facial recognition of cows in dairy units can individually monitor all aspects of behavior in a group, as well as body condition score and feeding. Facial recognition of cows EFFECT OF USAGE • With this system it is possible to feed cows a lot less expensively if you know what they will and will not eat. It is meant to be deployed as a group or "swarm".The other three steps involve autonomous robots that tend the crops, harvest them, and finally one robot that can plant, tend, and harvest autonomously transitioning from one phase to another. Swarm Prospero EFFECT OF USAGE • The application of the system increases the productivity of land on a per unit basis. USE CASE – Strawberry Harvesting Robot • Automated harvester recently wheeled through rows of strawberry plants here, illustrating an emerging solution to one of the produce industry's most pressing problems: a shortfall of farmhands.

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, microscopic image analysis, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. This book describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database. Dr. Yang is the founder of the Biomedical Image Computing and Imaging Informatics (BICI2) lab (http://www.bme.ufl.edu/labs/yang/). His major research interests are focus on biomedical image analysis and imaging informatics, computer vision, biomedical informatics and machine learning.