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Machine Learning with C - Polynomial Regression on GPU

@machinelearnbot

Hello, this is my second article about how to use modern C for solving machine learning problems. This time I will show how to make a model for polynomial regression problem described in previous article, but now with another library which allows you to use your GPU easily. For this tutorial I chose MShadow library, you can find documentation for it here. This library was chosen because it is actively developed now, and used as a basis for one of a wide used deep learning framework MXNet. Also it is a header only library with minimal dependencies, so it's integration is not hard at all.


Cloud data and AI services training roundup May 2018

#artificialintelligence

To help you stay up to date on online training opportunities, we're releasing a monthly list of the latest free Data and Artificial Intelligence (AI) sessions in one convenient post. Azure SQL Database is the intelligent, fully managed relational cloud database service that provides the broadest SQL Server engine compatibility, so you can migrate your SQL Server databases without changing your apps. Accelerate app development and make maintenance easy and productive using the SQL tools you love to use. Here's a rundown of recent and upcoming training sessions to help you learn more. Getting ahead means embracing digital transformation to leverage the cloud.


Model Selection & Validation - ROC Curve - An Example Part-7

#artificialintelligence

A lab excercise is show cased to calculate ROC and AUC for a sample data set of logistic regression model. Learn and apply the practical code to test the data. Data Scientists take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage and organize. But worry not we are here to the rescue and teach you how to be a data scientist, more importantly, upgrade your analytic skills to tackle any problem in the field of data science. Join us on "statinfer.com" for becoming a "scientist in data science" Our "Machine Learning" course is now available on Udemy https://www.udemy.com/machine-learnin... Part 1 โ€“ Introduction to R Programming.


Teasing innovative acumen, the IoT way

#artificialintelligence

What if a car is operated by hand gestures or an automated rover that cleans the floor? Well, around 90 engineering students from Andhra Pradesh and Telangana are working on such innovative concepts to make them a reality. The students, all final year engineering students from reputed educational institutions in the two Telugu States, are here in the city for a two-month internship programme as part of which they work on several artificial intelligence-based projects and developing apps to understand the nitty-gritty of the Internet of Things (IoT). Students from National Institute of Technology (NIT) Warangal, GITAM College of Engineering in Visakhapatnam and GMR Institute of Technology in Srikakulam among others have formed a talent pool that is working simultaneously on various software projects. They have made two floors of a three-storied building at Atchampeta on the city outskirts their workplace while they are putting up in paying guest accommodations and rented houses near their office premises.


Artificial intelligence: 4 truths CIOs should know

#artificialintelligence

When asked to describe their artificial intelligence efforts to date, many IT leaders attending the MIT Sloan CIO Symposium used two words: "slow" and "hype." As that exercise illustrates, many CIOs are still struggling to cut through the noise and better understand how AI and machine learning will impact their businesses moving forward. Are robots coming to take our jobs? Are we going to fall exponentially behind our competitors if we can't hire the best data scientists to deal with AI? The speakers on stage had some answers to these and other pressing questions around AI.


Cork is Ireland's creative and tech city of tomorrow General, news for Ireland, Ireland,

#artificialintelligence

Ireland is witnessing a new age of technological and creative innovation with Cork, Dublin and Galway ranking highest in terms of new ways of sharing, creating and innovating respectively. This is according to the New Renaissance Hotspots Report, commissioned by Huawei and authored independently by the Institute of Arts and Ideas and Kjaer Global. The in-depth analysis scored cities based on key indicators, such as the number of creative and knowledge-based jobs, levels of engagement with digital creativity and cultural activities, as well as the diversity of integration with new technologies. It also considered how well cities were prepared for the future, analysing the number of graduates in key creative and technology-based fields and looking at applications for patents in ICT. The study predicts that the New Renaissance will be responsible for the creation of more than 1.1 million jobs across Europe by 2025 and 1.47 million jobs by 2030.


Multilingual Natural Language Processing Applications: From Theory to Practice

@machinelearnbot

Multilingual Natural Language Processing Applications is the first comprehensive single-source guide to building robust and accurate multilingual NLP systems. Edited by two leading experts, it integrates cutting-edge advances with practical solutions drawn from extensive field experience. Part I introduces the core concepts and theoretical foundations of modern multilingual natural language processing, presenting today's best practices for understanding word and document structure, analyzing syntax, modeling language, recognizing entailment, and detecting redundancy. Part II thoroughly addresses the practical considerations associated with building real-world applications, including information extraction, machine translation, information retrieval/search, summarization, question answering, distillation, processing pipelines, and more. This book contains important new contributions from leading researchers at IBM, Google, Microsoft, Thomson Reuters, BBN, CMU, University of Edinburgh, University of Washington, University of North Texas, and others.


360 Live VR Teleportation Uses Drones, Neural Networks, and Perseverance

@machinelearnbot

This past semester I added research to my already full schedule of math and engineering classes, as any masochistic student eagerly would. Packed schedule aside, how do you pass up the chance to work on implementing 360 virtual teleportation to anywhere in the world, in real-time. Yes, it is indeed the same concept as the cult worshipped Star Trek transporter, minus the ability to physically be at the location. Perhaps we can add a, "beam me up, Scotty" command when shutting down. The research lab I was working with is the Laboratory for Immersive CommunicatiON (LION).


In Artificial Intelligence, Young Ethiopians Eye a Fertile Future

#artificialintelligence

"I don't think Homo sapiens-type people will exist in 10 or 20 years' time," Getnet Assefa, 31, speculates as he gazes into the reconstructed eye sockets of Lucy, one of the oldest and most famous hominid skeletons known, at the National Museum of Ethiopia. "Slowly the biological species will disappear and then we will become a fully synthetic species," Assefa says. "I believe [we] can inspire the Ethiopian youth to actually get really engaged in AI and feel like it's their thing." "Perception, memory, emotion, intelligence, dreams -- everything that we value now -- will not be there," he adds. Assefa is a computer scientist, a futurist, and a utopian -- but a pragmatic one at that. He is founder and chief executive of iCog, the first artificial intelligence (AI) lab in Ethiopia, and a stone's throw from the home of Lucy.


KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings

arXiv.org Machine Learning

Question answering (QA) has been a longstanding challenge in the field of artificial intelligence. Numerous research works have pushed forward techniques for building QA systems. Many existing approaches achieve high performance on benchmark datasets. However, most of the questions in those datasets only require surface-level reasoning, and do not reveal the full-scale complexity and challenge of the question answering problem. Recently, the AI2 Reasoning Challenge (ARC) has been proposed [Clark et al., 2018], which is designed to pose a challenge to the QA community. On the ARC Challenge Set, several state-of-the-art QA systems, including leading neural models from the well-known SQuAD and SNLI tasks, only perform slightly better than the random baseline. This striking observation has demonstrated that QA is still far from being solved. Why it is so difficult to answer the questions in the ARC Challenge Set? 1) ARC consists of natural science questions, namely questions authored for human exams. All of these questions are drawn from real exams; 2) In order to encourage progress on hard questions, a Challenge Set has been partitioned from ARC.