In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways.

In this one, you will learn how to create a Neural Network (NN) and use it for deciding whether a student has alcohol consumption problems. Do students drink too much? How can you predict that? How much too much is exactly? Those questions might be difficult to answer, yet we can start somewhere.

This is an interesting course on applications of linear algebra in data science. The course will first take you through fundamentals of linear algebra. Then, it will introduce you to applications of linear algebra for recognizing handwritten numbers, ranking of sports team along with online codes. The course is open for enrollment.

Machine learning (ML) has received a lot of attention recently, and not without good reason. It has already revolutionized fields from image recognition to healthcare to transportation. "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." Not very clear, is it? This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn.

Organizations around the world are producing accurate data-based predictions and benefitting from insightful analysis in a fraction of the time required by conventional tools and methods. This is the power of machine learning automation. In this webinar, learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors. Built for speed and scalability, DataRobot radically reduces the time required to complete a data science project. From data to deployment, with DataRobot you can deliver highly-accurated predictions faster, react quickly to rapidly changing market conditions, and speed the transformation of your business.

But you don't know where to start, or perhaps you have read some theory, but don't know how to implement what you have learned. This tutorial will help you break the ice, and walk you through the complete process from importing and analysing a dataset to implementing and training a few different well known classification algorithms and assessing their performance. I'll be using a minimal amount of discrete mathematics, and aim to express details using intuition, and concrete examples instead of dense mathematical formulas. You can read why here. We will be classifying flower-species based on their sepal and petal characteristics using the Iris flower dataset which you can download from Kaggle here. Kaggle, if you haven't heard of it, has a ton of cool open datasets, and is a place where data scientists share their work which can be a valuable resource when learning.

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

Apple has revealed iOS 11, the future of the iPhone. The software comes with a range of new features, including changes to Messages and other iOS apps. But it also added a range of "technologies" – new tweaks intended to make the apps run much better. That was in keeping with a focus in Apple on improving the experience of its software, turning up and tweaking things rather than adding a host of new features, something it also did in macOS. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

To meet the growing demand for AI expertise, companies are offering online education courses to prepare the workforce for the future. Increasingly, computers and devices learn and act on their own using software algorithms, the building blocks for artificial intelligence (AI) and machine learning (ML). Getting smartphones to understand voice commands, smart home sprinkler systems to change with the weather and online services to predict what people want requires programmers skilled in AI and ML. Demand for these coding skills is skyrocketing. Making devices smart and proactive remains controversial to anyone who fears that automation will lead to human job loss.

Google is using machine learning to teach computers to sketch and make music, but one engineer says it isn't ready to "generate" a new Beatles album just yet. IN the future, cars will drive themselves, fridges will order groceries, and doors will unlock automatically as you approach. But what happens when computers move beyond chores and take on creative endeavours? What happens when computers start making art? It's a question Google is investigating, not only investing money in making computers code the most efficient programs themselves, but asking them to learn how to draw, and make their own music based on our own.