LVMH-owned beauty retailer Sephora is doubling down on augmented reality and artificial intelligence sales tactics by enabling shoppers to virtually try on false lashes, watch tutorials using their own image and engage via a chatbot to trial and purchase lip color. With Sephora's customers virtually trying on more than 70 million lip shades using the Virtual Artist in-app functionality that was introduced earlier this year, false lashes are being added to expand the program. Users of the Sephora application can also now experience live step-by-step makeup application tutorials using their own uploaded images and augmented reality technology. "This is a significant expansion because we are adding elements that we know will help empower and educate our clients' purchase making decisions, and they're done in a way that is fun and engaging," said Bridget Dolan, vice president of Sephora Innovation Lab. "The new Live Tutorials especially are a game changer for our users," she said.

It's been called the'sexiest job of the 21st century', the'hottest job of the decade', and is the fastest-growing field in tech at the moment – the impact of Data Science in today's world cannot be overstated. As a discipline, data science involves the collection and study of data – both structured and unstructured – to gain insights and information that can be used by organizations to devise effective strategies. By collating data over a period of time, patterns can be identified that enable companies to find new market opportunities, enhance efficiency, reduce costs, and place themselves at a competitive advantage in their industry. Due to rapid technological advances, especially in areas like mobile advertising, social media, and website personalization, a massive amount of data is being generated on a daily basis. These data volumes have resulted in industries having to become data-savvy & adapt to the new landscape – or risk falling behind the competition.

In this capstone project, you will leverage what you've learned throughout the Nanodegree program to solve a problem of your choice by applying machine learning algorithms and techniques. You will first define the problem you want to solve and investigate potential solutions and performance metrics. Next, you will analyze the problem through visualizations and data exploration to have a better understanding of what algorithms and features are appropriate for solving it. You will then implement your algorithms and metrics of choice, documenting the preprocessing, refinement, and postprocessing steps along the way. Afterwards, you will collect results about the performance of the models used, visualize significant quantities, and validate/justify these values.

During the company's Worldwide Partner Conference, Microsoft has unveiled its upcoming plans to provide online degrees that cater to the demands of highly competitive technological fields. Officially launched as the Microsoft Professional Degree (MPD) program, the first course offered to interested professionals and fresh graduates alike, mainly focuses on skill development and education through a Data Sciences curriculum. "Recognizing a shortage of qualified individuals to fill the growing need for data scientists, Microsoft consulted with education and industry partners to develop a curriculum concentrated on developing the skills and real world experience these new roles require," says Microsoft. This specific MPD program features courses that educate incoming applicants on how they can visualize and implement data in Microsoft Excel and Power BI, as well as supplemental (and needed) skills in R and Python programming language, statistics and machine learning. "At Microsoft, we believe the approach and tools used for learning need to continually evolve to meet the demands of our device-centric and data-driven world," said Alison Cunard, the general manager at Microsoft Learning Experiences.

You can watch the Azure ML tutorial video at the landing page to have a quick overview about the Azure ML service. This will open the page where we will develop our Azure ML experiments in the next labs.

In the last few months the Internet has been flooded with deep dreams: images augmented by neural networks which look incredibly trippy. Deep dreams have the potential to become the new fractals; beautifully backgrounds everyone knows are related to Maths, but no one knows really how. What are deep dreams, how are they generated and what can they teach us? A neural network gets an image as an input, and returns a classification result: yes, that's a face. It achieves this by recognising features in an hierarchical fashion.

Teachers are not ends-in-themselves, they are always a means to an end - improvements in the learner. Given this premise, could it be possible to eventually replace teachers with AI technology? This may not happen soon but let's, as a thought experiment, ask whether it could. Obvious points are that AI is 24/7, fast, scalable and cheaper. This gives it a head start.

This course will help you to gain the skills to use one of the fastest growing mobile technologies, Chatbots. Now you too can learn to build sophisticated Chatbots for your customers all with NO Coding. This course is for those in Web design, marketing and graphics, who want to be able to offer their clients something new and exciting. I will cover Chatbots for Websites, for Facebook, for KiK, and for Slack – although the bots created can be used also on many other services. I will show you examples of travel bots, entertainment bots, productivity bots, and retail bots.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

I've worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS - a Javascript library for training Neural Networks. Javascript allows one to nicely visualize what's going on and to play around with the various hyperparameter settings, but I still regularly hear from people who ask for a more thorough treatment of the topic. This article (which I plan to slowly expand out to lengths of a few book chapters) is my humble attempt. It's on web instead of PDF because all books should be, and eventually it will hopefully include animations/demos etc. My personal experience with Neural Networks is that everything became much clearer when I started ignoring full-page, dense derivations of backpropagation equations and just started writing code. Thus, this tutorial will contain very little math (I don't believe it is necessary and it can sometimes even obfuscate simple concepts). Since my background is in Computer Science and Physics, I will instead develop the topic from what I refer to as hackers's perspective. Basically, I will strive to present the algorithms in a way that I wish I had come across when I was starting out. "…everything became much clearer when I started writing code." You might be eager to jump right in and learn about Neural Networks, backpropagation, how they can be applied to datasets in practice, etc. But before we get there, I'd like us to first forget about all that. Let's take a step back and understand what is really going on at the core. Update note: I suspended my work on this guide a while ago and redirected a lot of my energy to teaching CS231n (Convolutional Neural Networks) class at Stanford. The notes are on cs231.github.io These materials are highly related to material here, but more comprehensive and sometimes more polished. In my opinion, the best way to think of Neural Networks is as real-valued circuits, where real values (instead of boolean values {0,1}) "flow" along edges and interact in gates. However, instead of gates such as AND, OR, NOT, etc, we have binary gates such as * (multiply), (add), max or unary gates such as exp, etc.