Recommender systems are one of the most common used and easily understandable applications of data science. Lots of work has been done on this topic, the interest and demand in this area remains very high because of the rapid growth of the internet and the information overload problem. It has become necessary for online businesses to help users to deal with information overload and provide personalized recommendations, content and services to them. Two of the most popular ways to approach recommender systems are collaborative filtering and content-based recommendations. In this post, we will focus on the collaborative filtering approach, that is: the user is recommended items that people with similar tastes and preferences liked in the past.
When Amazon unveiled the Echo Show last year, many people made fun of it for its bulky, awkward appearance. But it proved to be a pioneer in the smart display category, showing that adding a screen to a voice assistant was actually useful. So much so, that Google followed a few months later with its own line of Echo Show rivals, thanks to partners like Lenovo and JBL. Google's smart displays were better-looking and had a more intuitive interface, with desirable features like step-by-step recipes and YouTube integration. Amazon must have taken note of the competition, however, because the new Echo Show has undergone a serious upgrade, with an improved design, superior sound quality and enhanced entertainment options.
It's Monday, which means back to work and school, no matter who or where you are. It also means another week of some great deals. Whether you're looking for a coffee maker or a new TV (or lots in between), there's plenty to choose from thanks to sales at Amazon, Walmart, and Target. When it comes to the kitchen, there are plenty of coffee makers and small appliances to choose from. This Hamilton Beach Coffee Maker is available for $35.49 if you're looking for something simple to use, while the Cuisinart SS-10 Premium Single-Serve Coffeemaker is great for people who need a quick caffeine jolt.
An amateur sex toy inventor has created a smart speaker that doubles up as a voice-controlled dominatrix. British engineer Gary, who keeps his surname anonymous, created his discipline device using parts of an electrified dog collar and an Amazon Echo Dot speaker. Known as mistress Alexa, the gadget administers shocks to its wearer's genitals following a short conversation that users initiate with the phrase'Alexa, punish'. Gary, who built the device for partner Kirsty, has posted a tutorial video to YouTube to help other sex toy enthusiasts build their own dominatrix technologies. An amateur sex toy inventor has created a smart speaker that doubles up as a voice-controlled dominatrix.
Google's Assistant, its answer to Amazon's Alexa and Apple's Siri, is getting smarter, more visual, and potentially, more helpful. At the I/O conference in Mountain View, Calif., Google put the spotlight on the assistant, bringing new voices, including one from singer John Legend, and more visuals. Additionally, Google has beefed up voice commands for its popular Maps app, bringing the Assistant to the feature in the summer. Google execs offered demos on new iPad-like Smart Displays coming from Lenovo and Google later in the year, which will allow voice navigation via the Google Assistant to say, watch Jimmy Kimmel Live via YouTube TV or order lattes from Starbucks. Google emphasized that visuals will be coming to the Google Assistant app, to marry voice navigation with tools like food recipes, where you'll get spoken step-by-step instructions, along with video.
There is a shift happening in the way we as a species communicate with machines. With the advent of Amazon Alexa, Google Assistant, Apple Siri, and Microsoft Cortana, the focus on Voice User Interfaces or Voice Activated Conversational Interfaces is rapidly increasing. This ever changing world presents a threat to the way we operate, especially when we do not understand it. A more AI aware world might be years away, but if we learn how to talk to and control the machines then we grow collectively. Yes, Amazon Alexa and similar voice activated interfaces look and sound pretty cool.
Topics: This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting'rating', learning about user'preference' and so on. You will exclusively work on data related to user details, movie details and others.
Today we're going to talk about how computers understand speech and speak themselves. As computers play an increasing role in our daily lives there has been an growing demand for voice user interfaces, but speech is also terribly complicated. Vocabularies are diverse, sentence structures can often dictate the meaning of certain words, and computers also have to deal with accents, mispronunciations, and many common linguistic faux pas. The field of Natural Language Processing, or NLP, attempts to solve these problems, with a number of techniques we'll discuss today. And even though our virtual assistants like Siri, Alexa, Google Home, Bixby, and Cortana have come a long way from the first speech processing and synthesis models, there is still much room for improvement.
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users' and items' attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user's attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition.