One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
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.
Text classification implementation with TensorFlow can be simple. One of the areas where text classification can be applied -- chatbot text processing and intent resolution. I will describe step by step in this post, how to build TensorFlow model for text classification and how classification is done. Please refer to my previous post related to similar topic -- Contextual Chatbot with TensorFlow, Node.js and Oracle JET -- Steps How to Install and Get It Working. I would recommend to go through this great post about chatbot implementation -- Contextual Chatbots with Tensorflow.
Chatbots are one of the most exciting and in-demand topics in tech. Gartner predicts that by 2020, 85% of businesses will have their own chatbot. If you want to learn this rapidly emerging technology, put a chatbot on your own website or make money by building chatbots for clients, this free chatbot course is for you. This course provides a practical introduction on how to build a chatbot with Watson Assistant (formerly Watson Conversation). Within it, you'll learn how to plan, build, test, analyze, and deploy your first chatbot.
Here's a list of some of the best Chatbot tutorials, courses, videos and books to help you learn Chatbots in 2018. ChatBots: How to Make a Facebook Messenger Chat Bot in 1hr by Stefan Kojouharov will help you build a Chat Bot for Facebook Messenger. This is a step by step guide in building a chatbot for Facebook Messenger. You will learn the main components of building a chatbot. This includes building the chatbot server, adding your code, deploying your chatbot to the cloud, and connecting it with Facebook Messenger.
In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a spam detector.
In this tutorial, we will cover Natural Language Processing for Text Classification with NLTK & Scikit-learn. Remember the last Natural Language Processing project we did? We will be using all that information to create a Spam filter. This tutorial will also cover Feature Engineering and ensemble NLP in text classification. This project will use Jupiter Notebook running Python 2.7.
This is part one of a three-part tutorial series in which you will use R to perform a variety of analytic tasks on a case study of musical lyrics by the legendary artist, Prince. Musical lyrics may represent an artist's perspective, but popular songs reveal what society wants to hear. Lyric analysis is no easy task. Because it is often structured so differently than prose, it requires caution with assumptions and a uniquely discriminant choice of analytic techniques. Musical lyrics permeate our lives and influence our thoughts with subtle ubiquity. The concept of Predictive Lyrics is beginning to buzz and is more prevalent as a subject of research papers and graduate theses. This case study will just touch on a few pieces of this emerging subject. To celebrate the inspiring and diverse body of work left behind by Prince, you will explore the sometimes obvious, but often hidden, messages in his lyrics. However, you don't have to like Prince's music to appreciate the influence he had on the development of many genres globally. Rolling Stone magazine listed Prince as the 18th best songwriter of all time, just behind the likes of Bob Dylan, John Lennon, Paul Simon, Joni Mitchell and Stevie Wonder. Lyric analysis is slowly finding its way into data science communities as the possibility of predicting "Hit Songs" approaches reality.
Sentiment analysis is the automated process of understanding an opinion about a given subject from written or spoken language. In a world where we generate 2.5 quintillion bytes of data every day, sentiment analysis has become a key tool for making sense of that data. This has allowed companies to get key insights and automate all kind of processes. But… How does it work? What are the different approaches? What are its caveats and limitations? How can you use sentiment analysis in your business? Below, you'll find the answers to these questions and everything you need to know about sentiment analysis. No matter if you are an experienced data scientist a coder, a marketer, a product analyst, or if you're just getting started, this comprehensive guide is for you. How Does Sentiment Analysis Work? Sentiment Analysis also known as Opinion Mining is a field within Natural Language Processing (NLP) that builds systems that try to identify and extract opinions within text. Currently, sentiment analysis is a topic of great interest and development since it has many practical applications. Since publicly and privately available information over Internet is constantly growing, a large number of texts expressing opinions are available in review sites, forums, blogs, and social media. With the help of sentiment analysis systems, this unstructured information could be automatically transformed into structured data of public opinions about products, services, brands, politics, or any topic that people can express opinions about. This data can be very useful for commercial applications like marketing analysis, public relations, product reviews, net promoter scoring, product feedback, and customer service. Before going into further details, let's first give a definition of opinion. Text information can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about something. Opinions are usually subjective expressions that describe people's sentiments, appraisals, and feelings toward a subject or topic. In an opinion, the entity the text talks about can be an object, its components, its aspects, its attributes, or its features.
This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one--learning how to get a GPU server online suitable for deep learning--and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF.