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Learn how to build an interactive text analytics solution with customization using IBM Data Science Experience, Python NLTK, IBM Cloud services, Watson services, and Orient DB.
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Learn how to build your own data set and train a TensorFlow model for image classification on a Kubernetes cluster.
Build a custom model using Watson Natural Language Understanding and Watson Knowledge Studio.
Create charts and graphs for Sentiment, Emotional Tone, and Keywords for Twitter handles and hashtags.
Create a mobile application leveraging TensorFlow that will recognize and translate handwritten Korean characters.
Dive into machine learning by performing an exercise on IBM Data Science Experience using Apache SystemML.
This developer pattern demonstrates the key elements of creating a recommender system by using Apache Spark and Elasticsearch.
Correlate content across documents using Python NLTK, Watson Natural Language Understanding (NLU) and IBM Data Science Experience (DSX)
The new Watson Conversation Slots feature allows you to create a complex dialog with fewer nodes. Using slots in this example, we can define the fields in one dialog node and handle the logic in a single node.
Use the IBM Watson Node.js SDK to create a web UI app that enriches multimedia files using speech-to-text conversion, tone analysis, natural language understanding, and visual recognition processing.
Create a cognitive news app in Node.js to deliver custom alerts via email for specific products or brands mentioned in the news. The app can also alert user on brand sentiment, related products as well as stock price changes.
Get a head start on developing connected devices in the home. Using Node-RED and Watson IoT APIs, you'll learn how to build a cognitive IoT app that detects irregularities in the voltage of your connected household devices.
Use transfer learning to leverage the TensorFlow Inception model and create your own image classifier using a PowerAI Jupyter Notebook.
Learn how to pull data points -- concepts, entities, categories, keywords, sentiment, emotion, etc. -- from Hacker News articles using natural-language service calls from a Swift-based application.
Discover how simple it is to build a home automation hub using natural-language services and OpenWhisk serverless technology.
Create a banking chatbot with conversation, natural language understanding, anger detection, and answer discovery from FAQ documents.
Use a TJBot with Watson services, Twilio, and FantasyData.com to create your own interactive sports buddy that can help you follow your favorite team, get game reminders, stats, and more.
Create a Watson Conversation-based financial chatbot that enables you to query your investments, analyze securities, and use multiple interfaces.
Efficiently build powerful deep learning applications and improve your machine learning speeds quickly.
Add IBM Watson Speech-to-Text and Conversation services to a virtual reality environment built in Unity, the popular 3D development platform.
Build a configurable, retail-ready chatbot.
Alexa is the voice service behind products like the Amazon Echo. In this tech talk replay, learn how to use IBM Cloud Functions (based on Apache OpenWhisk) to integrate Alexa with Watson Conversation.
Learn how you can add intelligence to a NAO robot using IBM Data Science Experience and IBM Conversation service.
Discover how to combine the power of a Jupyter Notebook, PixieDust, and IBM Watson™ cognitive services to glean useful marketing insight from a vast body of unstructured Facebook data. Learn how to improve brand perception, product performance, customer satisfaction, and audience engagement by taking data from a Facebook Analytics export.
In this tech talk replay, IBM Developer Advocate Anamita Guha provides an overview of the new Bot Asset Exchange and shows you how you can use it to start creating chatbots today.
Developer advocates Cullen Taylor and Spencer Krum talk about a new project called Rotisserie. Rotisserie is an open source IBM project focused on bringing the concept of the “red zone” in American football to live Twitch streams. Rotisserie focuses on the incredibly popular battle royale video game PlayerUnknown’s Battlegrounds and uses object character recognition technology to interpret how far into a game a particular Twitch streamer is.
This tech talk showcases an easily configurable chatbot that demonstrates a natural language shopping experience for customers.
This video shows a demonstration of how to use Node-RED, IBM Bluemix and Watson Conversation service to quickly create applications that utilize data from NASA in preparation for the NASA SpaceApp Challenge.
In this replay, members of the Watson Developer Cloud Swift SDK development team walk you through the process of building Swift apps that use Watson and also introduce you to some application demos that use the SDK. They demonstrate Cognitive Concierge, a restaurant recommendations app written entirely in Swift, which is built using the Kitura web framework and uses the Swift SDK to integrate the Watson Conversation, Speech to Text, and Text to Speech services to enable a conversation with Watson. The Alchemy Language API is used to analyze positive and negative sentiments in reviews, which helps the user decide which restaurant they should go to.
IBM TJBot is the first kit in the collection of IBM Watson Maker Kits, do-it-yourself open source templates to connect to Watson services in a fun way. TJBot creator and IBM Research “Cool Things Czar” Maryam Ashoori and IBM Researcher Victor Dibia provide a closer look at TJBot, Watson services, and Raspberry Pi-based recipes. You can 3D-print or laser cut the TJBot robot frame and use the recipes to bring TJBot to life.
In this presentation, IBM Senior Software Engineer Brian Burns takes a deeper dive into EclairJS and how it interacts with Apache Spark.
Apache SystemML project leader Luciano Resende explains Apache SystemML and Declarative Machine Learning (DML). Apache SystemML includes linear algebra primitives, statistical functions, and ML-specific constructs that make it easier and more natural to express ML algorithms, making data scientists more productive and freeing data from the underlying input formats and physical representations. Apache SystemML also provides automatic optimization to ensure efficiency and scalability, and runs in MapReduce or Spark environments.
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