Displaying 1 to 19 from 19 results

pixiedust-facebook-analysis - A Jupyter notebook that uses the Watson Visual Recognition, Natural Language Understanding and Tone Analyzer services to enrich Facebook Analytics and uses PixieDust to explore and visualize the results

  •    HTML

In this Code Pattern, we will use a Jupyter notebook to glean insights from a vast body of unstructured data. Credit goes to Anna Quincy and Tyler Andersen for providing the initial notebook design. We'll start with data exported from Facebook Analytics. We'll enrich the data with Watson’s Natural Language Understanding (NLU), Tone Analyzer and Visual Recognition.

pixiedust-traffic-analysis - A Jupyter notebook using PixieDust and PixieApps to visualize San Francisco traffic accidents

  •    HTML

In this Code Pattern we will use PixieDust running on IBM Data Science Experience (DSX) to analyze traffic data from the City of San Francisco. DSX is an interactive, collaborative, cloud-based environment where data scientists, developers, and others interested in data science can use tools (e.g., RStudio, Jupyter Notebooks, Spark, etc.) to collaborate, share, and gather insight from their data. The intended audience for this Code Pattern is application developers and other stakeholders who wish to utilize the power of Data Science quickly and effectively.

powerai-market-sentiment - Built for developers familiar with IBM Power systems that are looking to leverage IBM's new PowerAI offering for machine learning

  •    Jupyter

Read this in other languages: 한국어. In this Code Pattern we will use a Jupyter notebook to showcase an example of machine learning with time series on IBM Power8 systems. The notebook will focus on evalulating the predictability of future financial market values in the "renewable energy" sector by examining related markets and sentiment detected in New York Times news articles.

spark-tpc-ds-performance-test - Use the TPC-DS benchmark to test Spark SQL performance

  •    C

Apache Spark is a popular distributed data processing engine that is built around speed, ease of use and sophisticated analytics, with APIs in Java, Scala, Python, R, and SQL. Like other data processing engines, Spark has a unified optimization engine that computes the optimal way to execute a workload with the main purpose of reducing the disk IO and CPU usage. We can evaluate and measure the performance of Spark SQL using the TPC-DS benchmark. TPC-DS is a widely used industry standard decision support benchmark that is used to evaluate performance of data processing engines. Given that TPC-DS exercises some key data warehouse features, running TPC-DS successfully reflects the readiness of Spark in terms of addressing the need of a data warehouse application. Apache Spark v2.0 supports all the ninety-nine decision support queries that is part of this TPC-DS benchmark.




starcraft2-replay-analysis - A jupyter notebook that provides analysis for StarCraft 2 replays

  •    HTML

Read this in other languages: 한국어, 中国. In this Code Pattern we will use Jupyter notebooks to analyze StarCraft II replays and extract interesting insights.

tjbot-sports-buddy - A Node application that can be run locally or on a Raspberry Pi powered TJBot

  •    Javascript

Read this in other languages: 한국어. Watson Conversation is now Watson Assistant. Although some images in this code pattern may show the service as Watson Conversation, the steps and processes will still work.

vr-speech-sandbox-cardboard - A sample application that demonstrates how to integrate voice commands and speech recognition into a virtual reality experience

  •    CSharp

Read this in other languages: 한국어. Watson Conversation is now Watson Assistant. Although some images in this code pattern may show the service as Watson Conversation, the steps and processes will still work.

vr-speech-sandbox-rift - A sample application that demonstrates how to integrate voice commands and speech recognition into a virtual reality experience

  •    CSharp

Watson Conversation is now Watson Assistant. Although some images in this code pattern may show the service as Watson Conversation, the steps and processes will still work. In this Code Pattern we will create a Virtual Reality game based on Watson's Speech-to-Text and Watson's Assistant services.


vr-speech-sandbox-vive - A sample application that demonstrates how to integrate voice commands and speech recognition into a virtual reality experience

  •    CSharp

Read this in other languages: 한국어. Watson Conversation is now Watson Assistant. Although some images in this code pattern may show the service as Watson Conversation, the steps and processes will still work.

dsx-twitter-auto-analysis - OBSOLETE: This repository is obsolete

  •    Jupyter

The Insights for Twitter service is retired from IBM Cloud. This repository will not be updated. We will keep the repository available for folks interested in some of the technical details. Please refer to other code patterns such as https://github.com/ibm/cognitive-social-crm for a similar use case. In this developer journey we will use Jupyter notebooks to analyze Twitter data and extract interesting insights from tweets. It will easily perform complex computations on a large amount of data in a notebook by using SparkContext, which enables you to run tasks on the Spark cluster. In addition, it will integrate data from dashDB using the Spark connector and use Spark and pandas DataFrames.

elasticsearch-spark-recommender - Use Jupyter Notebooks to demonstrate how to build a Recommender with Apache Spark & Elasticsearch

  •    HTML

Recommendation engines are one of the most well known, widely used and highest value use cases for applying machine learning. Despite this, while there are many resources available for the basics of training a recommendation model, there are relatively few that explain how to actually deploy these models to create a large-scale recommender system. This Code Pattern demonstrates the key elements of creating such a system, using Apache Spark and Elasticsearch.

watson-banking-chatbot - A chatbot for banking that uses the Watson Conversation, Discovery, Natural Language Understanding and Tone Analyzer services

  •    Javascript

Read this in other languages: 中国. Watson Conversation is now Watson Assistant. Although some images in this code pattern may show the service as Watson Conversation, the steps and processes will still work.

watson-discovery-analyze-data-breaches - A Node

  •    Javascript

In this Code Pattern you will upload your own data into the Watson Discovery Service. Then you'll configure a web application so that it can query the data collection you created. The web app allows you to explore that data. Use the Deploy to IBM Cloud button OR create the services and run locally.

watson-discovery-news - A Node

  •    Javascript

In this Code Pattern, we will build a Node.js web application that will use the Watson Discovery Service to access Watson Discovery News. Watson Discovery News is a default data collection that is associated with the Watson Discovery Service. It is a dataset of primarily English language news sources that is updated continuously, with approximately 300,000 new articles and blogs added daily.

watson-discovery-news-alerting - Monitor a product's marketplace life-cycle using Watson's Discovery service to intelligently alert when a product's stance in the marketplace has changed

  •    Javascript

In this Code Pattern, we will build a Node.js web application that will use the Watson Discovery Service to access Watson Discovery News. Watson Discovery News is a default data collection that is associated with the Watson Discovery Service. It is a dataset of primarily English language news sources that is updated continuously, with approximately 300,000 new articles and blogs added daily.

watson-discovery-ui - Develop a fully featured Node.js web app built on the Watson Discovery Service

  •    Javascript

In this Code Pattern, we walk you through a working example of a web application that queries and manipulates data from the Watson Discovery Service. This web app contains multiple UI components that you can use as a starting point for developing your own Watson Discovery Service applications. For this Code Pattern, we will be using data that contains reviews of Airbnb properties located in the Austin, TX area.

watson-online-store - Code for Cognitive Developer Journey that uses Watson Conversation and Watson Discovery

  •    Python

Watson Conversation is now Watson Assistant. Although some images in this code pattern may show the service as Watson Conversation, the steps and processes will still work. In this developer Code Pattern we will create a Watson Assistant based chatbot that allows a user to: 1) find items to purchase using Watson Discovery, and 2) add and remove items from their cart by updating a Cloudant NoSQL Database.

SystemML_Usage - Demonstrate how to perform a Machine Learning exercise using Apache SystemML

  •    Jupyter

Data Science Experience is now Watson Studio. Although some images in this code pattern may show the service as Data Science Experience, the steps and processes will still work. In this Code Pattern we will use Apache SystemML running on IBM Watson Studio to perform a Machine Learning exercise. Watson Studio is an interactive, collaborative, cloud-based environment where data scientists, developers, and others interested in data science can use tools (e.g., RStudio, Jupyter Notebooks, Spark, etc.) to collaborate, share, and gather insight from their data. Apache SystemML is a flexible machine learning platform that is optimized to scale with large data sets.






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