Displaying 1 to 15 from 15 results

nodejs-in-notebooks - Run Node.js code in Python notebooks

  •    Jupyter

Notebooks are where data scientists process, analyse, and visualise data in an iterative, collaborative environment. They typically run environments for languages like Python, R, and Scala. For years, data science notebooks have served academics and research scientists as a scratchpad for writing code, refining algorithms, and sharing and proving their work. Today, it's a workflow that lends itself well to web developers experimenting with data sets in Node.js. To that end, pixiedust_node is an add-on for Jupyter notebooks that allows Node.js/JavaScript to run inside notebook cells. To learn more follow the setup steps and explore the getting started notebook or click on the sample image below to preview the output.

rainbow - Use Watson Visual Recognition and Core ML to create a Kitura-based iOS game that has a user search for a predetermined list of objects

  •    Swift

This code pattern is an iOS timed game that has users find items based on a list of objects developed for Apple phones. It is built to showcase visual recognition with Core ML in a fun way. This project repository consists of an iOS app and a backend server. Both components are written in the Swift programming language and leverages the Kitura framework for the server side. Cloudant is also used to persist user records and best times, and Push Notifications are used to let a user know when they have been removed from the top of the leaderboard. Our application has been published to the App Store under the name WatsonML, and we encourage folks to give it a try. It comes with a built-in model for identifying six objects; shirts, jeans, apples, plants, notebooks, and lastly a plush bee. Our app could not have been built if not for fantastic pre-existing content from other IBMers. We use David Okun's Lumina project, and Anton McConville's Avatar generator microservice, see the references below for more information.

pytorch-on-watson-studio - Use Watson Studio and PyTorch to create a machine learning model to recognize hand-written digits

  •    Jupyter

Recognizing handwritten numbers is a piece of cake for humans, but it's a non-trivial task for machines. Nowadays, with the advancement of machine learning, people have made machines more and more capable of performing this task. We now have mobile banking apps that can scan checks in seconds and accounting software that can extract dollar amounts from thousands of contracts in minutes. If you are interested in knowing how this all works, please follow along with this code pattern as we take you through the steps to create a simple handwritten digit recognizer in Watson Studio with PyTorch. PyTorch is a relatively new deep learning framework. Yet, it has begun to gain adoption especially among researchers and data scientists. The strength of PyTorch is its support of dynamic computational graph while most deep learning frameworks are based on static computational graph. In addition, its strong NumPy like GPU accelerated tensor computation has allowed Python developers to easily learn and build deep learning networks for GPUs and CPUs alike.

customer-churn-prediction - Predict which telecom customers might leave using IBM Watson Machine Learning

  •    HTML

In this Code Pattern, we use IBM Watson Studio to go through the whole data science pipeline to solve a business problem and predict customer churn using a Telco customer churn dataset. 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 as well as build and deploy machine learning and deep learning models.

data-pre-processing-with-pywren - Use PyWren over IBM Cloud Functions to perform face recognition deep learning

  •    Jupyter

Let’s say you write a function in Python to process and analyze some data. You successfully test the function using a small amount of data and now you want to run the function as a serverless action at massive scale, with parallelism, against terabytes of data. What options do you have? Obviously, you don’t want to learn cloud IT tricks and setup VMs, for example. Nor do you necessarily want to become a serverless computing expert in scaling data inputs, processing outputs, and monitoring concurrent executions.

watson-stock-market-predictor - A IBM Developer code pattern for Watson Studio: forecast the stock market with Python Notebooks, SPSS Modeler, Data Refinery, and other Watson Studio tools

  •    Jupyter

In this code pattern, we will demonstrate on how subject matter experts and data scientists can leverage IBM Watson Studio to automate data mining and the training of time series forecasters using open-source machine learning libraries, or the built-in graphical tool integrated into Watson Studio. It applies ARIMA algorithms (Auto-regressive Integrated Moving Average) and other advanced techniques to construct mathematical models capable of predicting trends based on data from the past. Using the IBM Watson Studio and other popular open-source Python libraries for data science, this code pattern provides an example of data science workflow which attempts to predict the end-of-day value of S&P 500 stocks based on historical data. It includes the data mining process, that uses the Quandl API – a marketplace for financial, economic and alternative data delivered in modern formats for today's analysts.

employee-attrition-aif360 - Walkthrough the data science life cycle with different tools, techniques, and algorithms

  •    Jupyter

This code pattern is a high-level overview of what to expect in a data science pipeline and the tools that can be used along the way. It starts from framing the business question, to buiding and deploying a data model. The pipeline is demonstrated through the employee attrition problem. The dataset used in the code pattern is supplied by Kaggle and contains HR analytics data of employees that stay and leave. The types of data include metrics such as education level, job satisfactions, and commmute distance.

programming-language-classifier - Classify programming languages with Watson Studio and Natural Language Classifier

  •    Jupyter

In this Code Pattern, we will use Jupyter Notebooks in IBM Watson Studio to build a model that predicts a code's programming language based on its text. The model will then be evaluated using IBM's Watson Natural Language classifier. Sign up for IBM's Watson Studio. By creating a project in Watson Studio a free tier Object Storage service will be created in your IBM Cloud account. Take note of your service names as you will need to select them in the following steps.

functions - TBD

  •    Python

A companion package to IBM Watson IoT Platform Analytics containing sample functions and base classes from which to derive custom functions. These instructions will get you up and running in your local environment or in Watson Studio for development and testing purposes.

db2-event-store-iot-analytics - IoT sensor temperature analysis and prediction with IBM Db2 Event Store

  •    Jupyter

This code pattern demonstrates the use of Jupyter notebooks to interact with IBM Db2 Event Store -- from the creation of database objects to advanced analytics and machine learning model development and deployment. The sample data used in this code pattern simulates data collected by real industry IoT sensors. The IoT sample data includes sensor temperature, ambient temperature, power consumption, and timestamp for a group of sensors identified with unique sensor IDs and device IDs. A simple IBM Streams flow is used to stream the sample data from a CSV file to an Event Store table.

augment-visual-recognition-detection-of-low-resolution-human-faces - This code pattern uses Watson Visual Recognition, Watson Studio, and a Python notebook to demonstrate a way to detect covered faces

  •    Jupyter

In this code pattern, we will demonstrate a methodology to extend Watson Visual Recognition face detection by providing a strategy that will detect the border cases such as, blur and covered faces, with Tensorflow Object Detection, compiled in Watson Studio. NOTE: The contents of the object_detection folder have been obtained from the Tensorflow Object Detection API repo. Since the entire repo was not needed and only a few folders were required.

generate-insights-from-data-formats-with-watson - How do we process data in different formats like docx, pdf etc and generate insights to be linked with structured data in database?This pattern helps in establishing relations between structured & unstructured data to generate recommendations using Watson NLU & Watson Studio

  •    Jupyter

In this code pattern, we will demonstrate a methodology to integrate structured data & unstructured data to generate recommendations. Processing unstructured data coming in different data formats has many challenges with respect to data extract & derive meaning to help us take informed decisions, however the related data would be in the structured format. It would be time consuming process to check different data sources manually for inference and that is where this pattern will be handy. We will showcase a configurable yet scalable process which will help in merging the different data sources and expedite the process of decision making. We have taken the example of HR recruitment process where we use the candidate's resume to be compared with job description & candidate database to identify the best suited candidate for a given job profile. This will help the HR to develop an efficient recruitment plan. Our motto is to select the right candidate which helps in risk mitigation for the organization thereby enhancing the ROI and increases the credibility for the recruitment process. We will be using Watson Studio & Watson NLU to solve this use-case. The intended audience for this code pattern is developers who want to learn a new method for scanning the text across different document format and establish a relation with the data stored in the structured format in a database. The distinguishing factor of this code pattern is that it allows a configurable mechanism of search optimization which allows the recruiter to select the best fit candidate for the role.

run-campaigns-target-customers - Integrate Watson Studio and Watson Campaign Automation to tailor your target audience for effective campaigns

  •    Jupyter

A business runs marketing campaigns to promote products with the objective of boosting revenues. The campaigns need to be run on appropriate audiences for maximum impact. A consumer not interested in a product will ignore the campaign offer. Identifying the target audience - The target audience can be determined by analyzing the purchases and browsing history of customers, social media posts, reviews and other data sources. This will help identify customers who could be interested in a product.

continuous-learning-with-watson-ml-and-db2 - Build models that learn over time with Watson Machine Learning, Watson Studio and IBM Db2 Warehouse on Cloud

  •    TSQL

This repository will not be updated. The repository will be kept available in read-only mode. In this code pattern, we will use IBM Watson Machine Learning and Watson Studio — which allows data scientists and analysts to quickly build and prototype models — to monitor deployments, and to learn over time as more data becomes available. Performance Monitoring and Continuous Learning enables machine learning models to re-train on new data supplied by the user or other data sources. All applications and analysis tools that depend on the model are automatically updated as Watson Studio handles the selection and deployment of the best model.

analyze-insights-on-startup-using-watson-studio - This code pattern will demonstrate a methodology to show how we can get analytical insights and visualisations from raw data on the Web

  •    Jupyter

The World Wide Web or the "Web" is the universe of network-accessible information. All this information present in a raw format on the Web. What if you want a way to ingest raw information on the web for any given topic and provide insights and visualiations for the same. This code pattern does, just that taking an example of performing analytics on Startups. Being in the age of start-ups. There is a rapid increase in a number of companies providing skilled services. We can scrape information about such companies and evaluate their success stories based on the number of articles or live use cases appeared in news portals.

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