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Train a deep learning model to classify audio embeddings on Watson Machine Learning and perform inference/evaluation with Watson Studio.
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Leverage Tensorflow and Fabric for Deep Learning to train and deploy Fashion MNIST model on Kubernetes.
Use a Jupyter Notebook to integrate the Fast Gradient Method from the Adversarial Robustness Toolbox into a model training pipeline leveraging FfDL.
Use an open source image caption generator deep learning model to filter images based on their content in a web application.
Use Node.js, Watson Assistant, and Watson Discovery services to create a virtual help desk to interact with end users for simple FAQ answer discovery.
Use Watson Knowledge Studio (WKS) to build a custom model to better categorize and structure procurement-related data for Watson Discovery to process and enrich so you can make purchasing decisions more quickly and accurately.
Use machine learning to predict a bank client's CD purchase with XGBoost, scikit-learn, and Python in IBM Watson Studio.
Create a Node.js app that takes Amazon reviews and feeds them into the Watson Natural Language Understanding service, which will determine the average review sentiment for you.
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.
Pattern demonstrates the methodology to determine target audience and run marketing campaigns using Watson Studio and Watson Campaign Automation.
Use automatic labeling to create a model from a video, then use the model to annotate a video.
Deploy and use a web-based health app on your smartphone using Watson services on IBM Cloud and IBM Watson Studio.
A simple web app shows how Watson's Natural Language Classifier (NLC) can classify ICD-10 code.
Use IBM Watson Discovery, CloudantDB, Node.js, and Alpha Vantage to create a web app to monitor sentiment, price, and news for individually listed stocks.
Build training and prediction apps with Java, Watson IoT Platform, Node-RED, and Watson Visual Recognition service.
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