Ready to drive positive change across the world with your code?
The 2018 Call for Code Global Challenge asks developers to create solutions that significantly improve preparedness for natural disasters and relief when they hit. The winning application will be adopted as an open source project (through the Linux Foundation) to be scaled and accelerated to deployment in the areas of greatest need.
Leverage Tensorflow and Fabric for Deep Learning to train and deploy Fashion MNIST model on Kubernetes
Learn how to train your Fashion MNIST model with IBM Fabric for Deep Learning (FfDL) on Kubernetes Cluster configured with GPU.
Use a Jupyter notebook to integrate the Adversarial Robustness Toolbox into a neural network model training pipeline to find model vulnerabilities
This code pattern explains how to use a Jupyter Notebook to integrate the Fast Gradient Method (FGM) from the Adversarial Robustness Toolbox (ART) into a model training pipeline leveraging Fabric for Deep Learning (FfDL).
Classify audio embeddings on Watson Machine Learning by training a deep learning model
This developer code pattern will guide you through training a deep learning model to classify audio embeddings on IBM's Deep Learning as a Service (DLaaS) platform - Watson Machine Learning - and performing inference/evaluation on IBM Watson Studio.
Create a custom classifier with Watson Visual Recognition to identify images of world cities taken from the ISS
This code pattern combines city images and Watson Visual Recognition to show you how to create a custom classifier that will identify various cities based on their images at night.
Leverage Watson Natural Language Classifier to tag medical classification codes
This app was built to demonstrate IBM's Watson Natural Language Classifier. It uses the Watson Python SDK to create the classifier, list classifiers, and classify the input text. We also make use of the freely available ICD-10 API, which, given an ICD-10 code, returns a name and description.
Build a custom language model for Watson Speech to Text
In this tutorial, learn how to build a custom language model for Watson Speech to Text for a specific domain.
Classify images offline with Watson Visual Recognition and Core ML
This code pattern shows you how to create a Core ML model using Watson Visual Recognition, which is then deployed into an iOS application.
Developing an image classifier using Watson Visual Recognition on Watson Studio
So, Learn how to in less than 45 minutes, build a fully functioning image classifier that can recognize any (yes, any) object with insanely high probabilities.
Use NASA data with Watson Studio and Machine Learning to predict the intensity of wildfires
Mitigating natural disasters is one of the world's greatest challenges, and some of the most devastating natural disasters are wildfires. To help us understand wildfires, NASA provides satellite data that measures the fire's intensity, using the brightness of the fires as a proxy. In this code pattern, we'll use Watson Studio and Watson Machine Learning to train a model with this data, allowing us to make wildfire intensity predictions using the location on a map.