Cognitive code patterns for developers

Get your hands dirty by checking out these real world examples and tutorials that highlight Watson Machine Learning and PowerAI Vision in action

Validate computer vision deep learning models

This developer code pattern provides a Jupyter Notebook that will take test images with known “ground-truth” categories and evaluate the inference results vs. the truth.

When you have completed this code pattern, you will understand how to:

  • Train and deploy an image classification model with PowerAI Vision
  • Run a Jupyter Notebook
  • Evaluate the results using a variety of accuracy statistics
  • Validate existing deployed models with new test data sets

Object tracking in video with OpenCV and Deep Learning

This Code Pattern demonstrates how to create a video car counter using PowerAI Vision Video Data Platform, OpenCV and a Jupyter Notebook. You’ll use a little manual labeling and a lot of automatic labeling to train an object classifier to recognize cars on a highway. You’ll load another car video into a Jupyter Notebook where you’ll process the individual frames and annotate the video.

Integrate inference on mobile phones

This tutorial will show you how to build and deploy a PowerAI Vision model and use it in an iOS app. You’ll learn how to:

  1. Create a data set
  2. Train a model for image classification
  3. Deploy it to a Web API
  4. Integrate the API into an iOS App

Locate and count items with object detection

Imagine that you’re a supplier of an item (such as Coca-Cola) and you want to know how many bottles are on a store’s shelf. You can build an app that helps you do just that. PowerAI Vision uses deep learning to create trained models based on images that you upload and label. You don’t need to write any code to train, deploy, and test a new object detection model. You simply upload the images, use your mouse to label the objects in your images, and then let PowerAI Vision do the learning.

Accelerate training of machine learning algorithms

Quickly train a machine learning algorithm using PowerAI through Nimbix Cloud. Start with a Jupyter notebook and use machine learning to investigate the predictability of future financial market shifts in the “renewable energy” sector by examining related markets and sentiment detected in The New York Times articles.

Leverage POWER8 with NVLink to dramatically decrease your training time vs. non-POWER architectures.

Image recognition training with PowerAI notebooks

Use transfer learning to create your own image classifier. In this pattern, you’ll use a model designed to recognize houses to become a classifier of houses with swimming pools.

  • Load and run a Jupyter Notebook with PowerAI on Nimbix Cloud
  • Use transfer learning to leverage the TensorFlow Inception model to create a custom classifier from a set of images
  • Test and demonstrate the resulting classifier

Build an image classifier to help search for extraterrestrial life

In this developer pattern, you’ll convert radio signal data into images so you can treat it like an image classification problem. Then you’ll train an image classifier using TensorFlow with a convolutional neural network. You’ll use Jupyter Notebooks on PowerAI to demonstrate model training and testing.

When you’ve completed this pattern, you will understand how to:

  • Convert signal data into image data
  • Build and train a convolutional neural network
  • Display and share results in Jupyter Notebooks

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