Last month, I delivered the one-day workshop Practical AI for the Working Software Engineer at the Artificial Intelligence Live conference in Orlando. As the title suggests, the workshop was aimed at developers, bu I didn't assume any particular programming language background. In addition to the lecture slides, the workshop was delivered as a series of Jupyter notebooks. I ran them using Azure Notebooks (which meant the participants had nothing to install and very little to set up), but you can run them in any Jupyter environment you like, as long as it has access to R and Python. You can download the notebooks and slides from this Github repository (and feedback is welcome there, too).
The workshop was divided into five sections, each with its associated Notebook:
- The AI behind Seeing AI. Use the web interfaces to Cognitive Services to learn about the AI services behind the "Seeing AI" app
- Computer Vision API with R. Use an R script to interact with the Computer Vision API and generate captions for random Wikimedia images.
- Custom Vision with R. An R function to classify an image as a "Hot Dog" or "Not Hot Dog", using the Custom Vision service.
- MNIST with scikit-learn. Use sckikit-learn to build a digit recognizer for the MNIST data using a regression model.
- MNIST with Tensorflow. Use Tensorflow (from Python) to build a digit recognizer for the MNIST data using a convolutional neural network.
The workshop was a practical version of a talk I also gave at AI Live, "Getting Started with Deep Learning", and I've embedded those slides below.
Azure Notebooks: Practical AI for the Working Software Engineer