In this series of posts, I will show how to build an image classifier using your own dataset. I’ll be using images of kangaroos and wallabies that I’ve taken, but these techniques should work well with any kind of images.
This class helps handle image data for use in machine learning. It helps to read image files from the directory and convert them into training and testing sets. It has the flexibility to return the data in any of the following forms:
Is there sarcasm on the Internet? OK, that’s an easy one. Here’s a more difficult question: can an AI be trained to detect that sarcasm? The first step to training that algorithm would be to create a corpus of sarcastic statements from the Internet. Fortunately, there’s a lot of sarcasm out there and, even more fortunately, much of it is already labeled.
What goes on inside the black box of a machine learning algorithm? While it may be impossible for a human to understand exactly why a large neural network produced the results it did, some algorithms are far more transparent. Decision trees are just such an example of a machine learning algorithm whose results can be understood by people.
The story of rabbits in Australia, and the resulting eradication efforts, provides a cautionary tale about viruses and immunity. This post will explore the growth of that population and the government’s response to it.