Local Interpretable Model-agnostic Explanations (LIME) is an important technique for explaining the predictions of machine learning models. It is called “model-agnostic” because it can be used to explain any machine learning model, regardless of the model’s architecture or how it was trained. The key to LIME is to “zoom in” on a decision boundary and learn an interpretable model around that specific area. Then we can see exactly how various factors affect the decision boundary. In this post, I’ll show how to use LIME to explain an image classification model.
This post is a tutorial demonstrating how to use Grad-CAM (Gradient-weighted Class Activation Mapping) for interpreting the output of a neural network. Grad-CAM is a visualization technique that highlights the regions a convolutional neural network (CNN) relied upon most to make predictions. While Grad-CAM is applicable to any CNN, it is predominantly employed with image classification models. This tutorial utilizes TensorFlow for implementation, but I made a parallel tutorial that works with PyTorch.
This post is a tutorial demonstrating how to use Grad-CAM (Gradient-weighted Class Activation Mapping) for interpreting the output of a neural network. Grad-CAM is a visualization technique that highlights the regions a convolutional neural network (CNN) relied upon most to make predictions. While Grad-CAM is applicable to any CNN, it is predominantly employed with image classification models. This tutorial utilizes PyTorch for implementation, but I made a parallel tutorial that works with TensorFlow.
This post shows how to load and evaluate the model we built in the previous post.
This is post is a walkthrough of creating a Siamese network with FastAI. I had planned to simply use the tutorial from FastAI, but I had to change so much to be able to load the model and make it all work with the latest versions that I figured I would turn it into a blog post. This is really similar to my other post on Siamese Networks with FastAI, except that in this one I will follow on with a post about how to evaluate the model.
Distributions are super important. In this post, I’ll talk about some common distributions, how to plot them, and what they can be used for.
This post contains instructions for how to work with personal access tokens on GitHub.
