This is a review of the CausalML package, a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research.
In this article, we explored a broad overview of epistemic uncertainty in deep learning classifiers, and develop intuition about how an ensemble of models can be used to detect its presence for a particular image instance.
In this article, we examine typical computer vision analysis techniques in comparison with the modern CLIP (Contrastive Language-Image Pre-Training) model.
We compare the performance of using different techniques for handling Missing At Random datasets in building predictive models. We also examine how these techniques affect the predictive performance of machine learning models.
When it comes to image synthesis algorithms, we need a method to quantify the differences between generated images and real images in a way that corresponds with human judgment. In this article, we highlight some of these metrics that are commonly used in the field today.
In this article, we saw how to use various tools to maximize GPU utilization by finding the right batch size for model training in Gradient Notebooks.
In this article, we examine the game theory based approach to explaining outputs of machine learning models: Shapely Additive exPlanations or SHAP. We then demo the technology using sample images in a Gradient Notebook.
In this article, we talk about what Dense Passage Retrieval is, how it works, and its uses. We also show how to implement it using the Simple Transformers python library in a Gradient Notebook.
In this tutorial, we show how to apply model interpretability algorithms from Captum on simple models. We demo building a basic model and use attribution algorithms such as Integrated Gradients, Saliency, DeepLift, and NoiseTunnel to attribute the image's label to the input pixels and visualize it.
